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Application of Bayesian graphs to SN Ia data analysis and compression
Ma, Cong1,2,3; Corasaniti, Pier-Stefano3; Bassett, Bruce A.4,5,6
2016-12-01
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume463Issue:2Pages:1651-1665
AbstractBayesian graphical models are an efficient tool for modelling complex data and derive self-consistent expressions of the posterior distribution of model parameters. We apply Bayesian graphs to perform statistical analyses of Type Ia supernova (SN Ia) luminosity distance measurements from the joint light-curve analysis (JLA) data set. In contrast to the chi(2) approach used in previous studies, the Bayesian inference allows us to fully account for the standard-candle parameter dependence of the data covariance matrix. Comparing with chi(2) analysis results, we find a systematic offset of the marginal model parameter bounds. We demonstrate that the bias is statistically significant in the case of the SN Ia standardization parameters with a maximal 6s shift of the SN light-curve colour correction. In addition, we find that the evidence for a host galaxy correction is now only 2.4s. Systematic offsets on the cosmological parameters remain small, but may increase by combining constraints from complementary cosmological probes. The bias of the chi(2) analysis is due to neglecting the parameter-dependent log-determinant of the data covariance, which gives more statistical weight to larger values of the standardization parameters. We find a similar effect on compressed distance modulus data. To this end, we implement a fully consistent compression method of the JLA data set that uses a Gaussian approximation of the posterior distribution for fast generation of compressed data. Overall, the results of our analysis emphasize the need for a fully consistent Bayesian statistical approach in the analysis of future large SN Ia data sets.
KeywordMethods: Data Analysis Methods: Statistical Supernovae: General Cosmological Parameters Distance Scale
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.1093/mnras/stw2069
WOS KeywordHUBBLE-SPACE-TELESCOPE ; SUPERNOVA LEGACY SURVEY ; DARK-ENERGY ; SYSTEMATIC UNCERTAINTIES ; COSMOLOGICAL PARAMETERS ; MODEL SELECTION ; STAR-FORMATION ; HOST GALAXIES ; LIGHT CURVES ; SDSS-II
Indexed BySCI
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000388122400041
PublisherOXFORD UNIV PRESS
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/24105
Collection中国科学院紫金山天文台
Corresponding AuthorMa, Cong
Affiliation1.Chinese Acad Sci, Purple Mt Observ, 2 West Beijing Rd, Nanjing 210008, Jiangsu, Peoples R China
2.Univ Chinese Acad Sci, Grad Sch, 19A Yuquan Rd, Beijing 100049, Peoples R China
3.Univ Paris Diderot, PSL Res Univ, Observ Paris, LUTH,UMR CNRS 8102, 5 Pl Jules Janssen, F-92190 Meudon, France
4.Univ Cape Town, Dept Math & Appl Math, Cross Campus Rd, ZA-7700 Rondebosch, South Africa
5.African Inst Math Sci, 6-8 Melrose Rd, ZA-7945 Muizenberg, South Africa
6.South African Astron Observ, Observ Rd, ZA-7925 Observatory, South Africa
Recommended Citation
GB/T 7714
Ma, Cong,Corasaniti, Pier-Stefano,Bassett, Bruce A.. Application of Bayesian graphs to SN Ia data analysis and compression[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2016,463(2):1651-1665.
APA Ma, Cong,Corasaniti, Pier-Stefano,&Bassett, Bruce A..(2016).Application of Bayesian graphs to SN Ia data analysis and compression.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,463(2),1651-1665.
MLA Ma, Cong,et al."Application of Bayesian graphs to SN Ia data analysis and compression".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 463.2(2016):1651-1665.
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