Photometric Redshifts with the LSST II: The Impact of Near-Infrared and Near-Ultraviolet Photometry
Melissa L. Graham, Andrew J. Connolly, Winnie Wang, Samuel J. Schmidt, Christopher B. Morrison, Željko Ivezić, Sébastien Fabbro, Patrick Côté, Scott F. Daniel, … 展开作者
Melissa L. Graham, Andrew J. Connolly, Winnie Wang, Samuel J. Schmidt, Christopher B. Morrison, Željko Ivezić, Sébastien Fabbro, Patrick Côté, Scott F. Daniel, R. Lynne Jones, Mario Jurić, Peter Yoachim, J. Bryce Kalmbach
精确的测光红移（照片- $z$）估计是必不可少的宇宙科学目标的维拉C。Rubin Observatory Legacy Survey of Space and Time（鲁宾天文台时空遗产巡天）在这项工作中，我们使用模拟的光度模拟星系目录，探讨如何LSST照片$z$估计可以提高近红外（NIR）和/或紫外线（UV）光度从欧几里得，WFIRST，和/或卡斯特空间望远镜。一般来说，我们发现，更深的光学测光可以减少光$z$估计的标准偏差比增加近红外或紫外线过滤器，但额外的过滤器是唯一的方法，以显着降低星系的比例与灾难性的低估或高估的光$z$。对于欧几里德，我们发现，${JH}$ $5σ$光度检测的增加可以减少标准偏差的星系与$z>1$（$z>0.3$）的${\sim}20\%$（${\sim}10\%$），和异常值的分数由${\sim}40\%$（${\sim}25\%$）。对于WFIRST，我们展示了如何添加深度${YJHK}$光度测量可以在$z>1.5$处将标准差降低${\gtrsim}50\%$，并将离群值的比例大幅降低到整体${\sim}2\%$。对于CASTOR，我们发现加入它的${UV}$和$u$带测光可以使具有$z<0.5$的星系的标准差减少${\sim}30\%$，离群值减少${\sim}50\%$。我们还评估了与NIR和UV调查重叠的天空区域内的照片- $z$结果，以及使用从调查的小区域深场构建的光谱训练集时的结果。
英文摘要：
Accurate photometric redshift (photo-$z$) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work we use simulated photometry for mock galaxy catalogs to explore how LSST photo-$z$ estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, WFIRST, and/or CASTOR space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-$z$ estimates more than adding NIR or UV filters, but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or over-estimated photo-$z$. For Euclid, we find that the addition of ${JH}$ $5σ$ photometric detections can reduce the standard deviation for galaxies with $z>1$ ($z>0.3$) by ${\sim}20\%$ (${\sim}10\%$), and the fraction of outliers by ${\sim}40\%$ (${\sim}25\%$). For WFIRST, we show how the addition of deep ${YJHK}$ photometry could reduce the standard deviation by ${\gtrsim}50\%$ at $z>1.5$ and drastically reduce the fraction of outliers to just ${\sim}2\%$ overall. For CASTOR, we find that the addition of its ${UV}$ and $u$-band photometry could reduce the standard deviation by ${\sim}30\%$ and the fraction of outliers by ${\sim}50\%$ for galaxies with $z<0.5$. We also evaluate the photo-$z$ results within sky areas that overlap with both the NIR and UV surveys, and when spectroscopic training sets built from the surveys' small-area deep fields are used.
Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies
University of Washington（华盛顿大学）；Imperial College London（帝国理工学院）；Rutgers, the State University of New Jersey（新泽西州立罗格斯大学）；University of Toronto（多伦多大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；Université Paris Cité（巴黎西岱大学）
Sub-percent Photometry: Faint DA White Dwarf Spectophotometric Standards for Astrophysical Observatories
我们已经建立了一个网络的19个微弱的（16.5等$< V < $ 19等）北赤道DA白矮星作为分光光度标准，为现在和未来的宽视场天文台。我们的分析推断SED模型的恒星是绑在三个CALSPEC的主要标准。我们的SED模型与全色哈勃空间望远镜（$HST$）测光的一致性优于1%。观测和模型之间的良好的协议验证了使用非本地平衡（NLTE）DA白矮星大气熄灭的星际尘埃作为准确的分光光度参考。我们的标准可从两个半球获得，适用于地面和空间观测站，覆盖紫外线到近红外线。这些微弱源的高精度使我们的标准网络非常适合任何对通量校准有非常严格要求的实验，例如使用大型综合巡天望远镜（LSST）和宽视场红外巡天望远镜（$WFIRST$）进行暗能量研究。
ULTIMATE-deblending I. A 50-band Ultraviolet to Mid-infrared Photometric Catalog Combining Space- and Ground-based Data in the JWST/PRIMER survey
School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）；Key Laboratory of Modern Astronomy and Astrophysics, Nanjing University, Ministry of Education（南京大学现代天文与天体物理教育部重点实验室）；Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM（巴黎萨克雷大学、巴黎西岱大学、法国原子能和替代能源委员会、法国国家科学研究中心、天体物理学信息与方法研究所）；INAF – Osservatorio Astronomico di Roma（意大利国家天体物理研究所罗马天文台）；Chinese Academy of Sciences South America Center for Astronomy, National Astronomical Observatories, CAS（中国科学院南美天文中心、中国科学院国家天文台）
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
Ruhr University Bochum（鲁尔大学波鸿）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Stanford University（卡弗里粒子天体物理与宇宙学研究所，斯坦福大学）；Université Clermont-Auvergne, CNRS, LPCA（克莱蒙奥弗涅大学，法国国家科学研究中心，LPCA）；McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University（梅斯威尔宇宙学与天体物理中心，卡内基梅隆大学物理系）
RAIL 是一个开源 Python 库，用于大规模概率光度红移估计，提供模块化工具进行端到端压力测试和红移 PDF 估算，适用于多种科学目标。
