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Abstract:
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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 to improve estimates of flux and flux uncertainty. We validate our model on both synthetic and real data, even in the crowded field limit. While we focus on optical-IR photometry, the method is not restricted to those wavelengths. We apply LPI to the 34 billion detections in the Dark Energy Camera Plane Survey. In addition to removing many >3-sigma outliers and improving uncertainty estimates by 2.5 on nebulous fields, we also show that LPI 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.
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