This PDF file contains the front matter associated with SPIE Proceedings Volume 13101, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The Canada-France-Hawai‘i Telescope, operational since 1979, currently has five scientific instruments ranging from a few years old to decades old, making it highly productive today. At this world-class facility, computing systems were built and software was developed to support some of the first and largest mosaic CCD cameras, control the telescope, transition from classical observing to queue scheduled observing, and to allow it to be remotely controlled. This involved many choices of computing platforms, programming languages, and significant open-source software development. Software tools and computing infrastructure have been continually adapted, purchased, made in house, and maintained. These “life cycles” are not easy to predict at their start. A retrospective analysis of how these have played out for over 40 years can inform future projects at CFHT and in astronomy in general. We detail the major decision points and speculate how outcomes would have been different had we taken alternative paths. We discuss a rationale for making software choices in future projects.

The SKA Observatory is an international organization whose mandate is to build and operate two multi-purpose radio telescope arrays. The SKA Low Frequency Telescope array, located in the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory in Western Australia, with the observing range 50 - 350 MHz, will consist of 131,072 log-periodic antennas organized as 512 stations; the maximum distance between two stations is 65 kilometers. The SKA Mid Frequency Telescope array, located in the Karoo region, South Africa, with the observing range 350 MHz - 15 GHz, will comprise 197 offset-Gregorian dishes. The SKA Mid Telescope dishes are 15 meters in diameter, and the maximum distance between two dishes is 150 kilometers. The SKA Global Headquarters is in the Jodrell Bank Observatory, near Manchester, UK. The construction of the SKA Telescopes is on the way, including the development of the Telescope Control System. The SKA Observatory, and each of the telescopes, will be delivered in stages, thus supporting incremental development of the collecting area, signal and data processing capacity, and the observing and processing modes. Much of the Control System functionality will be required early in construction to support integration and verification of other systems. This paper provides an overview of the SKA Telescope Control System, including the design patterns and technology choices, summarizes what has been achieved so far, and provides reflections on lessons learned so far.

The European Solar Telescope (EST) aims to become the most ambitious ground-based solar telescope in Europe. This paper summaries the planned architecture, software practices adopted at the moment for the development environment and future lines. EST has adopted a mix of proven software from existing telescopes that are suited to the telescope requirements with new development systems, CI/CD practices and agile methodologies among others.

The Extremely Large Telescope (ELT) is a 39 meters optical telescope under construction in the Chilean Atacama Desert. The control software is under advanced development and the system is slowly taking shape for first light in 2028. ESO is directly responsible for coordination functions and control strategies requiring astronomical domain knowledge. Industrial contractors are instead developing the low-level control of individual subsystems. We are now implementing the coordination recipes and integrating the local control systems being delivered by contractors. System tests are performed in the ELT Control Model in Garching, while waiting for the availability of individual subsystems at the telescope. This paper describes the status of development for individual subsystems, of the high-level coordination software and of the system integration on the ELT Control Model (ECM), focusing on testing and integration challenges.

The Caltech Submillimeter Observatory (CSO) telescope in Mauna Kea, Hawaii, USA, has been disassembled and will be transported to Chajnantor Plateau, Chile by the end of 2024 and renamed as Leighton Chajnantor Telescope (LCT). LCT’s scientific objectives include multiwavelength imaging and spectroscopic survey of the galactic plane, magnetic fields in star formation via dust polarimetry, etc. To achieve these objectives, the instrumentation and control systems of LCT need to be upgraded such that its technological criteria can be improved significantly (e.g., 12 μm surface accuracy, 1 arcsec pointing accuracy, 10×17 W/Hz^1/2 detecting sensitivity). This necessitates a comprehensive reconstruction of the computer, software, and data systems of LCT. In this work, we propose a four-phase reconstruction scheme. The first phase is dedicated to the design of the observation data management system, software system, and the computer and network system. The second phase focuses on infrastructure reconstruction which involves evaluating the computer environment, unifying the operating system across all the computers, and updating and developing the user interface program. The third phase focuses on application systems reconstruction to achieve higher accuracy, faster rotating speed and better capability of disturbance rejection. The final phase includes experiment and implementation of the developed systems on LCT to discover and correct the deficiencies. This work will provide a fundamental framework for upgrading LCT’s control systems, thereby enhancing its scientific capabilities.

The Dragonfly Spectral Line Mapper (DSLM) is a semi-autonomous, distributed-aperture based telescope design, featuring a modular setup of 120 Canon telephoto lenses, and equal numbers of ultra-narrowband filters, detectors, and other peripherals. Here we introduce the observatory software stack for this highly-distributed system. Its core is the Dragonfly Communication Protocol (DCP), a pure-Python hardware communication framework for standardized hardware interaction. On top of this are 120 REST-ful FastAPI web servers, hosted on Raspberry Pis attached to each unit, orchestrating command translation to the hardware and providing diagnostic feedback to a central control system running the global instrument control software. We discuss key features of this software suite, including docker containerization for environment management, class composition as a flexible framework for array commands, and a state machine algorithm which controls the telescope during autonomous observations.

The US Naval Observatory (USNO) operates a wide range of telescopes, from historic meter-class telescopes through modern portable commercial-off-the-shelf (COTS) systems. As the number and variety of systems increases, maintaining separate control software for each telescope becomes impractical. We have implemented Telescope Control Software (TCS) in Python that allows us to quickly and safely unify interactions with, and outputs from, a large variety of telescopes. We will discuss the design and operations of this TCS, including examples of how the flexible nature enables similar control for vastly different system architectures and observational goals.

The Atacama Pathfinder EXperiment (APEX) operates a 12m telescope in Chile at 5107m above sea level in the Andes mountains. Given this isolated location, APEX was developed as a telescope for remote operations, ensuring that it could be effectively managed despite the challenging environment. To guarantee optimal operation in terms of science and engineering, several developments were deployed related to monitoring and network systems. In this paper, we will describe the key properties that have enabled APEX to successfully operate in remote mode over the last seven years. Our discussion will detail the significant experiences gained during this period. We will describe the reliable network system setup implemented in APEX, the monitoring system that has been developed for safety and remote operation, which incorporates a redundant database in a master/slave topology at both control and high sites. Additionally, we will explain the design of the control room infrastructure that allows for continuous monitoring, alerting operators to failures through trend analysis for prompt response. We will provide a perspective how the successful implementation, lessons learned and expertise gathered through APEX as remote telescope could help as a pathfinder for the future of remote operations at the VLT and ELT, in the framework of the integrated Operation (IOP) Programme whose goal is to integrate VLT and ELT to a single observatory. Effective remote management under the IOP framework promises not only enhanced efficiency and safety but also a cohesive operational synergy. To this end we will provide a comparison of the remote-controlled systems at VLT and APEX, evaluating their strengths and weaknesses, which can provide guidance for shaping the future remote operations of the ELT and VLT.

The SKA Observatory, currently in the construction phase, will have two of the world’s largest radio telescopes when completed in 2028. The scale of the project introduces unique challenges for the telescope software design and implementation at all levels, from user interfacing software down to the lower-level control of individual telescope elements. The Observation Execution Tool (OET) is part of the Observation Science Operations (OSO) suite of applications and is responsible for orchestrating the highest level of telescope control through the execution of telescope control scripts. One of the main challenges for the OET is creating a design that can robustly run concurrent observations on multiple subarrays while remaining responsive to the user. The Scaled Agile Framework (SAFe) development process followed by the SKA project also means the software should be allow to iterative implementation and easily accommodate new and changing requirements. This paper concentrates on the design decisions and challenges in the development of the OET, how we have solved some of the specific technical problems and details on how we remain flexible for future requirements.

The “Gran Telescopio de Canarias” (GTC) is an optical-infrared 10-meter segmented mirror telescope at the ORM observatory in Canary Islands (Spain). In 2009 it started its scientific operational phase. The GTC Control System (GCS) is a distributed object and component-oriented system. In the first developments, the different motion control and sensor systems were quite coupled to the technologies that were used for their implementation: PMAC controller, CANOpen protocol, Modbus protocol, specific drives such as Kollmorgen Servostar S700 or Technosoft IDM680, ESD CAN library, etc. This work has been the result of the evolution of these developments carried out in the field rotation, acquisition and guiding systems of the focal stations updated in recent years. All the knowledge acquired in this context has been reflected in a new design, generalizing concepts such as mechanism, axis, actuator, bus protocol and access library, in such a way that the components of the coordination package are completely decoupled from the technology that is used for the effective control of movement. On the one hand, this design provides simplicity, since the creation of new devices is reduced to specifying a configuration. And on the other hand, it provides stability to the system, focusing the points of variability on the simple extension of new technologies that will be incorporated into the system, such as Ethercat. One of the objectives has been to reduce development costs, going from an effort of more than a year to just a few weeks, in this way we accelerate the transfer to operation of the functionalities that we continue to incorporate into the telescope and export this solution to any other telescope. Examples of this are the future New Robotic Telescope (NRT) and the European Solar Telescope (EST), which will benefit from this new framework and the rest of our system, since they will use the GCS in their developments. Another objective has been to increase the quality of the system, thanks to the fact that this framework constitutes a common knowledge base, allowing improvements to be cumulative and avoiding divergences that prevent us from making future changes with little effort.

METIS, the Mid-infrared ELT Imager and Spectrograph, will be one of the first-generation ELT instruments. It has an Instrument Control System (ICS) that allows the instrument to operate in various observing modes, using a multitude of mostly cryogenic mechanisms such as filter wheels, linear stages (e.g. to move mirrors and masks), a derotator, piezo mechanisms for high-speed or high-accuracy displacements (e.g. for pupil stabilization, for adaptive optics field selection and modulation, ...), a chopper, and so on. Thermal and vacuum control of the cryostat on the other hand is handled by a dedicated PLC-based system and is not described in this paper. The ICS is built using the ESO ELT instrument framework, which provides the basic building blocks to control the mechanisms, along with the interface to the telescope and its services, from the low-level pointing and tracking system, the real-time Single Conjugate Adaptive Optics (SCAO) system, to the high-level sequencer-based observing system. In this paper we provide an overview of the ICS electronics, the low-level software running on a Beckhoff PLC, and finally the high-level software running on a Linux workstation. As a detailed description of the entire system is out of scope of the paper, we focus instead on the general design, implementation and testing principles. We show how a fast real-time network (EtherCAT) and off-the-shelf industrial I/O, together with the services provided by the ELT instrument framework, can meet the requirements of ELT instruments, and how they can offer an elegant solution to technically demanding problems such as the high-speed synchronization between a chopper and a detector controller. Finally, we demonstrate how a modular electronics design, a flexible software architecture, and a strong focus on simulation can alleviate some of the organizational challenges of building, integrating and testing an ICS of a complex instrument, which subsystems are developed by institutes in different locations.

The VLT at Paranal Observatory has been in operation for over two decades, and soon, the ELT will be managed by the same operational team. Maintaining operational efficiency and minimizing downtime with limited resources will be crucial. Previous research has shown that software logs effectively capture the telescopes' behavior, providing valuable operational insights. We've integrated various log analysis techniques from academic literature and industry best practices. These techniques allow engineers to monitor system health, analyze error sequences, detect anomalies, and reconstruct processes which improve maintenance and extract new insights. Additionally, we've utilized generative artificial intelligence and NLP transformer-based models, to infer observation behavior and predict execution failures. We have taken advantage of both the Paranal Datalab on-premises facility and Azure Cloud. In this work, we provide technical details and outline the key challenges and opportunities in adopting this technique within an astronomy facility.

