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Imaging Surveys: Goals/Challenges

Imaging Surveys: Goals/Challenges. Luiz da Costa European Southern Observatory. May 12, 2005. Outline. Past & Future Imaging Surveys The EIS project experience: lessons learned EIS Data Reduction System Applications. Motivation. Large-aperture telescopes (Keck, VLT, OWL?)

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Imaging Surveys: Goals/Challenges

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  1. Imaging Surveys: Goals/Challenges Luiz da Costa European Southern Observatory May 12, 2005

  2. Outline • Past & Future Imaging Surveys • The EIS project experience: lessons learned • EIS Data Reduction System • Applications

  3. Motivation • Large-aperture telescopes (Keck, VLT, OWL?) • Expanded MOS capabilities • Space Missions: HST, Chandra, XMM, Spitzer, Galex • Advent of wide-field imagers (optical/infrared) enabling to probe new regions of parameter space

  4. Science Goals • Galaxy Evolution • Constrain Dark Energy • Abundance and clustering evolution of galaxy clusters • Weak gravitational lensing on large scales • Evolution of the spatial distribution of galaxies • Luminosity distances of Type 1a SNe • Transient phenomena • Solar system probes: asteroids, comets, TNOs • Variable stars/galaxies • Gamma-ray bursts • Extreme (cool, distant, unknown) rare objects which require depth and area

  5. Recent Surveys • General purpose surveys • NOAO • INT • EIS • Targeted surveys (deep, space/ground) • COMBO-17 • FORS • SUBARU • FIRES • GOODS

  6. Some Recent Optical and Near IR Surveys Optical Near IR SDSS DLS NWDS NWDS BTC

  7. Next Generation (Optical) • CFHTLS • 4-m; MegaCam 36 CCD 1 x 1 degrees fov • Very wide 1300 sq degrees 3-bands • Wide 170 sq degrees 5-bands i’=24.5 (lensing, LSS) • Deep 4 square degrees r=28 (gal. evolution) • VST • 2.5m; OmegaCam 32 CCD 1 x 1 square degree fov • Vesuvio 100 square degrees 5-bands R=26 • OmegaCam survey 1000 sq.deg. 5-bands r’=24 AB • Data rate from observation 0.84 Mpix/sec

  8. Next Generation (Optical) • Dark Energy Survey (DES) • Blanco telescope 60 CCD 3 square deg. 500 Mpixels • 4000 sq. deg 4-bands R=24 • complement SZ cluster survey South Pole Telescope • Pan-STARRS (Panoramic Rapid Response) • 4 small wide-field telescopes • 6000 sq deg per night r=24 • LSST (Large Survey Synoptic Telescope) • 8-m telescope, 3 sq.deg fov; 3200 Mpixels • 5-band; 18,000 sq.deg; 26.5 AB • 10 Petabyte/year; 100 Mpixels/sec (2012) !!!

  9. Next Generation (IR) • UKIDSS • UKIRT 4-m; 4 2k x 2k Rockwell devices 0.21 sq.deg. • LAS 4000 sq.deg K =18.4 • GPS 1800 sq.deg K=19.0 • GCS 1400 sq.deg K=18.7 • DES 33 sq.deg. K=21 • UDS 0.77 sq.deg. K=23.0 • VISTA • 4-m telescope; 1 sq.deg. Fov; 16 2k x 2k • Data rate from observations 1.28 Mpix/sec

  10. Optical Near IR PS LSST QUEST SDSS CFH-L VST DLS NWDS CFH-L NWDS BTC GOODS UDF HDF

  11. Challenges • Cope with : • large data rates and volumes • different instruments and/or strategies • archival data • Enable re-calibration (tests) of large sets and comparison with previous versions (talk to 2MASS and SDSS people) • Ensure quality of large volume and diverse nature of products • Reduced (nightly), subtracted, stack and mosaic images; • single-band, color catalogs • galaxy and star catalogs, clusters of galaxies, color-selected objects • Administrate & distribute data/products/information timely • Small budgets for operation

  12. In other words … How to enable relative small teams to efficiently provide reliable data from which to extract science from many simultaneous SLOANS (in volume) using different instruments and observing techniques Not even with several graduate students !!!

  13. Basic Requirements • High-throughput, instrument-independent image processing system • keep up with input data rate • handle time critical observations (SN searches, short-period variability) • integrate scheduling & reductions to optimize observations • Administrative layer for: data management, consolidated procedures, and bookkeeping • Re-calibration: associated database enabling version; history; comparison • Well-defined interfaces between processes to: • Ensure quality & consistency of survey products • Provide support for configurable workflows • Avoid relying on individuals • Software enabling un-supervised operations in 24/7 duty-cycle over long stretches of time • Flexible hardware architecture to optimize operations (beowulf x cluster) New Science requires new techniques The EIS experience

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