Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression

TL;DR

This paper introduces local pixelwise infilling (LPI), a regression-based method that improves star flux and uncertainty estimates in complex backgrounds, validated on synthetic, real data, and applied to the DECaPS2 survey.

Contribution

We develop LPI, a novel local covariance-based regression technique for better photometry in structured backgrounds, applicable to existing and future surveys.

Findings

Reduces outliers in nebulous fields by >3σ

Improves flux uncertainty estimates by 2-3 times

Stable in crowded and uncrowded fields

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 pixelwise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty on 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 (DECaPS2). In addition to removing many $>3\sigma$ outliers and improving uncertainty estimates by a factor of $\sim 2-3$ 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 the flux and flux uncertainty estimates of past as well as future surveys.