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Galaxies in the UKIDSS Large Area Survey

Explore the UK Infrared Deep Sky Survey discussing near-IR luminosity functions, photometric redshifts, physical interpretation of near-IR colors, and future prospects like large-scale clustering and galaxy properties evolution. Details on UKIDSS surveys, progress, luminosity function analysis, and photometric redshifts are provided with insights into improving accuracy and reducing outliers in redshift predictions with near-IR photometry.

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Galaxies in the UKIDSS Large Area Survey

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  1. Galaxies in the UKIDSS Large Area Survey Jon Loveday Anthony Smith Celine Eminian University of Sussex

  2. Outline • UK Infrared Deep Sky Survey overview and status • Near-IR luminosity function • Photometric redshifts • Physical Interpretation of near-IR Colours • Conclusions/future prospects

  3. Goals • Large-scale clustering to z ~ 0.6 (BAO, neutrino mass) • Evolution of galaxy properties (LF, SFR) and clustering since z ~ 0.6 • Try out techniques on real data before future surveys such as DES, PanSTARRS, LSST etcbegin

  4. UKIDSS • UK Infrared Deep Sky Survey • UKIRT 3.8m telescope plus WFCAM (4x20482 Hawaii-II arrays, 0.21 deg2) • Étendue of 2.38 m2 deg2 largest of any IR camera until VISTA • zYJHK (1 ~ 2.5 ) near-IR filters • 5 surveys, 3 extragalactic • Significantly deeper than 2MASS

  5. UKIDSS • Observing started May 2005 • 7 year observing plan (~50% of UKIRT time) • Pipeline processing in Cambridge, archive in Edinburgh • No consortium proprietary data period • Data immediately available to ESO members once verified • Rest of world 18 months later

  6. UKIDSS Surveys

  7. UKIDSS survey progress

  8. Near-IR Luminosity Function(Smith, Cross, Loveday, in prep) • UKIDSS-LAS DR2 K-band photometry + SDSS DR5 redshifts • Need to allow for selection effects in • r-band flux (SDSS spectro limit) • K-band flux (UKIDSS completeness limit) • UKIDSS angular size • Surface brightness

  9. LAS: K<16 Vega (17.9 AB)

  10. SDSS: 5740 deg2453,349 galaxies with redshifts LAS-K: 476 deg2

  11. 19,105 galaxies to K=16 over 195 deg2 (400,000 over 4000 deg2by end of 2009)

  12. z = 0.01 z = 0.3 Too bright r’-Petrosian magnitude Too faint Too bright K-Petrosian magnitude Too faint Too concentrated K-surface brightness Too diffuse Too large K-radius Too small Vmax (Mr’, MK, K, RK) Multivariate : 1/Vmax method 16,452 galaxies within selection limits

  13. (Bivariate Brightness Distribution) K-band BBD (1/Vmax)

  14. K-band BBD (SWML)

  15. Red core (u-r) > 2.35 (SWML)

  16. Blue core (u-r) < 2.35 (SWML)

  17. K-band luminosity function

  18. LF Summary • UKIDSS K-band LF broadly consistent with previous results • Some discrepancies between 1/Vmax and SWML estimates • Low-luminosity discrepancy partly due to large-scale structure? • UKIDSS will be competitive with 2MASS in terms of volume/galaxy numbers with DR3 onwards (expected December 2007) • Extend analysis to DXS, UDS and VISTA surveys with photo-z to probe evolution

  19. Photometric RedshiftsCeline Eminian • Use SDSS ugriz and UKIDSS-LAS YJHK magnitudes in ANNz (Collister & Lahav 2004) • Network architecture 5:10:10:1 (5 bands) or 9:12:12:1 (9 bands) • Committee of five networks • For each sample, use SDSS spectroscopy: • 3/8 for training • 1/8 for verification • 1/2 for testing (numbers shown on plots)

  20. SDSS Main

  21. SDSS Main + UKIDSS

  22. SDSS Main + LRGs

  23. SDSS Main + LRGs +UKIDSS

  24. SDSS Main Adding near-IR photometry helps to reduce outliers

  25. Photo-z Summary • At low redshifts (z ≤ 0.6) addition of near-IR photometry helps to improve errors by reducing outliers • Lack of improvement for LRGs with UKIDSS data due to • Small training set cf. network size? • Uniformity of LRG SED? • Severe lack of spectroscopic training data for ordinary galaxies at redshifts between ~0.2 and 1 • Cannot use LRG-trained network to predict redshifts of non-LRGs • AAOmega service proposal in queue to obtain spectroscopic redshifts of wide range of galaxies out to z = 0.6 from coadded data in SDSS southern stripe

  26. Physical Interpretation of near-IR Colours • Eminian et al, 2007, MNRAS in press • Compare 3-arcsec aperture photometry from SDSS and UKIDSS-LAS with physical galaxy properties deduced from SDSS spectra (SDSS-MPA database; Brinchmann et al 2004) and with stellar population synthesis models • Pair matching technique to remove correlations with mass, redshift and concentration

  27. Increasing star-formation rate correlates with bluer optical colours but redder near-IR colours • Due to dominance of TP-AGB stars in HK bands (Marraston 2005) • These stars also responsible for correlation of HK with dust?

  28. Comparsion with BC03 Stars: constant SFR; Squares  = 3Gyr; Ages 5, 10, 15 Gyr bot-top

  29. Comparsion with CB07 (prelim)

  30. Conclusions/Future Prospects • Goal is to measure evolution in stellar mass and clustering of a wide range of galaxy masses to z ~ 0.6 • Well-calibrated photometric redshifts of representative galaxies will be vital to do this • UKIDSS DR3 (December 2007) will probe volume competitive with 2MASS and provide far cleaner window function for clustering statistics • Immediate goal: how well can large-scale clustering be measured using photo-z (eg. w() in photo-z slices) compared with using spectroscopic redshifts? • Techniques can then be applied to UKIDSS DXS & UDS, VISTA …

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