文章基本信息

标题：EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model
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作者：Thomas W.-S. Holoien ; Philip J. Marshall ; Risa H. Wechsler 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2017
卷号：153
期号：6
DOI：10.3847/1538-3881/aa68a1
语种：English
出版社：American Institute of Physics
摘要：We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.
关键词：methods: statistical;supernovae: general