The Square Kilometre Array (SKA) is a groundbreaking radio telescope project, aiming at constructing the two biggest radio telescopes in Australia and South Africa. They will have a larger collecting area and sky resolution than existing radiotelescopes, and they will handle an unprecedented amount of data flowing between computing facilities. The functionality of these telescopes heavily depends on the quality of the operating software. The project’s magnitude and complexity require effective testing processes capable of preemptively identifying and addressing potential bugs and errors. In this context, a simple regression testing strategy is not enough. In the first years of SKA construction, we noticed that tests, which typically pass, may occasionally experience failures. Collecting and analyzing test results over extended time periods could help in understanding the origin of such failures and to find solutions that address them. It would be a significant step forward to improve the reliability of SKA software. Data mining is a process of discovering patterns, trends, correlations, or useful information from large sets of data. It can be applied to a large set of test results concerning the operations of a specific SKA software component, i.e. the Local Monitoring and Control of Central Signal Processor (CSP.LMC). The CSP.LMC is tested with a multilevel strategy, spawning from unit to system tests, that can be performed on different environments. In this paper we analyze the strengths of this approach, describe some of the pitfalls in implementing it, and discuss the possibility to apply it to different SKA Software components.

The upcoming NASA Pandora Mission, scheduled for launch in 2025, will obtain exoplanet transmission spectra and stellar activity information to better characterize and correct for the spectral contamination of transmission spectra by the host star. Pandora will obtain at least ten wavelength-resolved transits each of 20 unique exoplanets, each with 24 hours of stellar baseline per transit. This will provide the vital context needed to disentangle stellar contamination from exoplanet transmission spectroscopy around cool stars, and understand the impact of star spots on retrieved atmospheric properties. Pandora will be equipped with i) a visible detector, providing time-series photometry at 550nm, and ii) a near-infrared detector, providing R=30 spectra from 0.9 to 1.6 microns with at least 150ppm precision at J=9. We have developed an open-source simulator of Pandora data to assist in the development of a) the Pandora concept of operations b) the Pandora Science Pipeline and c) science analysis software to retrieve transmission spectra from Pandora data. In particular, we describe how we use the scipy.sparse Python submodule to create memory efficient simulations. This software is both fast and efficient, to enable various operating scenarios to be simulated. Our simulator tool (v.1.0) is available as open-source software, and much of the infrastructure can be generalized to other missions with similar specifications or detectors to Pandora.

The New Robotic Telescope (NRT) is a fully autonomous robotic four-meter class telescope located at the Roque de los Muchachos Observatory (ORM) on La Palma, Canary Islands, Spain. The autonomous nature requires a robust, fault tolerant, real time Control System. This is achieved by using proven industrial Beckhoff PLCs and an Ethercat data bus for real time operation. The Ethercat data bus is used to link all pieces of PLC hardware together, this drastically cuts down on the number of control cables going through rotators and cable wraps and increases reliability with a ring topology giving cable redundancy. The PLC code is developed using a Unit testing framework which lowers the risk of breaking expensive hardware during code changes and allows extra functionality to be added easily. This is being implemented to allow new hardware to be added easily and old hardware can later be swapped out for newer models, lowering maintenance costs. The PLCs are controlled by a Kubernetes Cluster using the OPC-UA protocol. The telescope functional safety will be tightly integrated with Beckhoff Twinsafe allowing complete telemetry all the way up the software stack.

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation atmospheric Cherenkov gammaray Observatory. CTAO will be constructed on two sites, one array in the Northern and the other in the Southern hemisphere, containing telescopes of three different sizes, for covering different energy domains. To combine and orchestrate the different telescopes and auxiliary instruments (array elements), the Array Control and Data Acquisition (ACADA) system is the central element for the Observatory on-site operations: it controls, supervises, and handles the data generated by the array elements. Considering the criticality of the ACADA system for future Observatory operations, corresponding quality assurance provisions have been made at the different steps of the software development lifecycle, with focus on continuous integration and testing at all levels. To enable higher-level tests of the software deployed on a distributed system, an ACADA test cluster has been set up to facilitate testing and debugging of issues in a more realistic environment. Furthermore, a separate software integration and test cluster has also been established that allows for the off-site testing of the integrated software packages of ACADA and of the corresponding array elements. Here the software integration can be prepared, interfaces and interactions can be tested, and on-site procedures that are required later in the process can be checked beforehand, only limited by the simulation capabilities that are delivered as part of the software packages. Once preparations and testing with the off-site test cluster are completed, the integrated software can be deployed at the target site. The software packages and setup parameters are kept under configuration control at all stages, and deployment steps are documented to ensure that installations are reproducible. This methodology has been applied for the first time in the context of the integration of ACADA with the first CTAO Large-sized Telescope (LST-1) in October 2023.

The European Southern Observatory (ESO) has made a considerable progress in the implementation of a new software framework, the Instrument Control System Framework (IFW) tailored to facilitate the development of upcoming astronomical instruments at the Extremely Large Telescope (ELT). This framework offers a complete, scalable, and adaptable infrastructure to support the diverse needs of instrument control. The framework’s architecture is strongly based on ESO's extensive experience in operating and maintaining VLT instruments while integrating the technological innovations specified by the ELT project. It presents a unified approach to instrument control, fostering the coordination of various heterogenous instruments subsystems and tasks, ranging from the control of instrument hardware functions and data visualization to the execution of science observations and instrument calibrations. The framework is primarily targeted to instrument developers from ESO partner institutes who are currently working on the first-generation ELT instruments. ESO extended the framework's application in 2019 to all new instruments within its optical telescopes. This strategy aims to reduce maintenance costs and promote ELT-VLT integrated operations, embracing future VLT instruments. The framework is being elaborated following the ELT Development Process, a sort of scrum like process supported by the tools Jenkins, Gitlab and Jira. This paper provides an overview of the design principles, key features, as well as details of the development process and main technologies employed in its construction.

Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS) 6U Astronomical Nanosatellite. Designed for the observation of Extragalactic Background Light (EBL), this mission is expected to generate a substantial amount of image data, particularly within the confines of CubeSat capabilities. This paper introduces the VERTECS Camera Control Board (CCB), an open-source payload interface board leveraging Commercial Off-The-Shelf (COTS) components, with a Raspberry Pi Compute Module four at its core. The VERTECS CCB hardware and software have been designed from the ground up to serve as the sole interface between the VERTECS bus system and astronomical imaging payload, while providing compute capability not usually seen in nanosatellites of this class. Responsible for mission data processing, it will facilitate high-speed data transfer from the imaging payload via gigabit Ethernet, while also providing a high-bitrate serial connection to the payload x-band transmitter for mission data downlink. Additional interfaces for secondary payloads are provided via USB-C and standard 15-pin camera connectors. The Raspberry Pi embedded within the VERTECS CCB operates on a standard Linux distribution, streamlining the software development process. Beyond addressing the current mission’s payload control and data handling requirements, the CCB sets the stage for future missions with heightened data demands. Furthermore, it supports the adoption of machine learning and other compute-intensive applications in orbit. This paper delves into the development of the VERTECS CCB, offering insights into the design and validation of this next-generation payload interface, to ensure that it can survive the rigors of space flight.

The Camera Control System (CCS) of the Wide Field Survey Telescope (WFST) serves as the core imaging module and is a complex distributed system composed of multiple devices. Building upon the Remote Autonomous Control System 2nd (RACS2), in this paper the RACS2-CCS framework was proposed and characterized mainly by its event-driven nature. The design incorporates basic control functions, a component manager mechanism, a file management mechanism, and a site interface component mechanism. The RACS2-CCS system can efficiently organize complex control processes, monitor system status, manage data files, and facilitate interactions between systems (such as the Observatory Control System (OCS) and the Telescope Control System (TCS)). This system is practically applied within WFST CCS.

The 4-meter Multi-Object Spectroscopic Telescope (4MOST) instrument uses 2436 individually positioned optical fibres to couple the light of targets into its spectrographs. The Fibre Target Alignment (FTA) software controls all aspects of the 4MOST instrument that are involved to position the 2448 spines of the AESOP positioner to their target locations closer than 10µm RMS, within 90 seconds. The AESOP fibre positioner provides a HTML interface which is used by the FTA software to command spine movements. The metrology system consists of four cameras, and a sophisticated software package to measure the location of fibres, which are moved by the AESOP spines. Spines reach their target typically after six to eight iterative movements, which are interlaced with metrology frames. The metrology software is capable of taking 4 images simultaneously, and reconstructing fibre positions to within 3μm RMS within five seconds. We present the FTA control software architecture, the interaction of sub-components and the different operation modes of the system. Especially the concurrent and simultaneous control of four metrology camera processes. Due to the complexity of the system, comprehensive debugging and visualization tools have been developed which allow a detailed understanding and interaction of the entire system. The graphical tool provides feedback for each individual camera stream and their combined result. It provides statistics and tools to manipulate individual spines, especially to recover them in case of entanglement. To develop the control software, a full end-to-end simulator has been created, which closes the loop between metrology image simulation, simulated fibre positioning and all control aspects in between. The metrology system uses the current spine position as presented by the AESOP positioner to render metrology camera images. Analysis and downstream computation is identical to the live software. When commanded to move spines, The AESOP simulator executes the identical steps to move spines, except sending electrical signals. After which it returns the expected spine positions after their move, which is taken as input for the next FTA iteration.

In this paper, we present an overview of the software architecture for the ArmazoNes high Dispersion Echelle Spectrograph (ANDES) spectrograph, which has been developed as part of the recent System Architecture Review (SAR) held in October 2023. Our focus in this paper is twofold: we will detail about the control software and science tools that are set to be implemented. In particular, we provide a detailed view on how the ELT Instrument Control Framework has been effectively deployed to manage the complexities of a distributed instrument like ANDES. This entails a comprehensive discussion of the key architectural decisions we have made to meet the requirements of the project. Furthermore, we offer insights into the suite of science software that will be an integral part of the ANDES instrument. This includes the Exposure Time Calculator, Observation Preparation tools, and the Data Reduction Library. Finally, we provide an overview of the Data Analysis Software and the End-to-End ANDES simulator. These tools are crucial for processing and analyzing the data collected by the ANDES spectrograph.

This study aims to compare the effectiveness of deep learning methods, specifically Feedforward Neural Networks (FNN), with traditional Pointing Models (PMs) for compensating Blind Pointing Errors in astronomical instruments. Ambitious projects like the ongoing study for the Atacama Large Aperture Submillimeter Telescope (AtLAST† ) inspired to investigate possible improvements of traditional Pointing Error (PE) modeling. The study assesses the practicality of FNNs by applying them to data from an instrument in operation: a precursor MeerKAT+ telescope from the Max Planck Institute for Radio Astronomy (MPIfR) to extend the current MeerKAT Radio Telescope Array at the South African Radio Astronomy Observatory (SARAO) site in the Meerkat National Park in South Africa.

We present a machine learning model based on deep encoder-decoder architecture that transforms an astronomical image into a latent space representation of the spatially varying Point Spread Functions (PSFs) within the image. This learned compressed representation can then be queried with a pixel position in order to create a realistic synthetic PSF for a transient source at that pixel. Our method is demonstrated using data from the Argus Pathfinder array’s transient detection pipeline. This methodology allows for more cost-effective generation of 100M+ image datasets for training transient detection pipelines and could be generalized to other next generation transient surveys.

