Deep point spread function photometric catalog of the VVV survey data
Journal article, 2019
Aims. We aim at producing a deep, point spread function (PSF) photometric catalog for the VVV survey J-,H-, and K-s-band data. Specifically, we aim to take advantage of multiple epochs of the survey to reach high limiting magnitudes.
Methods. We developed an automatic PSF-fitting pipeline based on the DaoPHOT algorithm and performed photometry on the stacked VVV images in J,H, and K-s bands.
Results. We present a PSF photometric catalog in the Vega system that contains about 926 million sources in the J,H, and K-s filters. About 10% of the sources are flagged as possible spurious detections. The 5 sigma limiting magnitudes of the sources with high reliability are about 20.8, 19.5, and 18.7 mag in the J,H, and K-s bands, respectively, depending on the local crowding condition. Our photometric catalog reaches on average about one magnitude deeper than the previously released PSF DoPHOT photometric catalog and includes less spurious detections. There are significant differences in the brightnesses of faint sources between our catalog and the previously released one. The likely origin of these differences is in the different photometric algorithms that are used; it is not straightforward to assess which catalog is more accurate in different situations. Our new catalog is beneficial especially for science goals that require high limiting magnitudes; our catalog reaches such high magnitudes in fields that have a relatively uniform source number density. Overall, the limiting magnitudes and completeness are different in fields with different crowding conditions.
techniques
surveys
catalogs
infrared
stars
photometric
Author
M. Zhang
Chinese Academy of Sciences
Max Planck Society
Jouni Kainulainen
Max Planck Society
Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics
Astronomy and Astrophysics
0004-6361 (ISSN) 1432-0746 (eISSN)
Vol. 632 A85Subject Categories
Astronomy, Astrophysics and Cosmology
Signal Processing
Medical Image Processing
DOI
10.1051/0004-6361/201935513