Aims: We present a custom support vector machine classification package for photometric redshift estimation, including comparisons with other methods. We also explore the efficacy of including galaxy shape information in redshift estimation. Support vector machines, a type of machine learning, utilize optimization theory and supervised learning algorithms to construct predictive models based on the information content of data in a way that can treat different input features symmetrically.
Methods: The custom support vector machine package we have developed is designated SPIDERz and made available to the community. As test data for evaluating performance and comparison with other methods, we apply SPIDERz to four distinct data sets: 1) the publicly available portion of the PHAT-1 catalog based on the GOODS-N field with spectroscopic redshifts in the range $z < 3.6$, 2) 14365 galaxies from the COSMOS bright survey with photometric band magnitudes, morphology, and spectroscopic redshifts inside $z < 1.4$, 3) 3048 galaxies from the overlap of COSMOS photometry and morphology with 3D-HST spectroscopy extending to $z < 3.9$, and 4) 2612 galaxies with five-band photometric magnitudes and morphology from the All-wavelength Extended Groth Strip International Survey and $z < 1.57$.
Results: We find that SPIDER-z achieves results competitive with other empirical packages on the PHAT-1 data, and performs quite well in estimating redshifts with the COSMOS and AEGIS data, including in the cases of a large redshift range ($0 < z < 3.9$). We also determine from analyses with both the COSMOS and AEGIS data that the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here.
An optimised survey strategy for the ERIS/NIX imager: searching for young giant exoplanets and very low mass brown dwarfs using the K-peak custom photometric filter
Photometric Redshift Calibration with Self Organising Maps
精确的测光红移校准是从宇宙剪切调查的所有宇宙学约束的鲁棒性的核心。对KiDS的分析重新加权了来自所有重叠光谱调查的训练样本，以提供直接的红移校准。使用自组织映射（SOM），我们证明了这种光谱编译是足够完整的KiDS，代表$99\%$的有效的二维宇宙剪切样本。我们使用SOM来定义一个$100\%$代表“金”宇宙剪切样本，每个层析箱。使用模拟模拟KiDS和光谱训练集，我们估计的不确定性的SOM红移校准，并发现光度噪声，样本方差，光谱选择效应（包括红移和幅度不完整性）诱导合并的最大散射的红移分布重建（$Δ\langle z \rangle=\langle z \rangle_{\rm est}-\langle z \rangle_{\rm true}$）的偏差$σ_{Δ\langle z \rangle} \leq 0.006$在所有的层析箱。我们表明，SOM校准是无偏的情况下，无噪声测光和完全代表性的光谱数据集，从理论上预期。在我们的模拟数据中包括光度噪声和光谱选择效应引入了最大偏差$Δ\langle z \rangle =0.013\pm0.006$，或$97.5\%$置信度下的$Δ\langle z \rangle \leq 0.025$，一旦质量标志已应用于SOM。这里提出的方法是一个显着的改进，以前采用的直接红移校准实施KiDS，由于其诊断和质量保证能力。在未来的宇宙剪切研究中…
JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: Photometric Catalog
Department of Astronomy and Astrophysics, University of California, Santa Cruz（加州大学圣克鲁兹分校天体物理学与天文学系）；Kavli Institute for Cosmology, University of Cambridge（剑桥大学卡弗里宇宙学研究所）；Cavendish Laboratory, University of Cambridge（剑桥大学卡文迪许实验室）；Steward Observatory, University of Arizona（亚利桑那大学斯图德天文台）；AURA for the European Space Agency (ESA), Space Telescope Science Institute（欧洲航天局（ESA）所属美国大学研究协会（AURA）太空望远镜科学研究所）；Centro de Astrobiología (CAB), CSIC–INTA（西班牙国家航空航天技术研究所（INTA）/西班牙国家科学研究委员会（CSIC）天体生物学中心）
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）
