OmegaCAM: The 16k x 16k Survey Camera for the VST

OmegaCAM: The 16k x 16k Survey Camera for the VST Observing and data reduction a Virtual Survey System Edwin A. Valentijn

Paranal July 2004

VLT Survey Telescope-VST • Alt-AZ - Cassegrain • aperture 2.610 m • corrected FOV 1.47 degree • lens corrector: U - z • Atmospheric disp. correct.: B -z • f/5.5 • scale 14.266 arcsec/mm • CCD pixel size: 15 um • 0.214 arcsec/pixel • image quality: 80% EE • two-lens: 1.70 pixel • ADC: 1.77 - 2.18 pixel

VST factory - Napoli

Detectors • Science array 1 x 1 degree, 32 CCDs • 15 mm pixels – 0.21 arcsec/pixel • Marconi (former EEV) 2k x 4k • 16k x 16k pixels • Auxiliary CCD’s – 4 CCDs • For guiding • Image analysis

Filters • Primary set: Sloan u’, g’, r’, i’, z’ high throughput interference • Johnson B, V, Stromgren-v • Segm Ha up to ~12000 km/s 658.8 665.5 672.2 678.9/ 10.7 nm • 1100, 4200, 7300, 10400km/sec / 4900 km/sec • Composite u’, g’, r’ ,i’ in four quadrants • Segm Ly alpha z=2-3 372, 400, 450, 507nm / 8 nm • Night sky leak CWL=851.8nm - 877.8nm /13nm

Wide Field Imaging Science • Provide targets for VLT • ~60% of time through ESO’s OPC • Individual programs • Supernovae, Lensing, Kuiper belt objects, Gamma ray, bursts, Microlensing, Brown dwarfs, High proper motion objects, Galactic halo objects, Quasars, AGNs • Sky Surveys • Long term archival research (10 yr mission) • Science Cases • Finding exceptional single, rare objects • Statistics on large samples of objects

Handling of the data is non-trivial • Pipeline data reduction • Calibration and re-calibration • Image comparisons and combinations • Working with source lists • Visualization } ESO compliant Large Data Volume • Wide-field imaging instruments, vast amounts of data • E.g.: VST = Southern sky (30 min exp, 300 nights/y) in 3 years. Large amount of data! 100 Tbyte of image data and Tbytes of source list data • Science can only be archive-based

Concepts for solutionVirtual Survey System • Environment that provides systematic and controlled • Access to all raw and calibration data • Execution and modification reduction/calibration pipelines • Execution of source extraction algorithms • Archiving reduced data and source lists, or regenerates these dynamically • Can be federated to link different data centers • Dynamical archive continuously grows, can be used for • small or large science projects • generating and checking calibration data • exchanging methods, scripts and configuration • Key functionality • Link back from source data to the original raw pixel data and calibration files

How to use this • Deep multi-color fields • No need to take all data in one campaign • Combine data of particular quality, assess results • Select sources, visualize interesting ones, … • 1-in-1,000,000 events spurious or not? • Large homogeneous surveys • E.g. weak lensing maps, cluster searches, star counts • Variability (source list- or pixel based) • Proper motions (asteroids, nearby stars) • Flux variations • Monitor instrument (calibration files) • Planning observations • View quality of existing data • Build on what already exists, add more filters, more exposure time, better seeing, …

Keys -Solution • Procedurizing • Data taking at telescope for both science and calibration data • Full integration with data reduction • Design • Data model (classes) defined for data reduction and calibration • View pipeline as an administrative problem

Observing Modes Dither • Dithermatching max. gap between arrays ~400 pixels • N pointings (N=5 is standard) • nearly cover all gaps in focal plane and maximizes sky coverage • Very complex context map • couple the photometry among individual CCDs. Dither with N = 5

Observing Modes Jitter • Jittermatching the smallest gaps in CCDs ~5 pixels • optimizes for maximumhomogeneity of the context map • observationsfor which the wide CCD gaps are not critical • all data from single sky pixel originates from single chip

Observing ModesStare and SSO • Stare reobserving fixed pointing positions multiple times • main workhorse monitoring instrumentand optical transients. • SSO observing Solar System objects • non-siderial tracking and the auto guiding switched off.

Strategiesscheduling observing modes • Standard • Single observations (one observing block) • Deep • Long, multiple integrations • Selected atmospheric conditions • Several nights • Frequent • Monitors same field • Timescales from minutes to months (overriding) • Mosaïc • Maps areas of sky > 1o

Calibration procedures Sanity checks Image pipeline Source pipeline Calibration procedures Quality control

Bias pipeline Source pipeline Flatfield pipeline Photometric pipeline Image pipeline Science Observations

Share the loadAstroWise Survey System • Processing • Hardware • Beowulf processors – 32 (most cases) • Multi Terabyte disks (10 – 100) • Data reduction • Derive calibration • Run image pipeline (1 Mpx/s) • Archiving • Storage • Images (100’s Tbyte), Calibration files (10 Tbyte) • Source parameters (1-10 Tbyte) • Federate (network speed) • 5 Mb/s (24 hours/day) full replication • 200 Mb/s no replication, on-the-fly retrieval

Concepts of federation • Federation maintained by a single database- Oracle9i • Full history tracking • of all input that went into result • providing on-the fly reprocessing • Dynamical archive - Context as object attributes • Project: Calibration, Science, Survey, Personal • Owner: Pipeline, Developer, User • Strategy: Standard, Deep, Freq (monitoring), Mosaïc • Mode: Stare, Jitter, Dither, SSO • Time: Time stamping VO interface • Software standards • Classes/data model/procedures • 00 – inheritance/ persistency • Python scripts/ c-libraries USER Python

Observing proposals • Garanteed time NOVA 10% • Call – 25 April--- see www.omegacam • Super clusters- distant clusters • Galactic structure • Weak shear, microlensing • Bulge • 2dF, 100 Sq Degree, 10000 Sq Deg • Deep field • Lorentz center July 2003 • Fall 2004

OmegaCAM: The 16k x 16k Survey Camera for the VST