用神经网络实现天文图像中变化背景与星点模糊的高效概率建模。
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
- 基于合成数据训练神经网络，输入图像及局部背景/点扩散函数信息，输出概率星表。
- 在斯隆巡天数据上实现光源检测、星系分离与流量测量，精度显著优于传统方法。
- 适合处理地面望远镜图像中空间变化的背景和模糊效应，可推广至其他巡天项目。
神经后验估计（NPE）是一种高效的近似变分推断方法，可用于从天文图像中构建光源的概率星表。目前，NPE尚未应用于具有空间变化协变量的模型。然而，地面天文图像存在空间变化的天空背景和点扩散函数（PSF），准确建模这些变化对构建精确星表至关重要。本文提出一种在空间变化背景和PSF条件下进行NPE的方法：通过现有巡天中随机采样的PSF和背景估计生成合成星表和半合成图像，并利用这些数据训练神经网络，使其以天文图像及背景/PSF表示为输入，输出概率星表。在斯隆数字巡天（SDSS）数据上的实验表明，该方法在光源检测、星/星系分离和通量测量任务中均表现优异，验证了其在空间变化条件下的有效性。
原文摘要 · Abstract (English)
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a method of performing NPE with spatially varying backgrounds and PSFs. In this method, we generate synthetic catalogs and semi-synthetic images for these catalogs using randomly sampled PSF and background estimates from existing surveys. Using this data, we train a neural network, which takes an astronomical image and representations of its background and PSF as input, to output a probabilistic catalog. Our experiments with Sloan Digital Sky Survey data demonstrate the effectiveness of NPE in the presence of spatially varying backgrounds and PSFs for light source detection, star/galaxy separation, and flux measurement.
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