We present results on integrating Machine Learning (ML) methods for adaptive optics control with a real-time control library: COmmon Scalable and Modular Infrastructure for real-time Control (COSMIC). We test the integration on simulations for the instrument SAXO+. Our proposed solution’s pipeline is formed by a two-model ML system. The first model consists of a very Deep Neural Network (DNN) that maps Wavefront Sensor (WFS) images to phase and is trained offline. The second model consists of predictive control with a more compact DNN. The predictive control stage is trained online, providing an adaptive solution to changing atmospheric conditions but adding extra complexity to the pipeline. On top of implementing the solution with COSMIC, we add a set of modifications to provide faster inference and online training. Specifically, we test NVIDIA’s TensorRT to accelerate the DNNs inference, reduced precision, and just-in-time compilation for PyTorch. We show real-time capabilities by using COSMIC and improved speeds both in inference and training by using the recommendations mentioned above.

he WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of optical and near infrared instruments, including an extreme precision, optical spectrograph, NEID, built for exoplanet radial velocity studies. In order to achieve sub ms⁻¹ precision, NEID has strict requirements on survey efficiency, stellar image positioning, and guiding performance, which have exceeded the native capabilities of the telescope’s original pointing and tracking system. In order to improve the operational efficiency of the telescope we have developed a novel telescope pointing system, built on a recurrent neural network, that does not rely on the usual pointing models (TPoint or other quasi-physical bases). We discuss the development of this system, how the intrinsic properties of the pointing problem inform our network design, and show preliminary results from our best models. We also discuss plans for the generalization of this framework, so that it can be applied at other sites.

The Submillimeter Array (SMA) requires precise full-sky blind pointing for its eight 6m antennas, aiming for an error within 3′′, a fraction of the 34′′ FWHM beam at 345 GHz. SMA’s typical 2–3′′ rms pointing accuracy is crucial for efficient array operation, especially with 4 to 6 antenna relocations across 23 pads in various configurations each semester. Traditional calibration using optical guidescopes for mount model errors has shifted to interferometric pointing measurements on quasars, for full model acquisition and baseline calibration. Following every array reconfiguration, mechanical imperfections in antenna mounting lead to significant deviations in azimuth encoder offset and axis tilt parameters, complicating pointing accuracy. To overcome this, a three-layer feed-forward neural network, trained on over ten years of data for each antenna-pad configuration, predicts post-reconfiguration changes. This approach, currently under evaluation and refinement, aims to expedite re-calibration, indicating potential substantial reductions in calibration time and enhanced operational efficiency.

Specreduce is an AstroPy-coordinated python package whose goal is to be a toolbox of functions and utilities that are relevant to the reduction of spectroscopic data. It is largely focused on optical/IR spectroscopy where the raw data consists of an image projected from a spectrograph onto a 2D imaging detector. The way the spectral and spatial information is encoded into these 2D images can be quite complex and varied (e.g. multi-object vs multi-fiber vs integral field spectroscopy). Methods and algorithms for handling this variety of data have been implemented across many previous and existing data pipelines. Specreduce aims to collect these best practices into a common, shared space that facilitates more collaboration and easier development of future spectroscopic pipelines.

With active time-domain surveys like the Zwicky Transient Facility, the anticipated Rubin Observatory’s Legacy Survey of Space and Time, and multi-messenger experiments such as LIGO/VIRGO/KANGRA for gravitational wave detection and IceCube for high-energy neutrino events, there is a new era in both time-domain and multi-messenger astronomy. The Astro2020 decadal survey highlights effectively responding to these astronomical alerts in a timely manner as a priority, and thus, there is an urgent need for the development of a seamless follow-up infrastructure at existing facilities that are capable of following up on detections at the surveys’ depths. At the W. M. Keck Observatory (WMKO), we are actively constructing critical infrastructure, aimed at facilitating the Target-of-Opportunity (ToO) trigger, optimizing observational planning, streamlining data acquisition, and enhancing data product accessibility. In this document, we provide an overview of these developing services and place them in context of existing observatory infrastructure like the Keck Observatory Archive (KOA) and Data Services Initiative (DSI).

Moving into the age of Time Domain Astronomy, robust, automated data reductions systems become essential. Here we present BANZAI-FLOYDS, a fully automated long-slit data reduction pipeline for the FLOYDS spectrograph at Las Cumbres Observatory. BANZAI-FLOYDS is fully written in Python, implementing wavelength calibration, fringe correction, object detection and tracing, telluric correction and flux calibration. The pipeline builds on the BANZAI library which handles the data flow and engineering allowing BANZAI-FLOYDS to only focus on spectroscopic processing. This design enables modularity of the processing stages allowing rapid development and encourages reuse for other spectrographs.

The Simons Observatory (SO) is a next-generation ground-based telescope located in the Atacama Desert in Chile, designed to map the cosmic microwave background (CMB) with unprecedented precision. The observatory consists of three Small Aperture Telescopes (SATs) and one Large Aperture Telescope (LAT), each optimized for distinct but complementary scientific goals. To achieve these goals, optimized scan strategies have been defined for both the SATs and LAT. This paper describes a software system deployed in SO that effectively translates high-level scan strategies into realistic observing scripts executable by the telescope, taking into account realistic observational constraints. The data volume of SO also necessitates a scalable software infrastructure to support its daily data processing needs. This paper also outlines an automated workflow system for managing data packaging and daily data reduction at the site.

METIS will be the first-light mid-infrared instrument at the ELT. Given the expected performance of the ELT’s adaptive optics systems, METIS will be able to probe regions of the sky previously inaccessible to astronomers. In support of both the METIS integration and verification efforts as well as the astronomical community at large, the METIS pipeline team has begun work on the METIS data reduction pipeline. The METIS pipeline will be written mostly in Python to take advantage of the new data reduction tools released by ESO. The development schedule has been set in such a way that the pipeline team will be able to directly support the testing and verification efforts during the upcoming system integration phase for METIS. In order to ensure that the required pipeline functionality is available when it is needed, the recipes and workflows functionality has been broken down into four levels of readiness: skeleton, functional, performance, and science-grade. This breakdown aims to ensure a more agile approach to the pipeline implementation as well as enabling productive contributions from all members of the highly geographically distributed team.

High Performance Computing based simulations are crucial in Astrophysics and Cosmology, helping scientists investigate and understand complex astrophysical phenomena. Taking advantage of Exascale computing capabilities is essential for these efforts. However, the unprecedented architectural complexity of exascale systems impacts simulation codes. The SPACE Center of Excellence aims to re-engineer key astrophysical codes to adapt to these new computational challenges by adopting innovative programming paradigms and software solutions. Through co-design activities, SPACE brings together scientists, code developers, HPC experts, hardware manufacturers, and software developers. This collaboration enhances exascale astrophysics and cosmology applications, promoting the use of exascale and post-exascale computing capabilities. Additionally, SPACE addresses high-performance data analysis for the massive data outputs from exascale simulations, using machine learning and visualization tools. The project facilitates application deployment across platforms by focusing on code repositories and data sharing, integrating European astrophysical communities around exascale computing with standardized software and data protocols. In this paper, we present the SPACE Center of Excellence and the preliminary results achieved by the project.

Symbolic regression techniques are promising approaches to learning mathematical models that fit experimental data. One of the most powerful techniques for symbolic regression is Grammatical Evolution (GE). This evolutionary computation technique explores a space of candidate models that are ensured to be syntactically correct expressions built from a set of arbitrary building blocks and operators. In GE the syntax for these expressions is defined by a problem-specific formal grammar. Therefore, GE can produce an explainable solution (e.g. a formula), not a black-box model. The current contribution assesses the viability of GE for PSF characterization, using real datasets from HST/WFPC2. Our experiments show that our method is able to find the most likely candidate mathematical expression for the PSF shape and can also model combinations of shapes taken from a predefined family of functions commonly used in astronomy (Gaussian and Moffat PSFs). These results support the hypothesis that the expressive power of GE can be used to tackle the problem of characterization of complex PSF functions, for example, as a necessary step in the prediction of intra-pixel position of stars.

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult.

A method for unanticipated fault diagnosis based on IGWO-iForest (Improved Grey Wolf Optimizer-Isolation Forest) is proposed to address various unpredictable problems faced by large telescopes in extreme environments. First, the random forest feature selection algorithm is used to identify the features of the original dataset and eliminate redundant features. Secondly, the differential evolution strategy is introduced into the GWO (Grey Wolf Optimizer) to improve the local search efficiency and accuracy, and the Levy flight strategy is introduced into the GWO to improve the global search ability of the algorithm. Then, the improved IGWO is used to optimize the parameters of the iForest model. Finally, the performance of the model is verified through data collected from a fault diagnosis and self-healing hardware-in-the-loop simulation platform. The experimental results show that the IGWO-iForest algorithm achieves a fault diagnosis accuracy of 99.1%, which demonstrates its higher sensitivity to a small number of unanticipated fault data compared with other anomaly detection algorithms, proving the effectiveness of this method in accurately diagnosing unanticipated faults in telescopes

In order to address the challenges of the Rubin Science Platform, Rubin developed a kubernetes-based approach to service deployment with an in-house service configuration and support infrastructure called phalanx, based on ArgoCD. It became apparent that the challenges of running a service-oriented architecture in a modern observatory summit lent themselves equally well to this approach. In this talk we will describe how phalanx was adapted for use for telescope, instrument and sensor control services and the advantages of providing a unified service infrastructure for both control systems and data services.

The amount of astronomical data that needs to be archived, calibrated, and processed continues to increase as telescopes and observing instruments advance. Securing necessary resources to store and process ever-increasing data is an operational challenge. To solve these issues, we conducted a demonstration experiment using ALMA archived data to efficiently utilize a commercial cloud for archive storage and data analysis pipeline processing. In archiving, a hybrid configuration combining on-premise storage and cloud based short-term and long-term storages is cost-effective, considering the trends on the number of data downloads over time since the data was obtained. In the data analysis processing, information on processing time and resource usage, such as memory and CPU core, measured during the pipeline process of approximately 400 observation data sets was analyzed, and a model was created to estimate processing time and the required amount of resources from the observation parameters. Based on the model created, the amount of required resources is predicted based on observation parameters, and an instance with the necessary and sufficient resources for pipeline processing is launched on demand on the cloud. These pipeline processes were completed with resources in a processing time comparable to that of on-premise ones. Since prices, services, computing resources, etc. on commercial cloud are updated frequently, we plan to continue making periodic estimates.

Datalab, the La Silla Paranal Observatory Platform for data analysis, is being migrated from Docker Swarm to Kubernetes to align with the integrated operations program's goals: Remote, Lean, Sustainable, and High performance. The migration implied to move from an on-premises to a cloud-native infrastructure replicated locally into a Cloud-Edge, providing hybrid cloud containerized applications support, implementing DevOps practices and automation. Using infrastructure as code and configuration management tools like Terraform and Ansible. Building CI/CD pipelines in Gitlab to automatically deploy the proper infrastructure into to the hybrid cloud to hold Kubernetes clusters (Azure Kubernetes Cluster and Vanilla Kubernetes). This approach allows the Observatory to enhance efficiency, reducing power consumption and improving scalability. Using Datalab as proof of concept but setting up the foundation to standardize these technologies in the organization. This paper outlines the provisioning and deployment of the new hybrid cloud infrastructure, providing a concise overview of its architecture, operational impact, and benefits for the observatory.

