标题： 面向时空遗产调查的天文学图像目录的神经后验估计 显示英文标题

标题： Neural Posterior Estimation for Cataloging Astronomical Images from the Legacy Survey of Space and Time

摘要： 薇拉·C·鲁宾天文台空间和时间遗产调查（LSST）将在2026年开始全面运行，产生前所未有的天文学图像量。 构建一个天文学目录，即一张包含成像恒星、星系及其属性的表格，是大多数基于天文学图像数据的科学工作流程中的基本步骤。 传统的确定性目录编制方法在目录编制是一个不适定问题时缺乏统计一致性，而现有的概率方法则存在计算效率低下、不准确或无法对多波段叠加图像进行推理的问题，多波段叠加图像是LSST图像的主要输出格式。 在本文中，我们探讨了一种最近开发的贝叶斯推断方法，称为神经后验估计（NPE），作为目录编制的一种方法。 NPE利用深度学习实现计算效率和高准确性。 在DC2模拟天空调查——一个设计用来模仿LSST数据的高度真实的合成数据集——上评估时，NPE在光源检测、流量测量、星系/恒星分类和星系形状测量方面系统地优于标准的LSST流水线。 此外，NPE提供了校准良好的后验近似值。 这些使用模拟数据获得的有希望的结果，说明了在没有模型误指的情况下NPE的潜力。 尽管在将NPE应用于真实LSST图像时，一定程度的模型误指是不可避免的，但有许多策略可以减轻其影响。 显示英文摘要

摘要： The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will commence full-scale operations in 2026, yielding an unprecedented volume of astronomical images. Constructing an astronomical catalog, a table of imaged stars, galaxies, and their properties, is a fundamental step in most scientific workflows based on astronomical image data. Traditional deterministic cataloging methods lack statistical coherence as cataloging is an ill-posed problem, while existing probabilistic approaches suffer from computational inefficiency, inaccuracy, or the inability to perform inference with multiband coadded images, the primary output format for LSST images. In this article, we explore a recently developed Bayesian inference method called neural posterior estimation (NPE) as an approach to cataloging. NPE leverages deep learning to achieve both computational efficiency and high accuracy. When evaluated on the DC2 Simulated Sky Survey -- a highly realistic synthetic dataset designed to mimic LSST data -- NPE systematically outperforms the standard LSST pipeline in light source detection, flux measurement, star/galaxy classification, and galaxy shape measurement. Additionally, NPE provides well-calibrated posterior approximations. These promising results, obtained using simulated data, illustrate the potential of NPE in the absence of model misspecification. Although some degree of model misspecification is inevitable in the application of NPE to real LSST images, there are a variety of strategies to mitigate its effects.