标题： 红移评估基础设施层（RAIL）：鲁宾时代光度红移的压力测试和大规模生产 显示英文标题

标题： Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

摘要： 几乎所有的银河系外应用都需要使用韦拉·C·鲁宾天文台的时空遗产调查（LSST）的星系红移信息，但其数十亿个星系样本的大部分将缺乏高保真光谱测量，而是依赖于光度红移（photo-$z$），这些红移受系统不精确和不准确的影响，最好由photo-$z$概率密度函数（PDFs）来概括。 我们介绍了Redshift Assessment Infrastructure Layers（RAIL）的版本1，这是一个开源的Python库，用于大规模的概率光度红移（photo-$z$）估计，由LSST暗能量科学合作组织（DESC）发起，并得到了LSST跨学科网络合作与计算（LINCC）框架团队的贡献。 RAIL的三个子包提供了端到端压力测试的模块化工具，包括一个生成现实复杂光度的正向建模套件，一个统一的API，用于通过可扩展的一组算法估计每个星系和集合的红移PDF，以及内置的光度红移（photo-$z$）PDF和点估计的指标。 RAIL作为一个灵活的工具包，使研究人员能够在大规模上推导和优化光度红移（photo-$z$）数据产品，适用于各种科学目标，并不限于LSST数据。 因此，我们向银河系外科学界，包括但不限于鲁宾天文台，描述RAIL软件库的设计和功能，以便任何研究人员都可以访问其广泛的光度红移（photo-$z$）表征和评估工具。 显示英文摘要

摘要： Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.