In the rapidly evolving landscape of software development the adoption of containerization has transformed the software supply chain. Containers encapsulate software components, ensuring consistency across multiple development, testing, and production environments. They foster agility and scalability by enabling microservices architecture and DevOps practices. The recent increase in cyberattacks targeting research institutes makes it critical to have a secure supply chain for containers and their orchestration. This paper delves into the integration of containers within the software supply chain, examining best practices and challenges in orchestration, security, and continuous integration and delivery (CI/CD) and distribution. We focus on how containers are secured from build stage, verified and distributed securely and validated in production, while also exploring the implications for dependency management and obsolescence in modern cloud-native infrastructures. Our analysis provides insights into maximizing the benefits of containerization to streamline development pipelines and enhance software supply chain resilience.

The 10.4m Gran Telescopio Canarias or GTC currently stands as the world's largest infrared optical telescope, notable for its considerable scale and intricate operational complexities. It is operated by the Spanish state-owned company GRANTECAN S.A. The telescope aligns with prevailing trends in industry, emphasizing smart factory strategies rooted in Industry 4.0, with a recent pivot towards human-centric human-machine interaction (Industry 5.0). The initial focus was identified in refining connectivity and defining the typology of sensors for optimizing operations. This led to the implementation of an Industrial Internet of Things or IoT middleware, grounded in Fiware3. The incorporation of smart sensors, leveraging links and protocols like LoRaWAN, Bluetooth, and Wi-Fi, has been streamlined through MQTT connectivity while the integration of AI, edge computing, confined space control, flow management, electric vehicle charging, and photovoltaics represents a multifaceted technological augmentation. Looking ahead, the primary challenges lie in bolstering cybersecurity (Operations Technology or OT), achieving seamless integration with Business Process Management (BPM), advancing data analytics capabilities, standardizing prescriptive maintenance protocols, and refining logistics processes. It is now possible to obtain data and information in a flexible way from the systems, and they can interact with each other. The connection to higher decision layers and cost savings is now also an opportunity, together with the ease of deployment made possible by the system's scalability. This has allowed a real and tangible focus on anticipating action on maintenance and operations, rather than reacting to problems encountered, among other opportunities.

The ELT Control System can be divided into the Central Control System (CCS) and subsystems Local Control Systems (LCS). At the heart of the CCS we have the High-Level Coordination and Control (HLCC) software which offers a single interface to the telescope towards the operators and instruments and coordinates the telescope subsystems. HLCC interfaces to the Local Control Systems (LCS) via the respective subsystem Local Supervisors (LSV). The LSVs are then responsible for interfacing to the different LCSs converting from the astronomical and user domains into actions and measurements in the individual device’s domain. Following celestial objects, i.e. tracking, is done on three ELT LSVs, the Main Structure (MS), the Dome and the Pre-Focal Station (PFS) LSVs. HLCC distributes the target information and the involved LSVs compute periodically the trajectory setpoints using the CCS’s pointing engine for their respective devices. The tracking also considers a dynamic pointing origin, used to cope with the fact that the instruments might not have a perfectly aligned center of rotation. Pointing models that consider imperfections and physical effects are used for the MS and PFS LSVs. The timestamped setpoints are sent to the corresponding LCSs and feedback is gathered using deterministic channels.

Once in operation, the Vera C. Rubin Observatory will execute a ten-year-long survey of the Southern sky known as the Legacy Survey of Space and Time (LSST). The Rubin Observatory Control System (Rubin-OCS) is a distributed system with each component in charge of a particular sub-system e.g.; the mount, the M1M3 mirror support system, etc. Each component is designed as an independent part of the system, and they must work together during operations. Communication between the components is done by means of a software middleware. The software middleware is the backbone of the system, allowing components to communicate with each other in a seamless way. The highly distributed nature of the Rubin-OCS places tight constraints in terms of latency, availability, and reliability for the middleware. The baseline implementation of the Rubin-OCS adopts the Data Distribution Service (DDS) technology for the middleware. In the Rubin-OCS, the middleware is encapsulated with a layer of abstraction known as the Service Abstraction Layer (SAL), which currently uses the ADLink-OpenSpliceDDS implementation of the Data Distribution Service (DDS) message passing system. Recently we performed a study of using Apache Kafka to replace DDS as the middleware technology for the Rubin-OCS. This study was motivated by the middleware-related challenges we faced while integrating the system as well as the recent announcements indicating that the adopted library may be deprecated during the lifespan of the project. The study involved throughput and latency studies and a proof of concept of our core libraries. Overall Kafka proved to be a suitable replacement for DDS.

The Square Kilometre Array Observatory (SKAO) is an international organization that currently builds two multi-purpose radio telescope arrays. The SKA Low Frequency Telescope array (SKA Low), located in the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory in Western Australia, with the observing range 50 - 350 MHz, will consist of more than 131,072 log-periodic antennas organized as 512 stations; the maximum distance between two stations is 65 kilometres. The SKA Mid Frequency Telescope array (SKA Mid), located in the Karoo region, Northwestern Cape province, South Africa, with the observing range 350 MHz - 15 GHz, will comprise 197 offset-Gregorian dishes; the dishes are 15 metres in diameter, the maximum baseline is 150 kilometres. This paper provides an overview of the automated attribute alarm handling in Tango Controls devices of the SKA Control System, the current software design for the early stages of the observatory, its horizontally scalable deployment, its integration and the lessons learned when real users and engineers deploy and use software.

The Wide-Area Linear Optical Polarimeters (WALOPs) are two instruments - WALOPNorth and WALOPSouth - that will be installed at the Skinakas and South African Astronomical Observatories respectively. Their goal is to work towards a polarimetric map of the Galaxy, for the needs of the PASIPHAE collaboration. The WALOP instruments, to be able to operate smoothly, require custom-made software to fit their (and the survey’s) specifications. We will present said software’s specifications and the methods and technologies used to meet these requirements.

The Wide-Field Infrared Transient Explorer (WINTER) is a new fully robotic infrared time-domain survey instrument at the Palomar Observatory, commissioned in June 2023. WINTER is performing a seeing-limited time domain survey of the infrared (IR) sky to detect, discover, and characterize astrophysical time-domain phenomena. As a dedicated observatory for real-time detection and rapid follow-up of infrared transient and variable targets, WINTER represents a new capability for multi-messenger astrophysics. We will describe the robotic software architecture of the WINTER Supervisor Program (WSP) which handles autonomous scheduling of both surveys and target-of-opportunity interrupts, as well as control and remote monitoring of the observatory, telescope, and cameras.

A highly optimized E-field Parallel Imaging Correlator (EPIC), currently under commissioning on the Long Wavelength Array in Sevilleta, New Mexico, can image the sky at a rate of 25,000 FPS per polarization and frequency. The system consists of six processing nodes, each producing images of the visible sky with a 1-degree spatial resolution at an 80 ms temporal resolution, covering a 3.2 MHz spectral window below 100 MHz, yielding a total bandwidth of 19.2 MHz. Light curves for selected sources of interest will be extracted from each image into a distributed database, and 5-minute accumulations are archived on the disk for further analysis. In this paper, we describe the components of our real-time imaging system, designed as a plug-and-play solution to deploy EPIC on similar arrays with only minor modifications.

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation atmospheric Cherenkov gamma-ray project. CTAO will be deployed at two sites, one in the Northern and the other in the Southern Hemisphere, containing telescopes of three different sizes for covering different energy domains. The commissioning of the first CTAO Large-sized Telescope (LST-1) is being finalized at the CTAO Northern site. Additional calibration and environmental monitoring instruments such as laser imaging detection and ranging (LIDAR) instruments and weather stations will support the telescope operations. The Array Control and Data Acquisition (ACADA) system is the central element for onsite CTAO operations. ACADA controls, supervises, and handles the data generated by the telescopes and the auxiliary instruments. It will drive the efficient planning and execution of observations while handling the several Gb/s camera data generated by each CTAO telescope. The ACADA system contains the CTAO Science Alert Generation Pipeline – a real-time data processing and analysis pipeline, dedicated to the automatic generation of science alert candidates as data are being acquired. These science alerts, together with external alerts arriving from other scientific instruments, will be managed by the Transients Handler (TH) component. The TH informs the Short-term Scheduler of ACADA about interesting science alerts, enabling the modification of ongoing observations at sub-minute timescales. The capacity for such fast reactions – together with the fast movement of CTAO telescopes – makes CTAO an excellent instrument for studying high-impact astronomical transient phenomena. The ACADA software is based on the Alma Common Software (ACS) framework, and written in C++, Java, Python, and Javascript. The first release of the ACADA software, ACADA REL1, was finalized in July 2023, and integrated after a testing campaign with the LST-1 finalized in October 2023. This contribution describes the design and status of the ACADA software system.

We have recently initiated a multi-institutional research program that will examine existing pipelines and catalog potential sources of variation in their resulting RV measurements. Through a series of EPRV community meetings we aim to establish community recommended, standardized formats for EPRV data products and to develop/distribute the tools necessary for direct comparisons of EPRV data between modern instruments. This program will lay the groundwork for a modular, open source, EPRV analysis toolbox that will be compatible with a wide variety of current and future instruments. Here we will provide a progress report on the program’s steps towards this community-endorsed data standard, and highlight lessons learned from the early years of operation across the NEID, KPF, EXPRES, CARMENES, HPF, and MAROON-X RV spectrographs.

Introducing HERMES (HOP Enabled Rapid Message Exchange Service), an application which supports sharing and querying structured data containing targets, photometry, spectroscopy, astrometry, and more. Many branches of astronomy, particularly time-domain and multimessenger astrophysics, are driven by time-critical alerts. Coordinating the community-wide response to provide characterization observations of the alerts is critical to realizing many of the science goals in these fields. As part of the SCIMMA (Scalable CyberInfrastructure to support multimessenger astrophysics) project, HERMES provides a platform for users to share messages and data in a structured format that can be sent over the SCIMMA Kafka streams, while also delivering a queryable database of those messages. The goal of HERMES is to encourage more astronomers to share data in a common, machine-readable format. While the platform is robust and general enough to handle many kinds of astrophysical data, HERMES is especially useful for non-localized event follow-up such as gravitational wave or neutrino events and maintains relationships between non-localized events and related messages and targets of interest. We discuss the Domain-Specific Language (DSL) designed for sharing structured astronomical data through HERMES, which also supports formatting and submitting data to external services such as NASA’s GCN (General Coordinates Network) circulars or the TNS (Transient Name Server). Finally, we present the integration between HERMES and TOM (Target and Observation Management) Toolkit based systems, allowing TOM users to share or ingest data through HERMES.

The forthcoming New Robotic Telescope, a collaboration between the UK and Spain, is poised to become the world’s largest and fastest autonomous observatory, located in La Palma. It is tailored to be a premier 4m class follow-up facility for the imminent wave of time-domain and transient astrophysics. It exemplifies innovation with its use of serverless architectures and a unified DevOps methodology, integrating Docker and Kubernetes to facilitate reliable, scalable, and responsive deployments both on-premises and cloud infrastructure. This model not only aligns with modern web-based principles and distributed deployments but also ensures that astronomers and operations staff have unfettered access to manage their observations, data products and monitoring of the facility in a unified modern interface, setting a new standard for modern astronomical research facilities. Building on the Liverpool Telescope’s autonomous robotic legacy, the New Robotic Telescope merges the GranTeCan Control System’s framework with a novel Robotic Control System, facilitating the transition from human-operated to fully automated observatory functions. We describe the current status of the infrastructure for the New Robotic Telescope software stack, focusing on the current DevOps infrastructure and ongoing development, as well as outlining the future work ahead of the initial construction of the telescope.

