论文标题
DESI传统成像调查数据的五号候选人目录版本9
The Quasar Candidates Catalogs of DESI Legacy Imaging Survey Data Release 9
论文作者
论文摘要
类星体可用于测量高红移时的重子声学振荡，这被认为是宇宙中最遥远的大规模结构的直接示踪剂。在实施上述研究之前，从观察值中选择类星体是至关重要的。这项工作着重于基于光度数据创建类星体候选者的目录，以提供主要的先验，以便将来通过光谱数据进行进一步的对象分类，例如黑暗能源光谱仪器（DESI）调查。我们采用机器学习算法（随机森林，RF）进行类星体识别。培训组包括$ 651,073美元的阳性和$ 1,227,172 $的负面因素，其中光度信息来自DESI传统成像调查（DESI-LIS）\＆Wide-Field Indrared调查探索（WISE），这些标签来自基于sloscoppical of Specrospicalsight Quasan Quastrosced Quasans sepripational seles sele（Sloscorpical of Slosed Quasans seprication selfipation and sere nepery sethery sethery sethery sethery set＆set /天文数据的测量和书目（SIMBAD）。训练有素的RF模型应用于Desi-Lis数据版本9中的点状源。为了量化分类器的性能，我们还将测试集注入到了待命的数据中。最终，我们获得了$ 1,953,932 $ A级类星体候选人和$ 22,486，884 $级B级类星体候选人$ 425,540,269 $来源（$ \ sim 5.7 \％\％）。该目录通过评估测试集上的分类完整性，涵盖了待办事项数据中的$ \ sim 99 \％$。候选人的统计特性与彩色选择方法给出的统计特性一致。我们的目录可以通过消除巨大的非Quasars来确认即将到来的DESI数据确认类星体的工作量，但保持较高的完整性。本文中的所有数据均可在线公开获取。
Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the Universe. It is fundamental to select quasars from observations before implementing the above research. This work focuses on creating a catalog of quasar candidates based on photometric data to provide primary priors for further object classification with spectroscopic data in the future, such as The Dark Energy Spectroscopic Instrument (DESI) Survey. We adopt a machine learning algorithm (Random Forest, RF) for quasar identification. The training set includes $651,073$ positives and $1,227,172$ negatives, in which the photometric information are from DESI Legacy Imaging Surveys (DESI-LIS) \& Wide-field Infrared Survey Explore (WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey (SDSS) and the Set of Identifications \& Measurements and Bibliography for Astronomical Data (SIMBAD). The trained RF model is applied to point-like sources in DESI-LIS Data Release 9. To quantify the classifier's performance, we also inject a testing set into the to-be-applied data. Eventually, we obtained $1,953,932$ Grade-A quasar candidates and $22,486, 884$ Grade-B quasar candidates out of $425,540,269$ sources ($\sim 5.7\%$). The catalog covers $\sim 99\%$ of quasars in the to-be-applied data by evaluating the completeness of the classification on the testing set. The statistical properties of the candidates agree with that given by the method of color-cut selection. Our catalog can intensely decrease the workload for confirming quasars with the upcoming DESI data by eliminating enormous non-quasars but remaining high completeness. All data in this paper is publicly available online.
