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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.
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The Dark Energy Survey (DES) is a 5000 deg2 grizY survey reaching characteristic photometric depths of 24th magnitude (10 sigma) and enabling accurate photometry and morphology of objects ten times fainter than in SDSS. Preparations for DES have included building a dedicated 3 deg2 CCD camera (DECam), upgrading the existing CTIO Blanco 4m telescope and developing a new high performance computing (HPC) enabled data management system (DESDM). The DESDM system will be used for processing, calibrating and serving the DES data. The total data volumes are high (~2PB), and so considerable effort has gone into designing an automated processing and quality control system. Special purpose image detrending and photometric calibration codes have been developed to meet the data quality requirements, while survey astrometric calibration, coaddition and cataloging rely on new extensions of the AstrOmatic codes which now include tools for PSF modeling, PSF homogenization, PSF corrected model fitting cataloging and joint model fitting across multiple input images. The DESDM system has been deployed on dedicated development clusters and HPC systems in the US and Germany. An extensive program of testing with small rapid turn-around and larger campaign simulated datasets has been carried out. The system has also been tested on large real datasets, including Blanco Cosmology Survey data from the Mosaic2 camera. In Fall 2012 the DESDM system will be used for DECam commissioning, and, thereafter, the system will go into full science operations.
The Dark Energy Survey Data Management (DESDM) system will process and archive the data from the Dark Energy Survey (DES) over the five year period of operation. This paper focuses on a new adaptable processing framework developed to perform highly automated, high performance data parallel processing. The new processing framework has been used to process 45 nights of simulated DECam supernova imaging data, and was extensively used in the DES Data Challenge 4, where it was used to process thousands of square degrees of simulated DES data.
We describe the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from Aug 2013 through Feb 2014. DES-SN is a search for transients in which ten 3-deg^2 fields are repeatedly observed in the g,r,i,z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernova (SN Ia) with the goal of measuring dark energy parameters. The essential DiffImg functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are 130 detections per deg^2 per observation in each band, of which only 25% are artifacts. Of the 7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least 2 separate nights, Monte Carlo simulations predict that 27% are expected to be supernova. Another 30% of the transients are artifacts, and most of the remaining transients are AGN and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies, and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a Monte Carlo simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 shallow fields with single-epoch 50% completeness depth 23.5, the SN Ia efficiency falls to 1/2 at redshift z 0.7, in our 2 deep fields with mag-depth 24.5, the efficiency falls to 1/2 at z 1.1.
Image Signal Processor (ISP) is a crucial component in digital cameras that transforms sensor signals into images for us to perceive and understand. Existing ISP designs always adopt a fixed architecture, e.g., several sequential modules connected in a rigid order. Such a fixed ISP architecture may be suboptimal for real-world applications, where camera sensors, scenes and tasks are diverse. In this study, we propose a novel Reconfigurable ISP (ReconfigISP) whose architecture and parameters can be automatically tailored to specific data and tasks. In particular, we implement several ISP modules, and enable backpropagation for each module by training a differentiable proxy, hence allowing us to leverage the popular differentiable neural architecture search and effectively search for the optimal ISP architecture. A proxy tuning mechanism is adopted to maintain the accuracy of proxy networks in all cases. Extensive experiments conducted on image restoration and object detection, with different sensors, light conditions and efficiency constraints, validate the effectiveness of ReconfigISP. Only hundreds of parameters need tuning for every task.
Processing of raw data from modern astronomical instruments is nowadays often carried out using dedicated software, so-called pipelines which are largely run in automated operation. In this paper we describe the data reduction pipeline of the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph operated at ESOs Paranal observatory. This spectrograph is a complex machine: it records data of 1152 separate spatial elements on detectors in its 24 integral field units. Efficiently handling such data requires sophisticated software, a high degree of automation and parallelization. We describe the algorithms of all processing steps that operate on calibrations and science data in detail, and explain how the raw science data gets transformed into calibrated datacubes. We finally check the quality of selected procedures and output data products, and demonstrate that the pipeline provides datacubes ready for scientific analysis.
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