Radio astronomy is currently facing a significant challenge due to the massive data volumes generated by modern radio-interferometers, which will be further exacerbated by the upcoming Square Kilometre Array. Efficient data processing at this scale necessitates advanced High-Performance Computing (HPC) resources. Our work focuses on developing a novel approach to implement the w-stacking algorithm on state-of-the-art HPC systems, specifically targeting heterogeneous architectures comprising both CPUs and GPUs. We introduce the RICK (Radio Imaging Code Kernels) code, designed to efficiently process radio-interferometric data by leveraging the parallelism and computational power of modern HPC nodes. This study demonstrates the effectiveness of RICK on a single computing node, showcasing significant performance improvements over traditional methods. The paper outlines the methodology, the algorithmic innovations, and the parallelization strategy, along with performance benchmarks on various CPU/GPU configurations, highlighting the potential of RICK for future large-scale radio astronomy projects.

Initiated in 2008, the Canadian Advanced Network for Astronomical Research (CANFAR) has evolved into an advanced science platform, a cloud-native framework for remote analysis of astronomical data. This innovative platform is designed for a diverse range of users, from large-scale groups like CHIME-FRB, to individual researchers in remote institutions. It offers intuitive interfaces such as notebooks, desktops, visualizers and IDEs, all accessible through a browser. These are supported by multi-tiered storage and Kubernetes orchestration. A comprehensive REST API facilitates seamless integration and automated batch processing. CANFAR stands out by combining conventional desktop analysis with cutting-edge, browser-based interfaces and GPU-accelerated machine learning capabilities. This unique blend has made it a hub for a varied user community, establishing it as a unified platform for comprehensive astronomical data analysis.

MCAO Assisted Visible Imager and Spectrograph (MAVIS) is a new instrument for ESO’s VLT AOF. MAVIS embarks an Adaptive Optics (AO) system to cancel the image blurring induced by atmospheric turbulence. The latency and computational load induced by the system dimensioning led us to design a new software and hardware architecture for the Real Time Controller (RTC). Notably, the COSMIC framework harnesses GPUs for accelerated computation and is adept at scaling across multiple processes without overhead using shared memory. Employing a graph-based architecture where operations are intuitively represented as nodes. It aims at simplifying design, implementation, testing and integration by relying on robust concepts and useful tools. Recent updates have further enhanced its versatility, cementing its potential as a future-proof, extensible framework for AO advancements and their development process.

Large sky surveys from dedicated survey telescopes allow astronomers to access large swathes of the optical and near-infrared sky at high spatial resolution. The increasingly large volume of multi-wavelength imaging and catalogue photometry data products they generate are a cumbersome data management burden for survey teams. Even the simplest of tasks like visual inspection can quickly become out of reach when trying to apply the traditional astronomical software toolkit familiar to most astronomers. More complex tasks like quality assurance and scientific exploitation demand interactive visualization of data products alone (e.g. catalogues overlaid on imaging) and in conjunction with multiwavelength data from other surveys. Here we introduce a web application that effortlessly performs these tasks for the reprocessed imaging (⪆20TB) and catalogues (⪆100GB) of the VISTA Hemisphere Survey. The application is powered by the asynchronous architecture developed for data central’s data aggregation service that can query and receive several data streams simultaneously. Users can inspect a single target or navigate multiple targets from an uploaded list. Survey images are dynamically converted from FITS to HiPS format before being loaded into an Aladin Lite instance. Multiple catalogues are retrieved and accurately drawn in Aladin Lite using markers and ellipses. Dynamically generated tables summarize catalogue metadata with links to full record information. Users requiring more than the provided visualization and adjustable image scaling can download image and catalogue cutouts for closer inspection. With further science-oriented refinement we plan to release the application as part of the data central science platform.

Operating a cutting-edge radio telescope like ALMA demands optimal utilization of every minute available in the sky. With an increasing allocation of observation hours to researchers each year, the imperative for continuous, seamless operations grows. ALMA relies on an array of computer systems functioning on a full-time basis, with numerous concurrent users, generating approximately 50,000 logs per minute and a staggering 70 million logs per day. Addressing the challenge of managing this voluminous data flow, log detector emerges as an in-house solution designed to automate the detection and reporting of known issues. By scrutinizing logs, this tool empowers users to define Finite State Machine (FSM) states and transitions. Subsequently, users can feed logs into this machine, inducing state transitions that signal potential problems or facilitate system monitoring tasks. This article aims to spotlight the capabilities of Log Detector and its impact on operational efficiency. Additionally, it offers insights into the lessons learned while developing an in-house operational tool and outlines future development plans.

At Vera C. Rubin Observatory, the need to manage metrics and telemetry data efficiently led to the creation of Sasquatch. Sasquatch consolidates our high-frequency telemetry harness, which captures the observatory engineering data, with the science performance metrics measured by the LSST Science Pipelines. Sasquatch utilizes InfluxDB, a time series database, to efficiently store and query time-series data. We combine InfluxDB Enterprise with Apache Kafka and deploy our solution on the Kubernetes platform. Our current setup at the US Data Facility enables real-time access to data mirrored from the Summit and leverages tools like Chronograf for time series data visualization, Kapacitor for alert management, and the Rubin Science Platform’s notebook environment for data analysis using Python. Sasquatch is currently employed during Rubin Observatory’s System Integration Testing and Commissioning phase and is an essential service as we transition into survey operations.

The Square Kilometre Array Observatory (SKAO) is a next-generation radio telescope that is being built in two locations in the Karoo region of South Africa and the Murchison region of Western Australia forming one Observatory run from a global headquarters based in the United Kingdom at Jodrell Bank. At the heart of the SKA software, there will be a database that persists and replicates its metadata between these three sites that have different functions. This paper will describe the main requirements for the SKA database, how this has been done in the past at the Atacama Large Millimeter/Sub millimeter Array (ALMA) in Chile, the lessons learned there from the ten years of operations and what new options have appeared to handle the SKA, three site telescope needs. The solution needs to be highly available, performant, cost effective and easy to implement and maintain in the long term.

We GPU ported with CUDA the solver module of the Astrometric Verification Unit–Global Sphere Reconstruction (AVU–GSR) pipeline for the ESA Gaia mission. The code finds the astrometric parameters of approximately 10⁸ sources, by solving a linear system with the LSQR. The coefficient matrix is large (10–50 TB) and sparse. The CUDA code accelerates over the original MPI + OpenMP solver of approximately 14x on CINECA cluster Marconi100. We migrated the code production to Leonardo, which has 4x GPU memory per node. This speedup was obtained without computing the system covariances, whose total number is N_unk × (N_unk − 1)/2 and occupy approximately 1 EB with N_unk approximately 5 × 10⁸. This “Big Data” problem cannot be solved with standard methods: we defined a two jobs, I/O-based pipeline, where one job writes the files and the second concurrent job reads them, iteratively computes the covariances, and deletes them. The covariances calculation does not significantly slowdown the code until a number of covariances elements equal to approximately 8 × 10⁶ .

The United States Extremely Large Telescope Program (US-ELTP) is a joint effort of three organizations: the Thirty Meter Telescope (TMT) International Observatory (TIO), the Giant Magellan Telescope Corporation (GMTO), and the U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory (NSF NOIRLab). The US-ELTP will provide US astronomers with access to observing and archival science with the GMT and TMT. A user services suite and integrated data management and science platform, the US-ELT NOIRLab Program Platform (NPP), is being developed by NOIRLab; its scope, conceptual architecture, and development progress are described here. The NPP leverages community best practices and lessons from existing and upcoming facilities such as the International Gemini Observatory, the Vera C. Rubin Observatory, and the Daniel K. Inouye Solar Telescope (DKIST) and is guided by inclusive and collaborative design principles to broaden access to GMT and TMT. The NPP will support the creation of proposal and observing programs, scheduling and execution of observations, data archival and automated data reduction, and exploratory data analysis. The system, its support, extensive documentation, and ample training will be guided by regular community surveys and interviews with a diverse range of community members, and, eventually, through usability testing based on prototypes and mockups.

Aspera is a NASA-funded UV SmallSat Mission in development with a projected launch in 2025. The goal of the mission is to detect and map warm-hot gas in the circumgalactic medium of nearby galaxies traced by the Ovi emission line at 103.2 nm. To that goal, Aspera will conduct long-exposure observations at one or more spatial fields around each target galaxy, employing two long-slit spectrographs. Spectra from both channels are focused on a single micro-channel plate detector. In preparation of the mission’s launch, we are developing a data reduction pipeline, the goal of which is to reconstruct a calibrated 3D IFU-like data cube by combining the photon event lists obtained during each observation for a given target galaxy. In this proceedings paper, we present an outline for the data reduction pipeline and describe the data flow through the processing of science observations. We will further discuss individual steps to be applied to the data during the processing and show how our final data cubes shall be reconstructed. Finally, we will present our planned data products and discuss how simulations of the Aspera data cubes are being used to develop the pipeline.

The Japan Astrometry Satellite Mission for Infrared Exploration (JASMINE) aims at high-precision astrometry in the near-infrared wavelengths (1.0–1.6 μm). This mission focuses on the Galactic center region, obscured by interstellar dust in optical wavelengths. JASMINE’s observation strategy differs from other missions and must be verified via dedicated simulations. To verify the mission concept, we designed a simplified simulation, the JASMINE mini survey, covering three years with 100 orbits. As a simple case, the data obtained in a single satellite orbit are analyzed simultaneously (Plate Analysis). The observation model was made differentiable and implemented as a probabilistic model to make the best use of Stochastic Variational Inference. Model parameters converged to a certain solution, while the observation model contained more than 30,000 parameters. The estimated coordinates well represented the stellar motions expected from the ground truth. A typical positional error was estimated to be about 70 µas, consistent with the measurement error and the number of measurements. The present results validate parts of JASMINE’s mission concepts, leading to significant advancements in understanding the Galactic center.

The Cherenkov Telescope Array Observatory (CTAO) is a major next-generation instrument in ground-based gamma-ray astronomy that will become operational in the era of multimessenger astronomy. With its unmatched sensitivity and angular resolution, CTAO will play a pivotal role in the study of transient phenomena in the GeV-TeV range. The Transients Handler is the component within the Array Control and Data Acquisition (ACADA) system that enables CTAO to respond swiftly to alerts about transient events with automatically scheduled observations. The Transients Handler’s tasks include (i) filtering thousands of events per night from multiple external and internal alert streams, (ii) matching these events with scientific proposals, (iii) determining the optimal observation strategy, and (iv) scheduling observations within five seconds of receiving an alert. Recently, in October 2023, the first implementation of the Transients Handler was successfully tested during the integration of ACADA with the first CTAO Large-sized Telescope (LST-1). In this contribution, we will present the design of the Transients Handler in detail and preview updates that will be introduced in the next implementation.

In 2016 the SKA Organisation decided that, due to the complexity of the software and the uncertainty of the implementation details, the software development would be managed as an agile development programme. This led to a strategic decision to retain control of the primary risks of software construction and, by implication, some degree of central management. In the years before construction began, we selected the Scaled Agile Framework® as the basis for agile development and the NEC4® framework as the contract structure to hire the developers needed. The latter is complemented by the Vested® methodology for creating highly collaborative business relationships. All of these are unusual in our environment but reflect world best practice in other areas. We report on the progress of the contracts after about three years of construction. Overall, we believe it has been a success, with: the competitive rates offered allowing the engagement of more resources than what was estimated at the time of the SKA System CDR due to the low risk structure; positive results from a regular "happiness" survey of the developers; high levels of trust; a strong engagement with suppliers actively contributing to the governance; and the development being largely on time and budget with no expected impact on the critical path. Finally, we will report on the lessons we have learned and what we feel others should take away to consider for use in future large scale scientific software programmes.

