结构化背景下的Photometry：回归局部像素填充
Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
摘要：
光度管道很难估计恒星在结构化背景（如灯丝或云）存在下的通量和通量不确定性。然而，正是这些复杂区域中的恒星对于理解恒星形成和星际介质的结构至关重要。我们开发了一种方法，类似于高斯过程回归，我们称之为局部像素填充（LPI）。使用局部协方差估计，我们预测每个恒星背后的背景和预测的不确定性，以提高通量和通量不确定性的估计。我们证明了我们的模型的有效性合成数据和真实的尘埃场。我们进一步证明了该方法是稳定的，即使在拥挤的领域限制。虽然我们专注于光学红外测光，但这种方法并不限于这些波长。我们将这种技术应用于暗能量相机平面调查（DECaPS2）第二次数据发布中的340亿次检测。除了删除许多$>3σ$离群值和改善不确定性估计的一个因素，$\sim 2-3$的朦胧领域，我们还表明，我们的方法是良好的表现在不拥挤的领域。我们实施LPI测光的完全后处理性质使其能够轻松地改善过去和未来调查的通量和通量不确定性估计。
英文摘要：
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 $>3σ$ 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.
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