期刊
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
卷 -, 期 11, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1475-7516/2020/11/015
关键词
redshift surveys; galaxy surveys; Lyman alpha forest; baryon acoustic oscillations
资金
- Science and Technology Facilities Council (STFC) studentship
- STFC Ernest Rutherford Fellowship [ST/N003853/1]
- FSE funds trough the program Ramon y Cajal of the Spanish Ministry of Science and Innovation [RYC-2018-025210]
- Royal Society
- STFC Consolidated Grant [ST/R000476/1]
- UCL Cosmoparticle Initiative
- Science and Technology Facilities Council [ST/R000476/1] Funding Source: researchfish
- STFC [1923402, ST/R000476/1, ST/N003853/1] Funding Source: UKRI
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据