The Giant Magellan Telescope (GMT) Software and Controls (SWC) team is responsible for designing, implementing, and maintaining the GMT Observatory Control System (OCS). GMT software modules are developed either in-house, or in collaboration with GMT partner institutions, following an Agile software development process. However, these software industry best-practices require significant tailoring to integrate well with other Engineering disciplines on a large, complex project such as GMT. In this paper we explore the various challenges in managing software development and how we are tackling them at GMT. Key areas include building the right team, handling programmatic challenges, streamlining development processes and engaging with customers and stakeholders. We’ve learned that people are at the heart of what we do, and the health of the team directly affects our ability to deliver high quality software on time and within budget. Also, managing limited resources is a common theme, requiring many different solutions in different domains. We have found the most effective to be a combination of process-optimization, resource-loaded scheduling, agile development, drastic overhead reduction and regular review of top priorities to help the team focus on what is important. Lastly, active engagement and efficient communication with customers and other stakeholders from the very beginning, help to set clear expectations and sets the team up for success. The team has made tremendous progress in the last few years in these areas and will continue to do so in the future due to a commitment to continuous improvement.

The ViaLactea Knowledge Base (VLKB) was designed and initially developed within the EU FP7 VIALACTEA project that included a Work Package dedicated to create infrastructure and tools to perform research in Milky-Way astrophysics. The infrastructure’s goal was to set up an archive and services to enable that research. About 50 dataset collections ( 35k datasets of various sizes, in FITS format), ten catalogues of compact sources (from thousands to a few million rows), a catalogue of morphological complex sources (few thousand sources), and a few other catalogues and simulated datasets were included in the archive, worth about 1TB of data. On top of those data, and their metadata descriptions, a set of services was deployed: a search and access (cutout and merge) service for the datasets, a general Table Access Protocol (TAP) service for all metadata and catalogues and some other dedicated attempts for serving specific user requirements. All the interfaces were developed in combination with the dedicated client, the ViaLactea Visual Analytics (VLVA) but were designed keeping in mind the discovery and access scenario that is continuously developed in the Virtual Observatory (VO) ecosystem. Indeed, interoperability was brought inside the VLKB afterwards, slowly (depending on the limited resources available after the end of the Vialactea project), mostly when the VLKB resources kept being used in galactic astrophysics projects or as a comprehensive resource of data and services in technical demonstrator projects. Those projects provided the continuity in funding basic maintenance of the VLKB and some updates (even if occasionally rather than continuously). With the first release of the VLKB in 2016, the subsequent maintenance gap spanning from then until 2020, and the restart of development since then, the current adoption of standards in the VLKB includes: an ObsCore table to keep the metadata for the observational datasets’ catalogue, the TAP service to expose the general metadata content for all its data resources (catalogues, images, radial velocity cubes and morphological complex objects, . . . ), a custom implementation of the SODA (Server-side Operation for Data Access) standard set up to replace the dataset cutouts (with UWS - Universal Worker Service - used to manage asynchronous cutout and merge requests). Furthermore, authentication and authorization infrastructure (AAI) solutions using OAuth/OIDC have been tested on top of the cutout service, and a multi-cutout solution has been presented at VO level as a feedback for the SODA and DataLink evolution. Other features (management of complex morphology, of simulated data, and registration of the VLKB resources into the VO Registry) are still undergoing or missing. In particular, enabling more standard interface for the VLKB and making VLVA aware and able to consume them, will let both the client be more general and easier to maintain and the server resources be consumed by non-dedicated client applications. This contribution reports the challenges in maintaining and improving the VLKB, the actual status of the technologies and standards in use for its resources, and the present and future perspectives for the VLKB itself.

The Observatory Science Operations (OSO) subsystem of the SKAO consists of a range of complex tools which will be used to propose, design, schedule and execute observations. Bridging the gap between the science and telescope domains is the key responsibility of OSO, requiring considerations of usability, performance, availability and accessibility, amongst others. This paper describes the state of the observatory software as we approach construction milestones, how the applications meet these requirements using a modern technology architecture, and challenges so far.

The Giant Magellan Telescope (GMT) is a next-generation ground-based segmented telescope. In the last few years, significant progress has been made by the GMT team and partners to design a natural guide-star wavefront control strategy that can reliably correct wavefront error, including the discrete piston aberration between segment gaps. After an extensive set of simulations and external reviews, the team proposed a design of a Pyramidal Wavefront Sensor (PWFS) combined with a Holographic Dispersed Fringe Sensor (HDFS) and started building a prototype for integrating a GMT simulator (High Contrast AO Testbed) with a PWFS and an HDFS. The prototype was developed in collaboration with the University of Arizona, INAF-Arcetri, and the GMT observatory. The software development of the adaptive optics controllers and the interfaces between all testbed components were done using the GMT software frameworks, as they will be implemented for the final observatory software. The GMT framework is model-based, and the software component interfaces are defined using a domain-specific language (DSL). In this paper, we show how the design of the testbed software fits within GMT's component-based architecture and what each partner was responsible for delivering. We discuss the challenge of a multidisciplinary team from multiple institutions in different time zones working together on the same software, describe how the software architecture and development process helped to ensure seamless integration and highlight other accomplishments and lessons learned.

Professional software engineering techniques have yet to consistently find their way into research software engineering. To address this problem, we develop the new LOFAR2.0 proposal tool in short iterations where software engineers strive for technical excellence to build maintainable software. Simultaneously, we embed stakeholders with an interest in academic concerns in this process to manage these concerns appropriately. Intensified collaboration between software engineers and relevant stakeholders creates more maintainable, durable, and reusable research software. This paper discusses the challenges we encounter and practical ways of working that help bridge the gap between academia and software engineering.

Despite Python being the preferred programming language of choice for most astronomers, building or extending data reduction pipelines in the language can be problematic. A common approach is to write Python functions or classes as wrappers, calling individual pipeline recipes underneath, but this does not scale well with increasing pipeline complexity. Data management is also fraught since housekeeping code must be written to carefully handle input and output products between recipes. We have addressed these issues by creating an extensible pipeline development framework that leverages the Python bindings for the ESO Common Pipeline Library (PyCPL) toolkit. Pipeline recipes can be defined in a regulated manner using existing ESO pipeline recipes or new Python recipes compliant with ESO standards. Users can easily build their own pipeline workflows for execution by the PyCPL companion package PyEsorex. The ability to define Python recipes offers a powerful means to extend existing ESO pipelines or develop entirely new pipelines. An overview of the framework is presented along with an illustrative MUSE pipeline workflow.

Thermal control of the MMT Observatory (MMTO) primary mirror (M1) has been optimized to reduce M1 thermal anomalies and mirror seeing while enhancing overall imaging quality. These refinements include 1) increased use of temperatures from outside, chamber, M1 glass, and U.S. National Weather Service sources for thermal control, 2) expanded monitoring and analysis of the M1 glass temperatures, 3) integration of multiple feedback-based PID (proportional-integral-derivative) controllers in M1 thermal control, and 4) extensive data analysis of thermal anomalies and trends within the M1 mirror and surrounding telescope enclosure. The newly deployed control strategy uses the minimum temperature from different sources (i.e., outside, chamber, and forecast) to regulate the temperature of the conditioned air used to cool the M1 mirror. The controlling temperature source commonly changes during the night. Before this current work, thermal control of the M1 ventilation system used a linear regression model to determine the setpoint of the main glycol chillers. This simple approach has been replaced by a combination of open-loop and closed-loop, feedback-based PID servo controllers that regulate the chillers and coolant valves along the M1 ventilation air path. Different feedback temperatures for the various PID servos are considered, allowing for more detailed and responsive conditioning of air within the M1 ventilation system. Comparison of M1 glass-air temperature contrasts to wavefront-sensor (WFS) seeing values define optimal performance conditions. This work has led to recommendations for operational changes that aim to improve thermal conditions for the M1 mirror and telescope chamber, including during the transition from daytime to nighttime activities.

This research analyzes historical data from the ASTRI-Horn, a Cherenkov telescope at the Astrophysical Observatory of Catania (Serra La Nave, Mt. Etna). Data from a multitude of sensors, distributed across the telescope, were studied. These sensors record various parameters, including currents, voltages, phases, positions, and temperatures, from different telescope components such as motors and encoders, as well as environmental conditions like temperature and humidity. Seven years of operational data have been analyzed to identify precursors indicative of component degradation. The aim was to discern unique data patterns or "signatures" corresponding with periods of component damage or replacement. These identified signatures will be instrumental in the development of a Predictive Maintenance (PdM) model, which will aim to foresee the standard operational patterns, issuing alerts for any detected anomalies or deviations, thereby facilitating early anomaly detection and resolution. PdM is an advanced maintenance strategy that uses data to help predict when parts might fail aiming to reduce unexpected costs, improving the overall efficiency and reliability of the telescope.

BlueMUSE is a blue, medium spectral resolution, panoramic integral-field spectrograph under development for the Very Large Telescope (VLT). We demonstrate and discuss an early End-To-End simulation software for final BlueMUSE datacube products. Early access to such simulations is key to a number of aspects already in the development stage of a new major instrument. We outline the software design choices, including lessons learned from the MUSE instrument in operation at the VLT since 2014. The current simulation software package is utilized to evaluate some of the technical specifications of BlueMUSE as well as giving assistance in the assessment of certain tradeoffs regarding instrument capabilities, e.g., spatial and spectral resolution and sampling. By providing simulations of the end-user product including realistic environmental conditions such as sky contamination and seeing, BlueSi can be used to devise and prepare the science of the instrument by individual research teams.

Modern astronomical telescopes often rely on control systems for observations. Many factors may affect the development of control systems, such as the differences in the development phases of devices, the robustness of devices. A simulation framework which mocks the component of each device is needed to speed up the development of control system, facilitating behavior-level simulation to support the upper layer development. Presently, many industry-standard simulation systems are predominantly based on actual hardware systems, which necessitate the development of independent hardware logic, such as the simulator of LSST. We have designed the Rsimu framework. This framework is built upon the RACS2 and is highly proficient in behavior simulation of devices. Rsimu's behavior is entirely configurable, and the properties of different components can be dynamically defined by pluggable configuration files. A shared data-plane is provided for components to synchronize their status, therefore helps developers to separate the behavior model of components apart. A series of designs, including pull-update, state-machine etc. are provided to help users to establish the simulation system.

We present the integral field unit part of the data reduction pipeline for METIS (Mid-infrared ELT Imager and Spectrograph), a first-generation infrared instrument that will be installed on the Extremely Large Telescope. The described software covers the entire process of correcting the instrumental effects and reconstructing the hyperspectral image. Apart from standard correction procedures common to virtually all digital imagers, the pipeline includes methods for distortion calibration, wavelength and flux calibration, correction of telluric absorption, reconstruction of the spectral cube with special emphasis on resampling the data only once, and finally algorithms for spatial and spectral dithering of multiple exposures taken at different field orientations and shifts, possibly taken many months apart. The pipeline has already passed the final design review and its implementation is underway.

