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NIR Transient Surveys: Innovations and Challenges in Near-Infrared Astronomy

This paper discusses the advancements and challenges in near-infrared (NIR) transient surveys conducted by various institutions, including WFAU and UKIRT. It highlights the significance of NIR observations for studying young stellar objects, low-mass stars, and high-redshift galaxies, while addressing the issues with detector limitations and data processing bottlenecks. The article emphasizes the multi-epoch work necessary for significant discoveries within dense, dusty regions of the Milky Way and presents an overview of specific survey projects and their methodologies, including calibration techniques and data curation processes.

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NIR Transient Surveys: Innovations and Challenges in Near-Infrared Astronomy

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  1. NIR Transient Surveys Nicholas Cross WFAU, Edinburgh Nigel Hambly, Mike Read, Ross Collins, EckhardSutorius, Rob Blake, Mark Holliman

  2. NIR Variability Science Drivers • NIR, smaller detectors, higher backgrounds and more expensive detectors than optical • Only do multi-epoch work where it is not practical for optical detectors • Looking through the dense dusty regions of the MW to the far side • Young Stellar Objects in star-forming regions • Low mass stars / brown dwarfs • High z galaxies / Snae • Can get better RR Lyrae / Cepheid distances in NIR IAU S285, Oxford

  3. NIR Variability Surveys • UKIRT WFCAM • UKIDSS – DXS/UDS (Deep surveys, multi-epoch), • WFCAM Transit Survey, • Calibration/Standard Stars, • Surveys of YSOs in Orion/Ophiuchus • VISTA • VISTA Variables in Via-Lactea (VVV), (RR Lyrae, Cepheids) • VISTA Magellanic Cloud (VMC), (RR Lyrae, Cepheids) • VIDEO (Deep Extragalactic – SNae) IAU S285, Oxford

  4. WFCAM • 3.8 m UKIRT telescope on Mauna Kea. • 4 2k x 2k Rockwell Hawaii 2 detectors. • Spaced 94% apart. • 0.4” pixels. • 13.65’ across each side. • 60% of time on UKIRT in 2005b • 100% for 2009a IAU S285, Oxford

  5. VIRCAM • 4.1m VISTA telescope at Cerro Paranel. • 16 2k x 2k Raytheon VIRGO detectors • Spaced 90% in x and 42.5% in y. • 0.34” pixels • Tile is 1.5° • VIRCAM has 100% of time. • > 3 times area WFCAM • 2 * QE IAU S285, Oxford

  6. VISTA Public Surveys IAU S285, Oxford

  7. VISTA Variables in Via-Lactea (VVV) • Very high density ~106 sources / sq. deg. • Issues with deblending • 500 sq. deg • ~100 epochs (currently ~10) • ~ few 1010 detections IAU S285, Oxford

  8. Processing of WFCAM and VISTA data • VDFS: VISTA Data Flow System (System for processing of UKIRT WFCAM and VISTA data. • CASU (Cambridge): Data reduction, processing of observing blocks, photometric and astrometric calibration • WFAU (Edinburgh): Archive, processing of multiple observing blocks – deep stacks, multi-band tables, links to external tables, MULTI-EPOCH. • For VISTA, data goes through ESO and final products go to ESO too. IAU S285, Oxford

  9. Constraints from VDFS • >=6 week time lag before data at WFAU • Data needs to be transferred to Cambridge (with VISTA this includes disk drive to Garching and then to Cambridge) • Accurate photometric calibration (including scattered light corrections uses 1 month of data. • VoEvent alerts are too late from WFAU • Reprocessing of OB data requires retransfer between CASU and WFAU and reingest of data at WFAU. • Detection tables are used by many curation processes – reingestion into these slows later stages. IAU S285, Oxford

  10. Stages of multi-epoch processing • Stack epochs to create deep images and extract catalogues • Create master list (Source table) from band-merged catalogues from deep images. • Recalibrate each epoch image compared to the deep image in that filter and pointing. • Create table linking sources to each observation • Calculate the noise properties of each pointing and filter • Calculate astrometric and photometric statistics for each source. IAU S285, Oxford

  11. Analysing Variables • Calculate mean, rms of magnitudes. • Bin in magnitude and calculate clipped median • Fit empirical noise model • (m)=a+b10-0.4m+c10-0.8m • Classify as variable or non-variable IAU S285, Oxford

  12. Archival Databases • Curation of WFCAM and VISTA data occurs in a RDBMS using Microsoft SQL Server. • Dynamic database, updated with new data, improved calibrations and reprocessed data when necessary. • Static releases to the science teams and world for science purposes. • Curation controlled by comparing current state of DB with requirements IAU S285, Oxford

  13. Programme Requirements • Pointing, filter and table requirements are setup by grouping the metadata and using specifications for each survey. • Schema updated if necessary • Stack / tile products made for a particular release number • Source table created for particular pointings • Each stage of multi-epoch processing checks the whether the previous table has changed in that pointing – higher curation event ID. IAU S285, Oxford

  14. VISTA tiles • Most surveys require tiles to reach expected depth, and tiles are standard ESO product. • PSF and sky vary on short time scales < integration time • Images filtered to remove large spatial variations (>30”) • Tile catalogues are inferior to pawprints: • Not as accurate astrometry • Do not deal with saturation correctly • Extended (>30”) sources are missing or have incorrect photometry • Catalogues from tiles and pawprints • Need to be able to compare – multiple layers and linking tables. IAU S285, Oxford

  15. Problems / bottlenecks /solutions • Reprocessing of OB data. • 1st year of VISTA – 2 sets of full reprocessing • Ingesting new data while curating later products • Put VVV on separate server and synchronise metadata tables • BUT foreign key constraints to vvvDetection cause major holdups if metadata is deleted. • Split vvvDetection into semesters / months so new data can be ingested into new semester. • Has not been implemented yet • Users want to use both tile and pawprint detections • Produce linking tables • BUT some queries that join these can join several tens of tables and SQL does not handle these joins well. • Enhancements to user interface allow users to save intermediate results IAU S285, Oxford

  16. Problems / bottlenecks /solutions • Checking non-detections of sources • Using half-space method of Budavari, major improvement • Dealing with very long processing times of VVV • Break curation into chunks with software testing to see what has already been done • Make sure memory never exceeds ~40% • BUT this adds additional overheads at beginning of each run • Variability table curation is dominated by DB reads (85% for VVV) • Use Query Analyser and other tools to optimise queries [OPTION (MAXDOP 1)], adding removing indexes. • Split detection tables into parts? • I/O limited between servers and disks • SQL Server “cluster” linked by infiniband 10Gbs-1 IAU S285, Oxford

  17. Other issues • Classification • DB has simple classification (variable or not) and some other statistical quantities. VVV will have ~106 variables • Chilean teams working on NIR templates for different types of variables • Trend analysis (IstvanDekany) • Accuracy • VSA/WSA, simple ZP recalibration – rms ~0.005mag • Good enough for most variables • Planetary Transits require (prefer) ~0.001 mag. • Confusion • Difference Imaging Analysis (EamonnKerins), will probably be applied to densest 40 sq. deg of VVV bulge. IAU S285, Oxford

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