Space observatories have many advantages over ground-based telescopes. However, constructing and launching large space telescopes remains a significant challenge. A solution to this problem lies in autonomous, in-space assembly. To gain benefits from efficiencies of scale and mass production, a modular telescope assembled in space can be constructed from identical mirror segments. These identical segments must then be deformed to an appropriate shape in space. This work examines the optical feasibility of such a project, using a 31 meter Ritchey- Chrétien telescope composed of about 1,000 1-m mirrors as a case study. In particular, this work examines the shape of the telescope optics through Zernike decomposition and computes the physical optics propagation of such a system to analyze the resultant PSF with simulation in Zemax OpticStudio.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a direct imaging campaign designed to search for new, young, self-luminous, giant exoplanet. To date, GPIES has observed nearly 500 targets, and generated over 30,000 individual exposures using its integral field spectrograph (IFS) instrument. The GPIES team has developed a campaign data system that includes a database incorporating all of the metadata collected along with all individual raw data products, including environmental conditions and instrument performance metrics. In addition to the raw data, the same database also indexes metadata associated with multiple levels of reduced data products, including contrast measures for individual images and combined image sequences, which serve as the primary metric of performance for the final science products. Finally, the database is used to track telemetry products from the GPI adaptive optics (AO) subsystem, and associate these with corresponding IFS data. Here, we discuss several data exploration and visualization projects enabled by the GPIES database. Of particular interest are any correlations between instrument performance (final contrast) and environmental or operating conditions. We show single and multiple-parameter fits of single-image and observing sequence contrast as functions of various seeing measures, and discuss automated outlier rejection and other fitting concerns. We also explore unsupervised learning techniques, and self-organizing maps, in particular, in order to produce lowdimensional mappings of the full metadata space, in order to provide new insights on how instrument performance may correlate with various factors. Supervised learning techniques are then employed in order to partition the space of raw (single image) to final (full sequence) contrast in order to better predict the value of the final data set from the first few completed observations. Finally, we discuss the particular features of the database design that aid in performing these analyses, and suggest potential future upgrades and refinements.
We present a new algorithm to be used for extraction of circumstellar discs from direct imagining observations. The underlying mechanism of this algorithm is Common Spatial Pattern filtering, a technique commonly found in fields outside astronomy. It is a method used to maximize the difference between two sets of data. We employ this by using distributing the disc signal in different locations, employing Angular Differential Imaging. By generating the difference, we can reconstruct the circumstellar disc in post-processing with limited speckle noise. We demonstrate the algorithm with a common disc: HR 4796a. These results are then compared to a current algorithm: Karhunen-Loeve Image Processing. Common Spatial Pattern filtering represents a new class of direct imaging signal extraction: modelling the signal directly rather than speckle subtraction.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the GPIES Data Cruncher, combines multiple data reduction pipelines together to intelligently process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow-up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our data reduction pipelines. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real-time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
One of the most effective methods to subtract the PSF of the host star and other confounding noise sources from direct imaging observations is Principal Component Analysis. Common Spatial Pattern filtering is a method from the same class of algorithms as PCA. We examine CSP as an alternative algorithm for PSF subtraction. The underlying principles of CSP are discussed, as well as the processing steps needed to achieve PSF subtraction. Both CSP and PCA have been used on data from the Gemini Planet Imager, analyzing images of β Pic b. Preliminary results indicate that CSP achieves similar results as PCA.
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