The Square Kilometre Array is an international project to build two radio interferometric telescopes, with a mid-frequency portion in South Africa and a low-frequency portion in Australia. The low-frequency portion of the telescope will contain 131,072 individual dipole antennas, each of which will need to have its health monitored. Previously, this monitoring in the Aperture Array Verification System 2 (AAVS2) had been done manually by publishing antenna bandpasses to a web-page where an engineer inspects the bandpasses for faulty data. This is a practical solution for AAVS2, however as the number of antennas is scaled up, an automated solution will be required. Using a random forest model trained on AAVS2 data manually classified as faulty or not, bandpass data can be classified with an accuracy of 98%. Testing this with Aperture Array Verification System 3, all faulty bandpasses were detected in the 512 bandpasses produced. This random forest model is encapsulated in a TANGO device independent of the devices controlling the antennas, so that as the number of antennas is scaled, the processing overheads are minimised. This also makes reconfiguration of the model (e.g. to use gradient boosting or to adjust the number/size of trees) a straightforward process. This model can then be used to highlight antennas which are suspected to be producing faulty data and mark that the antenna is not to be used for observation.

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based instrument for gamma-ray astronomy. CTAO will be located at two sites, one in the Northern (La Palma, Spain) and the other in the Southern Hemisphere (Paranal, Chile), with telescopes in three different sizes to cover different energy ranges. The commissioning of the first CTAO Large-Sized Telescope (LST-1) is being finalized at the CTAO-North site. The Array Control and Data Acquisition (ACADA) software is a central element of on-site CTAO operations. ACADA comprises subsystems for central control, the short-term scheduler, monitoring systems, and data handling at rates of GB/s. Consequently, it is a very complex software that requires many developers with different expertise, such as control software, data acquisition, data analysis, scheduling, configuration, and human interfaces. To implement such complex software, ACADA has been broken down into subsystems, which CTAO delegates to expert developer teams around the world through in-kind contributions. All the software is under version control exploiting a dedicated installation of GitLab. We have created at least one repository for each subsystem and a final one for the integration. We have defined the software development and integration procedures so that all phases of the Software Development Life Cycle (SDLC) are supported. Particular attention has been paid to the critical time when a software version is in operation on site and, bug-fixing and new features need to be kept under version control in parallel. The goal is to manage bug fixes without adding new features out of the scope of the release, but at the same time to guarantee the distribution of bug fixes for future releases. This contribution presents our strategy to manage multiple software versions according to the CTAO development plan.

MORFEO (Multi-conjugate adaptive Optics Relay For ELT Observations) is a new instrument being built for the ESO’s Extremely Large Telescope (ELT). The project is in the final design phase, and it is expected to be commissioned in 2030. The Instrument Control Software of MORFEO will be based on the new ESO ELT software framework, which is still under development, and a key activity during the control software implementation is the Continuous Integration (CI) of the code. Continuous integration is the practice of automating the integration of code changes from multiple contributors into a single software project. We present the current CI workflow that ensures that both the software control team and the software quality assurance team can work synchronously, effectively and independently. We also present the options we considered and the reasons that led us to choose this workflow.

With dozens of telescopes in both hemispheres, the Cherenkov Telescope Array Observatory (CTAO) will be the largest ground-based gamma-ray observatory and will offer extensive energy coverage from 20 GeV to 300 TeV. Its large effective area, wide field-of-view, rapid slewing capability, and exceptional sensitivity will make CTAO an essential instrument for the future of ground-based gamma-ray astronomy. Furthermore, its two arrays will send alerts on transient and variable phenomena (e.g., gamma-ray bursts, active galactic nuclei, gamma-ray binaries, and serendipitous sources) to maximise the scientific return. Effective and rapid communication with the community requires a reliable automated system to detect and issue candidate science alerts. This automation will be achieved by the Science Alert Generation (SAG) pipeline, a core system of the CTA Observatory. The SAG is part of the Array Control and Data Acquisition (ACADA) system. The SAG working group develops pipelines for data reconstruction, data quality monitoring, science monitoring, and real-time alert issuance to the Transients Handler system of ACADA. The SAG performs the first real-time scientific analysis during data acquisition. The system analyzes data on multiple time scales (from seconds to hours) and must issue candidate science alerts with 20 seconds of latency and at least half the CTAO nominal sensitivity. Dedicated, highly optimized software and hardware architectures must be designed and tested to satisfy these stringent requirements and manage trigger rates of tens of kHz from both arrays. In this work, we present the general architecture and current development status of the ACADA/SAG system.

EMIR (Espectrografo Multiobjeto Infra-Rojo) is a wide field, near infrared, multiobject spectrograph, with image capabilities, which is currently located at one of the Nasmyth focus of the 10.4m GTC (Gran Telescopio Canarias). It allows observers to obtain many intermediate resolution spectra simultaneously, in the near IR bands: Z, J, H and K. A configurable cryogenic multislit mask unit provides target acquisition too. This paper describes the upgrade of EMIR (to EMIR+) which incorporates a new Teledyne H2RG infrared detector, using the SIDECAR integrated controller and a Markury Scientific MACIE interface card over IP communication. A detailed description of the Data Acquisition System (DAS), integrated into the GTC Control System (GCS) software, is given. It configures the URG or FS acquisition modes, starts the acquisition process, capture the data coming from the H2RG unit, stores the FITS data and propagate the images to produce astronomical files. We also developed a Python-based MACIE Controller Simulator to test and debug the DAS, which works as a real MACIE interface, responding to all petitions and generating test images to feed the DAS or other control programs.

Cloud computing offers unparalleled flexibility, a constantly increasing set of “Infrastructure as a Service’’ capabilities, resource elasticity and security isolation. One of the most significant barriers in astronomy to wholesale adoption of cloud infrastructures is the cost for hot storage of large datasets - particularly for Rubin, a Big Data project sized at 0.5 Exabytes (500 Petabytes) over the duration of its ten-year mission. We are planning to reconcile this with a “hybrid” model where user-facing services are deployed on Google Cloud with the majority of data holdings residing in our on-premises Data Facility at SLAC. We discuss the opportunities, status, risks, and technical challenges of this approach.

The Very Large Telescope Interferometer (VLTI) must control its Optical Path Differences (OPD) to extremely high precision in order to achieve its characteristic and desired high performance. This proves a challenge when using Very Large Telescope’s (VLT) 8 meter Unit Telescopes (UT) given they are not fully dedicated to interferometry and can be equipped with up to three different instruments each. Among the several important control systems that allow the VLTI to achieve the necessary precision for this task is Manhattan II (MNII), which measures vibrations along the Optical Path (mirrors M1 to M7) and sends Optical Path Length (OPL) corrections to the Delay Lines (DL). In the context of GRAVITY+ upgrade, MNII is being extended to cover a larger portion of the light path (previously M1 to M3) and expanded with Phase-locked Loop (PLL) to improve OPD control by targeting specific frequencies. Alongside, several options are being explored to further improve the capabilities of the system. Active compensation is improved by the upgrade of MNII’s PLL. In addition, better troubleshooting tools and automatic Anomaly Detection (AD) systems are needed to constantly monitor and react to the changing vibration signature of the UTs. Furthermore, similar AD systems will be fundamental in the future for the operation of the upcoming Extremely Large Telescope (ELT). This work is about the ongoing efforts to develop an automatic AD system using Machine Learning on MNII’s vibration data. We focus on the different methods and models used in the proof of concept which include Auto-encoders, clustering and classical statistical methods as well, the infrastructure required to have a working end-to-end prototype, the data pipeline, preprocessing and the future envisioned production system.

The Large Millimeter Telescope (LMT) is a 50m-diameter single dish millimeter-wave radio telescope located in Mexico, built by the country of Mexico and the University of Massachusetts Amherst. The National Science Foundation Mid-Scale Innovations Program (MSIP) is now supporting access to the LMT for any astronomer located at a US institution with a share of total 15% of the scientific observation time. The LMT cyber-infrastructure is being innovated to accommodate the new science operation workflow. We developed and deployed the LMT data archive, fully integrating the data pipeline, data model, and data management workflow of most LMT instruments. For the LMT data archive, we use our own installation of the Dataverse software as the backend, with a custom-built frontend to provide a user-friendly search interface for discovering data products. The data model and data management workflow are developed along with the commissioning (hardware and/or software) of the instrument specific pipelines. The software package dvpipe is developed to package the data products from the instrument specific pipelines as science-ready data products and put them into the LMT data archive.

FORS2 (FOcal Reducer/low dispersion Spectrograph) is a multimode (imaging, polarimetry, long slit and multi-object spectroscopy) optical instrument mounted on the Cassegrain focus of the UT1 of ESO’s Very Large Telescope (VLT). Its versatility and large wavelength range (330-1100 nm) make it one of the most requested instruments at the VLT. To keep it operational for at least the next 15 years, the FORS upgrade project (FORS-Up), a collaboration between ESO and INAF-OATs, was started: the twin spectrograph FORS1, decommissioned in 2009, has been sent to Europe and is currently undergoing a complete refurbishment in the integration hall of the Astronomical Observatory of Trieste. Once the upgrade is finished, FORS1 will replace FORS2 at the VLT. In this paper, we report the status of the work currently in progress on the control software: the original one is based on the VLT standards, and it is now being reimplemented within the new ELT (Extremely Large Telescope) software framework. New GUIs have been designed for FORS, which give the user in-depth control over the instrument; new templates for observational, engineering and maintenance procedures have been developed; hardware components have been configured, either as standard devices or as special devices (requiring customized solutions). The upgrade will ensure the continued operation of FORS and represent an invaluable testbed for the new ELT software framework.

We present the advancements in the development of the scheduler for the Son Of X-shooter (SOXS, 1,2) instrument at the ESO-NTT 3.58-m telescope in La Silla, Chile. SOXS is designed as a single-object spectroscopic facility and features a high-efficiency spectrograph with two arms covering the spectral range of 350-2000 nm and a mean resolving power of approximately R=4500. Its primary purpose is to conduct UV-visible and near-infrared follow-up observations of astrophysical transients, drawing from a broad pool of targets accessible through the streaming services of wide-field telescopes, both current and future, as well as high-energy satellites. The instrument is set to cater to various scientific objectives within the astrophysical community, each entailing specific requirements for observation planning, a challenge that the observing scheduler must address. A notable feature of SOXS is that it will operate at the European Southern Observatory (ESO) in La Silla, without the presence of astronomers on the mountain. This poses a unique challenge for the scheduling process, demanding a fully automated algorithm that is autonomously interacting with the appropriate databases and the La Silla Weather API, and is capable of presenting the operator not only with an ordered list of optimal targets (in terms of observing constraints) but also with optimal backups in the event of changing weather conditions. This requirement imposes the necessity for a scheduler with rapid-response capabilities without compromising the optimization process, ensuring the high quality of observations and best use of the time at the telescope. We thus developed a new highly available and scalable architecture, implementing API Restful applications like Docker Containers, API Gateway, and Python-based Flask frameworks. We provide an overview of the current state of the scheduler, which is now ready for the approaching on-site testing during Commissioning phase, along with insights into its web interface and preliminary performance tests.

