Title: Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production Show Chinese title

Title: 红移评估基础设施层（RAIL）：鲁宾时代光度红移压力测试及大规模生产

Abstract: 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. Show Chinese abstract

Abstract: 几乎所有的地外使用案例都需要利用薇拉·C·鲁宾天文台的时空遗产巡天（LSST）中的星系红移信息，然而它所包含的数十亿个星系样本中绝大多数将缺乏高保真光谱测量数据，转而依赖于受到系统性偏差和不准确影响的测光红移（photo-$z$），这些偏差和不准确最佳地通过测光红移（photo-$z$）概率密度函数（PDFs）来体现。 我们发布了Redshift Assessment Infrastructure Layers（RAIL）版本1，这是一个开源的Python库，用于大规模概率测光红移（photo-$z$）估计，由LSST暗能量科学合作组织（DESC）发起，并得到了LSST跨学科协作与计算网络（LINCC）框架团队的贡献。 RAIL的三个子包提供了模块化工具，用于端到端的压力测试，包括一个前向建模套件，用于生成真实复杂的测光数据；一个统一的API，通过一组可扩展的算法来估算每个星系和星系群的红移PDFs；以及内置的针对测光红移（photo-$z$）PDFs和点估计的度量标准。 RAIL作为一个灵活的工具包，能够大规模推导和优化测光红移（photo-$z$）数据产品，适用于各种科学目标，不仅限于LSST数据。 因此，我们向地外科学界，包括但不限于鲁宾天文台的研究者们描述RAIL软件库的设计和功能，以便任何研究者都能访问其广泛的测光红移（photo-$z$）特征和评估工具。