Photometry on Structured Backgrounds: Local Pixel-wise Infilling by Regression
Abstract

Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixel-wise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty of that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey. In addition to removing many >3σoutliers and improving uncertainty estimates by a factor of ∼2–3 on nebulous fields, we also show that our method is well behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve more »

Authors:
;
Award ID(s):
Publication Date:
NSF-PAR ID:
10369014
Journal Name:
The Astrophysical Journal
Volume:
933
Issue:
2
Page Range or eLocation-ID:
Article No. 155
ISSN:
0004-637X
Publisher:
DOI PREFIX: 10.3847
National Science Foundation
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