论文标题
在结构化背景上的光度法：通过回归进行局部pixelwise填充
Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
论文作者
论文摘要
在存在结构化背景（例如细丝或云）的情况下，光度计管道难以估算恒星的通量和通量不确定性。但是，在这些复杂区域中，恒星对于理解恒星形成和星际介质的结构至关重要。我们开发了一种类似于高斯过程回归的方法，我们将其称为局部像素填充（LPI）。使用局部协方差估计，我们预测了每个恒星背后的背景以及该预测的不确定性，以提高通量和通量不确定性的估计值。我们显示了模型对合成数据和真实尘埃场的有效性。我们进一步证明该方法即使在拥挤的现场限制中也是稳定的。虽然我们专注于光学光度法，但该方法不仅限于这些波长。我们将此技术应用于暗能量摄像机飞机调查（DECAPS2）的第二个数据发布中的340亿个检测。除了删除许多$>3σ$异常值，并在模糊田地将不确定性估计提高了$ \ sim 2-3 $外，我们还表明我们的方法在不受欢迎的领域中表现得很好。我们实施LPI光度法的完全后处理性质使其可以轻松地改善过去和未来调查的通量和通量不确定性估计。
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.
