发表机构
Max-Planck-Institut für extraterrestrische Physik; Exzellenzcluster ORIGINS; Université Clermont-Auvergne; Aix-Marseille Université; Université Paris-Saclay; Université Paris-Cité; Núcleo de Astronomía de la Facultad de Ingeniería, Universidad Diego Portales; School of Physics and Astronomy, University of Southampton; INAF-Osservatorio Astronomico di Roma; Institute for Gravitation and the Cosmos, The Pennsylvania State University（马克斯·普朗克地外物理学研究所; ORIGINS卓越集群; 克莱蒙奥弗涅大学; 艾克斯-马赛大学; 巴黎萨克雷大学; 巴黎 Cité 大学; 迭戈帕拉莱斯大学工程学院天文学中心; 南安普顿大学物理与天文学院; 罗马国家天体物理研究所天文台; 宾夕法尼亚州立大学引力与宇宙学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对Vera C. Rubin天文台的LSST巡天，用LePHARE分析AGN测光红移的影响因素，提供AGN处理配置及COSMOS天区AGN测光红移数据，优化AGN测光红移计算。
AI 中文摘要
活动星系核（AGN）在星系演化中发挥关键作用，但它们是河外源中的少数群体，具有多样的光谱能量分布（SED），其SED取决于选择方式。即将到来的LSST等大规模巡天将识别大量AGN，但分析工具并未针对AGN优化。这些巡天中测光波段数量有限，会影响AGN测光红移的计算，而测光红移对科学进展至关重要。我们使用LePHARE展示了有限波段数量和错误假设对AGN测光红移确定的影响。我们在COSMOS天区对6类通过X射线、射电、红外、光变、色指数和光谱标准选择的AGN样本进行测试，使用HSC-CLAUDS的测光数据，其深度和波长覆盖最接近LSST。我们在红移评估基础设施层（RAIL）中为LePHARE提供LSST管道，便于SED拟合与机器学习算法的比较。若使用星系模板处理光学数据中呈点状的AGN，将得到极不可靠的测光红移。此外，全天浅巡天（如eROSITA、WISE和ZTF）会遗漏大量AGN，这些“隐藏”的AGN常被公共巡天数据误判为星系，导致测光红移错误。我们针对每类AGN提供建议配置，以及随红移、星等和选择条件变化的预期性能指标。为便于AGN的全波段研究，我们还发布了基于28波段测光、通过6类标准在COSMOS天区识别的所有AGN源的测光红移和后验分布。
英文摘要
Active Galactic Nuclei (AGN) play a crucial role in galaxy evolution, but they are a minority of extragalactic sources with diverse Spectral Energy Distributions (SEDs), which depend on their means of selection. Upcoming large-scale surveys such as LSST will identify many AGN, but analysis tools are not optimized for them. The limited number of photometric bands in these surveys impacts the calculation of photometric redshifts for AGN, which are essential for scientific advancement. We use LePHARE to demonstrate the impact that a limited number of bands and erroneous assumptions have on the determination of the photometric redshifts of AGN. We conduct tests on six AGN samples selected using X-ray, radio, infrared, variability, color, and spectroscopic criteria in the COSMOS field, using photometry from HSC-CLAUDS, which is closest in depth and wavelength coverage to LSST. We present the LSST pipeline for LePHARE within the Redshift Assessment Infrastructure Layers (RAIL), facilitating comparison between SED fitting and machine learning algorithms. AGN that appear as point-like sources in optical data will be assigned highly unreliable photometric redshifts if they are processed using galaxy templates. Additionally, shallow all-sky surveys (like eROSITA, WISE, and ZTF) miss many AGN. As a result, these "hidden" AGN are often misidentified as galaxies in public survey data, leading to incorrect photometric redshift. We provide the configurations that are suggested for each type of AGN alongside measures of expected performance as a function of redshift, magnitude, and selection. To facilitate studies with a panchromatic view of AGN, we also release photometric redshifts and posterior distributions for all AGN sources identified in the COSMOS field using the six criteria, based on 28-band photometry.
