The CARMENES instrument is a pair of high-resolution (R⪆80,000) spectrographs covering the wavelength range from 0.52 to 1.71 μm, optimized for precise radial velocity measurements. It was installed and commissioned at the 3.5m telescope of the Calar Alto observatory in Southern Spain in 2015. The first large science program of CARMENES is a survey of ~ 300 M dwarfs, which started on Jan 1, 2016. We present an overview of all subsystems of CARMENES (front end, fiber system, visible-light spectrograph, near-infrared spectrograph, calibration units, etalons, facility control, interlock system, instrument control system, data reduction pipeline, data flow, and archive), and give an overview of the assembly, integration, verification, and commissioning phases of the project. We show initial results and discuss further plans for the scientific use of CARMENES.
The main goal of the CARMENES instrument is to perform high-accuracy measurements of stellar radial velocities (1 m/s) with long-term stability. CARMENES is installed at the 3.5 m telescope in the Calar Alto Observatory (Spain) and it is equipped with two spectrographs covering from the visible to the near-infrared. We present the software packages that are included in the instrument control layer. The coordination and management of CARMENES is handled by the Instrument Control System (ICS), which is responsible for carrying out the operations of the different subsystems providing a tool to operate the instrument in an integrated manner from low to high user interaction level. The ICS interacts with the following subsystems: the near-infrared (NIR) and visible channels, composed by the detectors and exposure meters; the calibration units; the environment sensors; the front-end electronics; the acquisition and guiding module; the interfaces with telescope and dome; and, finally, the software subsystems for operational scheduling of tasks, data processing, and data archiving. The software control framework and all the software modules and layers for the different subsystems contribute to maximize the scientific return of the instrument. The CARMENES workflow covers from the translation of the survey strategy into a detailed schedule to the data processing routines that extract radial velocity data from the observed targets. The control suite is integrated in the instrument since the end of 2015.
Astronomical observatories are complex systems requiring the integration of numerous devices into a common platform. We are presenting here the firsts steps to integrate the popular Robotic Operating System (ROS) into the control of a fully autonomous observatory. The observatory is also equipped with a decision-making procedure that can automatically react to a changing environment (like weather events). The results obtained so far have shown that the automation of a small observatory can be greatly simplified when using ROS, as well as robust, with the implementation of our decision-making algorithms.
CARMENES, the new Calar Alto spectrograph especially built for radial-velocity surveys of exoearths around M dwarfs, is a very complicated system. For reaching the goal of 1 m/s radial-velocity accuracy, it is appropriate not only to monitor stars with the best observing procedure, but to monitor also the parameters of the CARMENES subsystems and safely store all the engineer and science data. Here we describe the CARMENES data flow from the different subsystems, through the instrument control system and pipeline, to the virtual-observatory data server and astronomers.
The Exoplanet Characterisation Observatory (EChO) was an ESA mission candidate competing for a launch opportunity within the M3 call. Its main aim was to carry out research on the physics and chemistry of atmospheres of transiting planets. This requires the observation of two types of events: primary and secondary eclipses. The events of each exoplanet have to be observed several times in order to obtain measurements with adequate Signal-to-Noise Ratio. Furthermore, several criteria must be considered to perform an observation, among which we can highlight the exoplanet visibility, its event duration, and the avoidance of overlapping with other tasks. It is important to emphasize that, since the communications for transferring data from ground stations to the spacecraft are restricted, it is necessary to compute a long-term plan of observations in order to provide autonomy to the observatory. Thus, a suitable mission plan will increase the efficiency of telescope operation, and this will result in a raise of the scientific return and a reduction of operational costs. Obtaining a long-term mission plan becomes unaffordable for human planners due to the complexity of computing the large amount of possible combinations for finding a near-optimal solution. In this contribution we present a long-term mission planning tool based on Genetic Algorithms, which are focused on solving optimization problems such as the planning of several tasks. Specifically, the proposed tool finds a solution that highly optimizes the objectives defined, which are based on the maximization of the time spent on scientific observations and the scientific return (e.g., the coverage of the mission survey). The results obtained on the large experimental set up support that the proposed scheduler technology is robust and can function in a variety of scenarios, offering a competitive performance which does not depend on the collection of objects to be observed. Finally, it is noteworthy that the conducted experiments allow us to size some aspects of the mission with the aim of guaranteeing its feasibility.
The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint Propagation techniques. A simulation platform, an analysis tool and different test case scenarios for CTA were developed to test the performance of the scheduler and are also described.
