Optimal strategies for identifying quasars in DESI
Optimal strategies for identifying quasars in DESI
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DOI：
10.1088/1475-7516/2020/11/015
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发表时间：
2020-07
影响因子：
6.4
通讯作者：
J. Farr;A. Font-Ribera;A. Pontzen
中科院分区：
文献类型：
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作者：
J. Farr;A. Font-Ribera;A. Pontzen
随着光谱调查规模的不断扩大，将光谱分类为类星体（QSO）的问题将需要超越其对人类专家的历史依赖。相反，自动分类器将越来越成为主要的分类方法，在模糊的情况下只留下一小部分光谱进行目视检查。为了最大限度地提高分类精度，充分利用现有的分类器将是非常重要的，特别是在寻找识别和消除独特的故障模式。在这项工作中，我们证明了基于机器学习的分类器QuasarNET将用于未来的调查，如暗能量光谱仪（DESI），将其性能与DESI管道分类器redrock进行比较。在四次穿越其足迹的第一次过程中，DESI将需要选择高z（z ≥ 2.1）类星体进行重新观测，因此我们首先评估分类器在从单次曝光光谱中识别高z类星体方面的性能。然后，我们量化的分类器的能力，在低和高Z仓，使用coadded光谱模拟结束的调查数据，以构建类星体目录。对于这些任务，QuasarNET能够以其目前的形式超越Redrock，从单次曝光中识别出大约99%的高z QSO，并产生污染水平低于%的QSO目录。通过结合QuasarNET和redrock的输出，我们可以进一步改进分类策略，从单次曝光中识别高达99.5%的高z类星体，并将最终的类星体目录污染降低到0.5%以下。这些综合战略有效地满足了DESI的类星体分类需求。
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 ⩾ 2.1) 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 out-perform 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.