As spectroscopic surveys continue to grow in size, the problem of classifying spectra targeted as quasars (QSOs) will need to move beyond its historical reliance on human experts. Instead, automatic classifiers will increasingly become the dominant classification method, leaving only small fractions of spectra to be visually inspected in ambiguous cases. In order to maximise classification accuracy, making best use of available classifiers will be of great importance, particularly when looking to identify and eliminate distinctive failure modes. In this work, we demonstrate that the machine learning-based classifier QuasarNET will be of use for future surveys such as the Dark Energy Spectroscopic Instrument (DESI), comparing its performance to the DESI pipeline classifier redrock. During the first of four passes across its footprint DESI will need to select high-$z$ ($z\geq2.1$) QSOs for reobservation, and so we first assess the classifiers' performance at identifying high-$z$ QSOs from single-exposure spectra. We then quantify the classifiers' abilities to construct QSO catalogues in both low- and high-$z$ bins, using coadded spectra to simulate end-of-survey data. For such tasks, QuasarNET is able to outperform redrock in its current form, identifying approximately 99% of high-$z$ QSOs from single exposures and producing QSO catalogues with sub-percent levels of contamination. By combining QuasarNET and redrock's outputs, we can further improve the classification strategies to identify up to 99.5% of high-$z$ QSOs from single exposures and reduce final QSO catalogue contamination to below 0.5%. These combined strategies address DESI's QSO classification needs effectively.
暗能量光谱仪（DESI）调查将使用类星体作为红移范围0.9<z 2.1的暗物质的直接示踪剂来测量大尺度结构<2.1 and using Ly-alpha forests in quasar spectra at z>。我们提出了几种方法来选择候选类星体的DESI，使用输入测光成像在三个光学波段（g，r，z）从DESI的遗产成像调查和两个红外波段（W1，W2）从宽视场红外探测器（WISE）。这些方法在DESI调查验证期间得到广泛测试。在本文中，我们报告的结果与不同的方法，并提出了选择我们优化的DESI主要调查。最终的类星体目标选择基于随机森林算法，并选择星等范围为16.5<r<23的类星体。超深观测的视觉选择表明，主要的选择包括71%的类星体，16%的星系，6%的恒星和7%的不确定的光谱。使用基于这种选择的光谱，我们建立了一个自动化的类星体目录，在标称有效曝光时间约为1000秒的情况下，达到了>99%的纯度。每平方米310美元。°的目标密度，主要选择允许DESI选择每平方米200多个QSO。°的（包括60个z>2.1的类星体），超出项目要求20%。所选类星体的红移分布与类星体光度函数的预测非常吻合。
Confirming lensed-quasar candidates with DESI and P200 spectroscopy I. 14 lensed quasars and 8 lensed galaxies
Nanchang University（南昌大学）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；Beijing Normal University（北京师范大学）；University of Hawaiʻi（夏威夷大学）；University of San Francisco（旧金山大学）；National Astronomical Observatories（国家天文台）；University of Science and Technology of China（中国科学技术大学）
Measurements of quasar proximity zones with the Ly$α$ forest of DESI Y1 quasars
The Ohio State University（俄亥俄州立大学）；Academia Sinica（中央研究院）；The University of Utah（犹他大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Boston University（波士顿大学）
Baryon Acoustic Oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys
我们在SDSS-IV DR 16 eBOSS类星体和从DESI遗产成像巡天中选择的密集光度计星系样本之间的投影交叉关联函数中搜索重子声学振荡。我们估计在这个红移范围内的星系测光样本的密度约为2900度$^{-2}$，这是比官方DESI ELG选择更深，光谱样本的密度约为20度$^{-2}$。为了减轻与使用接近检测极限的不同成像调查相关的系统性，我们使用了一种神经网络方法，该方法考虑了成像属性和观测到的星系密度之间的复杂依赖关系。我们发现，我们是有限的成像调查的深度，影响的光度计样品的密度和纯度和重叠的红移与类星体样品，从而影响该方法的性能。当将测光星系与$0.6 \leq z \leq 1.2$中的类星体进行互相关时，与从自相关中获得的球平均距离$D_{\rm V}$（9%精度）的约束相比，互相关联函数可以对同动角距离$D_{\rm M}$（6%精度）提供更好的约束。虽然还没有竞争力，这项技术将受益于即将到来的调查，这将使它能够超越目前的限制，我们已经确定在这项工作中更深的光度数据的到来。
COMAP Pathfinder -- Season 2 results IV. A stack on eBOSS/DESI quasars
California Institute of Technology（加州理工学院）；University of Oslo（奥斯陆大学）；Cornell University（康奈尔大学）；Southern Methodist University（南方卫理公会大学）；University of Toronto（多伦多大学）；University of Miami（迈阿密大学）；The University of Manchester（曼彻斯特大学）；Korea Advanced Institute（韩国科学技术院）
Cosmology from Planck CMB Lensing and DESI DR1 Quasar Tomography
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Waterloo（滑铁卢大学）；Perimeter Institute for Theoretical Physics（理论物理前沿研究所）；University of California, Berkeley（加州大学伯克利分校）；University of Cambridge（剑桥大学）
