A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation | |
Yuan ZL(袁尊理)1,4,5; Jarvis,Matt J.2,3; Wang JC(王建成)1,4,5 | |
刊名 | ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES |
2020-05-01 | |
卷号 | 248期号:1页码:18 |
ISSN号 | 0067-0049 |
DOI | 10.3847/1538-4365/ab855b |
产权排序 | 第1完成单位 |
文献子类 | Article |
英文摘要 | We propose a flexible method for estimating luminosity functions (LFs) based on kernel density estimation (KDE), the most popular nonparametric density estimation approach developed in modern statistics, to overcome issues surrounding the binning of LFs. One challenge in applying KDE to LFs is how to treat the boundary bias problem, as astronomical surveys usually obtain truncated samples predominantly due to the flux-density limits of surveys. We use two solutions, the transformation KDE method) and the transformation-reflection KDE method to reduce the boundary bias. We develop a new likelihood cross-validation criterion for selecting optimal bandwidths, based on which the posterior probability distribution of the bandwidth and transformation parameters for are derived within a Markov Chain Monte Carlo sampling procedure. The simulation result shows that perform better than the traditional binning method, especially in the sparse data regime around the flux limit of a survey or at the bright end of the LF. To further improve the performance of our KDE methods, we develop the transformation-reflection adaptive KDE approach ). Monte Carlo simulations suggest that it has good stability and reliability in performance, and is around an order of magnitude more accurate than using the binning method. By applying our adaptive KDE method to a quasar sample, we find that it achieves estimates comparable to the rigorous determination in a previous work, while making far fewer assumptions about the LF. The KDE method we develop has the advantages of both parametric and nonparametric methods. |
学科主题 | 天文学 ; 天体物理学 ; 高能天体物理学 |
URL标识 | 查看原文 |
出版地 | TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND |
资助项目 | National Natural Science Foundation of China[11603066] ; National Natural Science Foundation of China[U1738124] ; National Natural Science Foundation of China[11573060] ; National Natural Science Foundation of China[11661161010] ; Yunnan Natural Science Foundation[2019FB008] ; Yunnan Natural Science Foundation[2019FB009] ; Oxford Hintze Centre for Astrophysical Surveys through Hintze Family Charitable Foundation |
WOS关键词 | GALAXY REDSHIFT SURVEY ; COSMOLOGICAL EVOLUTION ; BOUNDARY CORRECTION ; BAYESIAN-APPROACH ; BIAS CORRECTION ; SAMPLES ; CUTOFF ; AGN ; SELECTION ; FIELD |
WOS研究方向 | Astronomy & Astrophysics |
语种 | 英语 |
出版者 | IOP PUBLISHING LTD |
WOS记录号 | WOS:000529872100001 |
资助机构 | National Natural Science Foundation of China[11603066, U1738124, 11573060, 11661161010] ; Yunnan Natural Science Foundation[2019FB008, 2019FB009] ; Oxford Hintze Centre for Astrophysical Surveys through Hintze Family Charitable Foundation |
内容类型 | 期刊论文 |
源URL | [http://ir.ynao.ac.cn/handle/114a53/23155] |
专题 | 云南天文台_高能天体物理研究组 |
通讯作者 | Yuan ZL(袁尊理) |
作者单位 | 1.Center for Astronomical Mega-Science, Chinese Academy of Sciences, Beijing 100012, People's Republic of China 2.Physics and Astronomy Department, University of the Western Cape, Bellville, 7535, South Africa 3.Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK 4.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650216, People's Republic of China 5.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People's Republic of China |
推荐引用方式 GB/T 7714 | Yuan ZL,Jarvis,Matt J.,Wang JC. A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2020,248(1):18. |
APA | Yuan ZL,Jarvis,Matt J.,&Wang JC.(2020).A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,248(1),18. |
MLA | Yuan ZL,et al."A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 248.1(2020):18. |
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