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Register a new userLSST and the Dark Sector: Image Processing Challenges
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J.A. Tyson
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Next generation probes of dark matter and dark energy require high precision reconstruction of faint galaxy shapes from hundreds of dithered exposures. Current practice is to stack the images. While valuable for many applications, this stack is a highly compressed version of the data. Future weak lensing studies will require analysis of the full dataset using the stack and its associated catalog only as a starting point. We describe a Multi-Fit algorithm which simultaneously fits individual galaxy exposures to a common profile model convolved with each exposures point spread function at that position in the image. This technique leads to an enhancement of the number of usable small galaxies at high redshift and, more significantly, a decrease in systematic shear error.
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The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a 5000 square degree survey of the southern sky in five optical bands (g,r,i,z,Y) to a depth of ~24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g,r,i,z) over 27 square degrees. DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.
Euclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH).
Gaia is an ambitious space astrometry mission of ESA with a main objective to map the sky in astrometry and photometry down to a magnitude 20 by the end of the next decade. While the mission is built and operated by ESA and an industrial consortium, the data processing is entrusted to a consortium formed by the scientific community, which was formed in 2006 and formally selected by ESA one year later. The satellite will downlink around 100 TB of raw telemetry data over a mission duration of 5 years from which a very complex iterative processing will lead to the final science output: astrometry with a final accuracy of a few tens of microarcseconds, epoch photometry in wide and narrow bands, radial velocity and spectra for the stars brighter than 17 mag. We discuss the general principles and main difficulties of this very large data processing and present the organisation of the European Consortium responsible for its design and implementation.
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