The Instrument Control Software of SOXS (Son Of X-Shooter), the forthcoming spectrograph for the ESO New Technology Telescope at the La Silla Observatory, has reached a mature state of development and is approaching the crucial Preliminary Acceptance in Europe phase. Now that all the subsystems have been integrated in the laboratories of the Padova Astronomical Observatory, the team operates for testing purposes with the whole instrument at both engineering and scientific level. These activities will make use of a set of software peculiarities that will be discussed in this contribution. In particular, we focus on the synoptic panel, the co-rotator system special device, on the Active Flexure Compensation system which controls two separate piezo tip-tilt devices.

The Cherenkov Telescope Array Observatory (CTAO) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. The CTAO will combine telescopes of different designs plus a large number of scientific instruments to achieve unprecedented performance and energy coverage. The Array Control and Data Acquisition (ACADA) system provides the means to execute observations and to handle the acquisition of scientific data in CTAO. The Resource Manager (RM) and Central Control (CC) subsystems are core components of the ACADA system. The RM subsystem provides infrastructure services concerning the administration of various resources to all ACADA subsystems. The CC subsystem implements the execution of observation requests received from the scheduler subsystem. CC interprets the received requests and sends appropriate commands to telescopes and other controllable array elements, supervises ongoing operations and coordinates the dynamic allocation and management of concurrent operations of telescopes to subarrays, which are logical groupings of individual CTAO telescopes performing coordinated scientific operations. This contribution provides a summary of the main design features, current status and future implementation plans for the ACADA RM and CC subsystems.

Software maintainability is a crucial aspect of software engineering, especially within research institutes with research infrastructure where software's longevity and adaptability directly impact scientific endeavors’ success. ASTRON, the Institute for Radio Astronomy in the Netherlands, has faced significant challenges in the past and present regarding the maintainability of its software. Previously, a rather unstructured approach was made to improve the state of software at ASTRON. Recently, a more structured approach has been taken to improve the overall state of the software landscape by employing different strategies. Astron started to use GitLab as its version control system including CI. ASTRON has started to structurally employ modern-day IDEs with all the tools that support better software engineering. Contemporary development practices have been used, and management practices have been adapted to modern software development. Lastly, ASTRON is investing in a shared model of language when building software systems together.

Radio-frequency interference (RFI) raises a challenging issue confronted by radio astronomy. This challenge is particularly pregnant when recording extremely faint signals such as those associated with pulsar observations. Indeed, generally of higher energy, RFI significantly degrades the quality of the measurements which makes astronomical data more difficult to interpret and analyze. The current solutions to tackle this problem usually consist in performing RFI flagging, i.e., localizing the time-frequency bins in the dynamic spectrum affected by interference. Then the RFI-corrupted data, i.e., the measurements associated with these identified bins, is generally discarded before any subsequent data processing, which unavoidably leads to a loss of information. Alternatively, this paper proposes to formulate RFI mitigation as a joint detection and restoration task to allow parts of the dynamic spectrum affected by RFI to be not only identified but also recovered. The proposed method relies on a particular instance of a recent architecture of deep convolutional networks. This network is trained on a large data sets generated within a simulation framework specifically designed according to physically-inspired and statistical models of the pulsar signals and of the RFI. Through extensive numerical experiments, the proposed approach is shown to reach competitive performance in terms of RFI detection and dynamic spectrum restoration.

MAVIS is the new MCAO Assisted Visible Imager and Spectrograph for ESO’s Very Large Telescope. It is intended to be installed at the Nasmyth focus of UT4 “Yepun” telescope and it is composed of two main parts: a multi conjugate adaptive optics module and its post focal instrumentation, an imager and an IFU spectrograph, both operating in the visible spectrum. The project is now in the final design phase, and it is expected to be commissioned in 2030. In this paper we focus on the interface between the Instrument Control System Software (ICSS) and the Soft Real-Time Computer (SRTC). ICSS is in charge of controlling all the motorized functions, managing the scientific exposures, monitoring the status of the system and coordinating the sequence of operations; on the other hand, RTC receives data from from the wavefront sensors (8 LGS and 3 NGS) to compute the corrections to be applied by the two-post focal deformable mirrors and 8 LGS jitter mirrors. ICSS will be based on the new ESO ELT software framework, which is still under development; SRTC will be based on the new ESO RTC Toolkit, also under development. We present the first design of the common interface between ICSS and SRTC, focusing mainly on the communication processes (commands and data) and which are the most critical points we had to face.

FORS (FOcal Reducer and Low Dispersion Spectrograph), a multi-mode optical instrument mounted on the Very Large Telescope's (VLT) UT1 Cassegrain focus, gets a new look. The upgrade, known as FORS-Up (FORS-Upgrade), is being carried out by ESO and INAF-OATs, and includes, beside replacement of some optical components, the replacement of all the motors, the development of a new calibration unit, the adoption of a new detector, and the design of a control electronics based on the new ELT standards. The refurbishment work has started on the twin spectrograph FORS1, decommissioned in 2009 which was sent to the integration premises of the Astronomical Observatory of Trieste. After resuming the final design of the control electronics, this paper presents the PLC software implementation and the current state of the electronics integration with the new mechanics carried out at INAF-OATs. It also focuses on the ELT-based software and hardware solutions that have been adopted to meet the performance and safety requirements for the motorized functions that control the multiobject spectroscopy blades and the scientific exposure shutter and require customized applications.

The Gran Telescopio Canarias (GTC), a 10.4-meter telescope with a segmented primary mirror comprising 36 hexagonal segments, relies on a crucial stabilization system to ensure the primary mirror behaves as a single entity. After 15 years of operation, some subsystems are beginning to incur high maintenance costs and are encountering obsolescence issues that, in some cases, limit their performance capabilities. The primary mirror stabilization system is among these subsystems, facing significant migration challenges due to its real-time characteristics and high number of hardware elements it manages. We introduce the validation process of a new platform based on Linux Preempt-RT, industrial Compact PCI computers, featuring real-time characteristics at both the operating system level and control card drivers. Due to the difficulty in accessing real hardware, both due to operational restrictions and security concerns, and to expedite the development process, a comprehensive test bench mimicking production hardware was constructed. Once the development reached sufficient maturity, tests were conducted on the production system, validating both the performance of the new solution and extracting performance metrics and satisfaction of real-time requirements. As a result, it has been confirmed that the new solution not only functions correctly and meets current requirements but also would allow for an increase in the nominal performance of the system and enable a control loop with higher performance.

During ongoing maintenance and development of software used to control the European Southern Observatory (ESO) Very Large Telescope (VLT), the detection of memory leaks in legacy and newly developed software is of the utmost importance. This paper describes investigations into the development and use of additional test support software using Machine Learning (ML) to determine the presence of memory leaks. The software is implemented to integrate within existing pytest code and is designed to be executed alongside software module nightly tests as part of Continuous Integration (CI) testing. The work’s prime objective is to highlight memory suspicious processes so that memory leaks can be found and fixed before software deployment at the observatory.

This paper presents a methodology to automate and accelerate the PLATO Payload (P/L) Boot Software (BSW) testing procedures by presenting a set of pre-programmed TCL scripts with different verification targets, satisfying the BSW requirements. These scripts are conceived in order to run an autonomous regression testing while verifying the BSW core functionalities, and in case of an additional BSW verification is needed, a set of scripts will be available for obtaining an automatic quick health-statement. The present method was proven by carrying out the pre-programmed functional and performance tests on the different PLATO’s BSW versions installed on the ICU development models. The tests performed on these models have proven their effectiveness during the BSW testing process, since the testing time has been greatly reduced and the test results can be archived to maintain a useful record that contemporaneously with the dedicated TCL scripts may assist in future verification of the flight BSW version.

The Low Frequency Array (LOFAR) is Europe’s largest radio telescope, originally designed, built and operated by ASTRON. It consists of an interferometric array of low band and high band antennas, distributed among 52 stations. Since 2018, a considerable upgrade of the main infrastructure has taken place both on the hardware and on the software side, the so-called LOFAR 2.0. The monitor and control software system of each LOFAR 2.0 station is based on the open-source TANGO-Controls framework, which manages the device architecture and the various functionalities of the station, including its states and transitions. Since each hardware device of the station is implemented as a software module, the startup of the station and its states transitions until a full operative state implies a non-trivial interaction and communication among the different device classes. The proposed design solution places each one of these devices in a specific hierarchical structure, which defines the parent-child relations and the allowed operations for its nodes. Besides that, the device hierarchy can be different according to the two main sequences that are involved in the station states transition: the power sequence and the control sequence. The whole set of sequential operations are entirely managed by the TANGO framework, in particular from a root device called Station Manager, which controls the children devices and the hierarchical sequences. In order to adhere to the TANGO architecture, the operations are mainly developed exploiting device attributes and properties, such that a potentially complex process is handled in a very straightforward, lightweight and maintainable way. The aforementioned software architecture has been already deployed and successfully tested on the LOFAR2 Test Station (L2TS) located in the Netherlands. Therefore, it is proving to be a primary feature for the whole LOFAR2 infrastructure, in view of a forthcoming fully operational phase within the next few years.

There is currently a strong push towards infrared astronomy, like the ground-breaking JWST and the upcoming ROMAN and Gaia NIR missions. The Japanese JASMINE telescope will be the first Near Infrared (NIR) astro-photometric mission to focus on the Galactic central region and, in many senses, it will be pioneering the field of NIR high-precision astrometry for Milky Way (MW) dynamics. In order to test our data processing pipelines, we require a robust and reliable way to generate mock images. In this contribution, we present the JASMINE input catalogue: the most complete census of point-like sources in the NIR towards the Galactic centre. We used this catalogue as a blueprint from which to generate mock sources that resemble real stars as much as possible, while offering also the possibility of generating entirely new sources to compensate for the observational incompleteness. The method, while conceptually simple, requires treating each star of the input catalogue as new evidence that updates our prior knowledge, which in this case is represented by the underlying model of the MW used. The result is a custom probability distribution function for each star from which to draw mock sources. This represents the biggest and most realistic mock catalogue of the MW centre to date. In the future, we will improve it by adding more proper motions and parallaxes to the input catalogue, and by modelling the dependence of the distance on the kinematics.

The expansion of scientific, economic, and military activity in space has driven concomitant growth in the diversity, dynamism, and size of the anthropogic space object population. The safety of spacecraft operations depends on the detection and monitoring of transient events among this population, such as satellite maneuvers, proximity operations, and component articulation, as well as the routine maintenance of high-accuracy ephemerides. Optical sensors are effective tools for this task when applied in large numbers. However, responsiveness to transient events necessitates geographic diversity, and cost-effectiveness motivates heterogeneity of aperture and instrumentation. Orchestrating a globally distributed, diverse collection of sensors to satisfy space-object tracking objectives remains an open challenge. In this work, we adopt the open-source MACHINA agentic software framework to embody an autonomous space domain awareness agent that addresses this challenge.

We introduce a novel software called TCSpy which is designed to efficiently control a multi-telescope array through network-based protocols. The primary objectives of TCSpy include centralized control of the array, support for diverse observation modes, and swift responses to the follow-up observations of astronomical transients. To achieve these objectives, TCSpy utilizes the ASCOM Alpaca protocol in conjunction with Alpyca, establishing robust communication among multiple telescope units. For the practical application of TCSpy, we implement TCSpy within the 7-Dimensional Telescope (7DT). 7DT is a telescope array consisting of 20, 0.5-m telescopes, equipped with 40 different medium-band filters. The main scientific goals of 7DT include detecting the optical counterparts of gravitational-wave sources, identifying kilonovae, and the spectral mapping of the southern sky. Through the integration of TCSpy, 7DT can achieve these scientific objectives with its unique observation modes and rapid follow-up capabilities.