An Application of Multi-band Forced Photometry to One Square Degree of SERVS: Accurate Photometric Redshifts and Implications for Future Science
Abstract
We apply The Tractor image modeling code to improve upon existing multi-band photometry for the Spitzer Extragalactic Representative Volume Survey (SERVS). SERVS consists of post-cryogenic Spitzer observations at 3.6 and 4.5 μm over five well-studied deep fields spanning 18 deg^2. In concert with data from ground-based near-infrared (NIR) and optical surveys, SERVS aims to provide a census of the properties of massive galaxies out to z ≈ 5. To accomplish this, we are using The Tractor to perform "forced photometry." This technique employs prior measurements of source positions and surface brightness profiles from a high-resolution fiducial band from the VISTA Deep Extragalactic Observations survey to model and fit the fluxes at lower-resolution bands. We discuss our implementation of The Tractor over a square-degree test region within the XMM Large Scale Structure field with deep imaging in 12 NIR/optical bands. Our new multi-band source catalogs offer a number of advantages over traditional position-matched catalogs, including (1) consistent source cross-identification between bands, (2) de-blending of sources that are clearly resolved in the fiducial band but blended in the lower resolution SERVS data, (3) a higher source detection fraction in each band, (4) a larger number of candidate galaxies in the redshift range 5 < z < 6, and (5) a statistically significant improvement in the photometric redshift accuracy as evidenced by the significant decrease in the fraction of outliers compared to spectroscopic redshifts. Thus, forced photometry using The Tractor offers a means of improving the accuracy of multi-band extragalactic surveys designed for galaxy evolution studies. We will extend our application of this technique to the full SERVS footprint in the future.
Additional Information
© 2017 The American Astronomical Society. Received 2016 December 30; revised 2017 April 4; accepted 2017 April 11; published 2017 May 24. We thank the referee for providing us with thoughtful comments that have significantly improved the quality of this work. We also thank Scott Ransom for assisting us with the implementation of parallelization in our Python driver script. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. G.W. acknowledges financial support for this work from NSF grant AST-1517863 and from NASA through programs GO-13306, GO-13677, GO-13747, & GO-13845/14327 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555. M.V. acknowledges support from the European Commission Research Executive Agency (FP7-SPACE-2013-1 GA 607254), the South African Department of Science and Technology (DST/CON 0134/2014), and the Italian Ministry for Foreign Affairs and International Cooperation (PGR GA ZA14GR02). This work is based on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Support for this work was provided by the grant associated with Spitzer proposal 11086. Our analysis includes observations obtained with the MegaPrime/MegaCam instrument, a joint project of CFHT and CEA/IRFU, at the Canada–France–Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This study is also based in part on data products produced at Terapix available at the Canadian Astronomy Data Centre as part of the Canada–France–Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. We additionally utilized data from the VIMOS VLT Deep Survey, obtained from the VVDS database operated by Cesam, Laboratoire d'Astrophysique de Marseille, France. The authors have made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013). We also used Montage, which is funded by the National Science Foundation under grant No. ACI-1440620, and was previously funded by the National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Institute of Technology. Facilities: Spitzer (IRAC), VISTA, CFHT.Attached Files
Published - Nyland_2017_ApJS_230_9.pdf
Submitted - 1704.01582.pdf
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Additional details
- Eprint ID
- 77715
- Resolver ID
- CaltechAUTHORS:20170524-124220253
- AST-1517863
- NSF
- GO-13306
- NASA
- GO-13677
- NASA
- GO-13747
- NASA
- GO-13845/14327
- NASA
- NAS 5-26555
- NASA
- GA 607254
- European Research Council (ERC)
- DST/CON 0134/2014
- Department of Science and Technology (South Africa)
- PGR GA ZA14GR02
- Ministero Affari Esteri e della Cooperazione internazionale (MAECI)
- NASA/JPL/Caltech
- 11086
- NASA
- ACI-1440620
- NSF
- NCC5-626
- NASA
- Created
-
2017-05-24Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field
- Caltech groups
- Infrared Processing and Analysis Center (IPAC)