SRGz: Machine Learning Methods and Properties of the Catalog of SRG/eROSITA Point X-ray Source Optical Counterparts in the DESI Legacy Imaging Surveys Footprint

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
A. V. Meshcheryakov, V. D. Borisov, G. A. Khorunzhev, P. A. Medvedev, M. R. Gilfanov, M. I. Belvedersky, S. Yu. Sazonov, R. A. Burenin, R. A. Krivonos, I. F. Bikmaev, I. M. Khamitov, S. V. Gerasimov, I. V. Mashechkin, R. A. Sunyaev
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引用次数: 0

Abstract

We describe the methods of the SRGz system for the physical identification of eROSITA point X-ray sources from photometric data in the DESI Legacy Imaging Surveys footprint. We consider the models included in the SRGz system (version 2.1) that have allowed us to obtain accurate measurements of the cosmological redshift and class of an X-ray object (quasar/galaxy/star) from multiwavelength photometric sky surveys (DESI LIS, SDSS, Pan-STARRS, WISE, eROSITA) for 87\({\%}\) of the entire eastern extragalactic region (\(0^{\circ}<l<180^{\circ}\), \(|b|>20^{\circ}\)). An important feature of the SRGz system is that its data handling model (identification, classification, photo-z algorithms) is based entirely on heuristic machine learning approaches. For a standard choice of SRGz parameters the optical counterpart identification completeness (recall) in the DESI LIS footprint is \(95{\%}\) (with an optical counterpart selection precision of \(94{\%}\)); the classification completeness (recall) of X-ray sources without optical counterparts in DESI LIS is \(82{\%}\) (\(85{\%}\) precision). A high quality of the photometric classification of X-ray source optical counterparts is achieved in SRGz: \({>}99{\%}\) photometric classification completeness (recall) for extragalactic objects (a quasar or a galaxy) and stars on a test sample of sources with SDSS spectra and GAIA astrometric stars. We present an analysis of the importance of various photometric features for the optical identification and classification of eROSITA X-ray sources. We have shown that the infrared (IR) magnitude \(W_{2}\), the X-ray/optical(IR) ratios, the optical colors (for example, \((g-r)\)), and the IR color (\(W_{1}-W_{2}\)) as well as the color distances introduced by us play a significant role in separating the classes of X-ray objects. We use the most important photometric features to interpret the SRGz predictions in this paper. The accuracy of the SRGz photometric redshifts (from DESI LIS, SDSS, Pan-STARRS, and WISE photometric data) has been tested in the Stripe82X field on a sample of 3/4 of the optical counterparts of eROSITA point X-ray sources (for which spectroscopic measurements are available in Stripe82X): \(\sigma_{NMAD}=3.1{\%}\) (the normalized median absolute deviation of the prediction) and \(n_{>0.15}=7.8{\%}\) (the fraction of catastrophic outliers). The presented photo-z results for eROSITA X-ray sources in the Stripe82X field are more than a factor of 2 better in both metrics (\(\sigma_{NMAD}\) and \(n_{>0.15}\)) than the photo-z results of other groups published in the Stripe82X catalog.

Abstract Image

SRGz: DESI遗留成像调查足迹中SRG/eROSITA点x射线源光学对应目录的机器学习方法和特性
我们描述了SRGz系统从DESI遗留成像调查足迹的光度数据中物理识别eROSITA点x射线源的方法。我们考虑了SRGz系统(2.1版本)中包含的模型,这些模型使我们能够从多波长光度天空调查(DESI LIS, SDSS, Pan-STARRS, WISE, eROSITA)中获得整个东部河外区域(\(0^{\circ}<l<180^{\circ}\), \(|b|>20^{\circ}\))的87 \({\%}\)的宇宙学红移和x射线物体(类星体/星系/恒星)的精确测量值。SRGz系统的一个重要特征是其数据处理模型(识别、分类、photo-z算法)完全基于启发式机器学习方法。对于SRGz参数的标准选择,DESI LIS足迹中的光学对等物识别完整性(召回率)为\(95{\%}\)(光学对等物选择精度为\(94{\%}\));DESI LIS中无光学对应的x射线源分类完备性(召回率)为\(82{\%}\)(精确度\(85{\%}\))。SRGz实现了高质量的x射线源光学对口物的光度分类:\({>}99{\%}\)在SDSS光谱源和GAIA天体测量星的测试样本上对河外物体(类星体或星系)和恒星的光度分类完备性(召回率)。我们分析了各种光度特征对eROSITA x射线源光学识别和分类的重要性。我们已经表明,红外(IR)星等\(W_{2}\), x射线/光学(IR)比率,光学颜色(例如\((g-r)\))和红外颜色(\(W_{1}-W_{2}\))以及我们引入的颜色距离在分离x射线物体的类别中起着重要作用。我们使用最重要的光度特征来解释SRGz的预测。SRGz光度红移的准确性(来自DESI LIS, SDSS, Pan-STARRS和WISE光度数据)已经在Stripe82X现场测试了3/4的eROSITA点x射线源的光学对应样本(Stripe82X中可获得光谱测量):\(\sigma_{NMAD}=3.1{\%}\)(预测的归一化中位数绝对偏差)和\(n_{>0.15}=7.8{\%}\)(灾难性异常值的比例)。本文提出的Stripe82X油田eROSITA x射线源的photo-z结果在两个指标(\(\sigma_{NMAD}\)和\(n_{>0.15}\))上都比Stripe82X目录中发表的其他组的photo-z结果好2倍以上。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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