The main goal of the CARMENES instrument is to perform high-accuracy measurements of stellar radial velocities (1m/s) with long-term stability. CARMENES will be installed in 2015 at the 3.5 m telescope in the Calar Alto Observatory (Spain) and it will be equipped with two spectrographs covering from the visible to the near-infrared. It will make use of its near-IR capabilities to observe late-type stars, whose peak of the spectral energy distribution falls in the relevant wavelength interval. The technology needed to develop this instrument represents a challenge at all levels. We present two software packages that play a key role in the control layer for an efficient operation of the instrument: the Instrument Control System
(ICS) and the Operational Scheduler. The coordination and management of CARMENES is handled by the ICS, which is responsible for carrying out the operations of the different subsystems providing a tool to operate the instrument in an integrated manner from low to high user interaction level. The ICS interacts with the following subsystems: the near-IR and visible channels, composed by the detectors and exposure meters; the calibration units; the environment sensors; the front-end electronics; the acquisition and guiding module; the interfaces with telescope and dome; and, finally, the software subsystems for operational scheduling of tasks, data processing, and data archiving. We describe the ICS software design, which implements the CARMENES operational design and is planned to be integrated in the instrument by the end of 2014. The CARMENES operational scheduler is the second key element in the control layer described in this contribution. It is the main actor in the translation of the survey strategy into a detailed schedule for the achievement of the optimization goals. The scheduler is based on Artificial Intelligence techniques and computes the survey planning by combining the static constraints that are known a priori (i.e., target visibility, sky background, required time sampling coverage) and the dynamic change of the system conditions (i.e., weather, system conditions). Off-line and on-line strategies are integrated into a single tool for a suitable transfer of the target prioritization made by the science team to the real-time schedule that will be used by the instrument operators. A suitable solution will be expected to increase the efficiency of telescope operations, which will represent an important benefit in terms of scientific return and operational costs. We present the operational scheduling tool designed for CARMENES, which is based on two algorithms combining a global and a local search: Genetic Algorithms and Hill Climbing astronomy-based heuristics, respectively. The algorithm explores a large amount of potential solutions from the vast search space and is able to identify the most efficient ones. A planning solution is considered efficient when it optimizes the objectives defined, which, in our case, are related to the reduction of the time that the telescope is not in use and the maximization of the scientific return, measured in terms of the time coverage of each target in the survey. We present the results obtained using different test cases.
This paper gives an overview of the CARMENES instrument and of the survey that will be carried out with it
during the first years of operation. CARMENES (Calar Alto high-Resolution search for M dwarfs with Exoearths
with Near-infrared and optical Echelle Spectrographs) is a next-generation radial-velocity instrument
under construction for the 3.5m telescope at the Calar Alto Observatory by a consortium of eleven Spanish
and German institutions. The scientific goal of the project is conducting a 600-night exoplanet survey targeting
~ 300 M dwarfs with the completed instrument.
The CARMENES instrument consists of two separate echelle spectrographs covering the wavelength range
from 0.55 to 1.7 μm at a spectral resolution of R = 82,000, fed by fibers from the Cassegrain focus of the telescope.
The spectrographs are housed in vacuum tanks providing the temperature-stabilized environments necessary to
enable a 1 m/s radial velocity precision employing a simultaneous calibration with an emission-line lamp or with
a Fabry-Perot etalon. For mid-M to late-M spectral types, the wavelength range around 1.0 μm (Y band) is the
most important wavelength region for radial velocity work. Therefore, the efficiency of CARMENES has been
optimized in this range.
The CARMENES instrument consists of two spectrographs, one equipped with a 4k x 4k pixel CCD for
the range 0.55 - 1.05 μm, and one with two 2k x 2k pixel HgCdTe detectors for the range from 0.95 - 1.7μm.
Each spectrograph will be coupled to the 3.5m telescope with two optical fibers, one for the target, and one
for calibration light. The front end contains a dichroic beam splitter and an atmospheric dispersion corrector,
to feed the light into the fibers leading to the spectrographs. Guiding is performed with a separate camera;
on-axis as well as off-axis guiding modes are implemented. Fibers with octagonal cross-section are employed to
ensure good stability of the output in the presence of residual guiding errors. The fibers are continually actuated
to reduce modal noise. The spectrographs are mounted on benches inside vacuum tanks located in the coud´e
laboratory of the 3.5m dome. Each vacuum tank is equipped with a temperature stabilization system capable
of keeping the temperature constant to within ±0.01°C over 24 hours. The visible-light spectrograph will be
operated near room temperature, while the near-IR spectrograph will be cooled to ~ 140 K.
The CARMENES instrument passed its final design review in February 2013. The MAIV phase is currently
ongoing. First tests at the telescope are scheduled for early 2015. Completion of the full instrument is planned
for the fall of 2015. At least 600 useable nights have been allocated at the Calar Alto 3.5m Telescope for the
CARMENES survey in the time frame until 2018.
A data base of M stars (dubbed CARMENCITA) has been compiled from which the CARMENES sample can
be selected. CARMENCITA contains information on all relevant properties of the potential targets. Dedicated imaging, photometric, and spectroscopic observations are underway to provide crucial data on these stars that
are not available in the literature.