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Title: EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Abstract

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.

Authors:
ORCiD logo; ; ORCiD logo
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1362075
Report Number(s):
SLAC-PUB-16990
Journal ID: ISSN 1538-3881; arXiv:1611.00363
DOE Contract Number:
AC02-76SF00515
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astronomical Journal (Online); Journal Volume: 153; Journal Issue: 6
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; ASTRO

Citation Formats

Holoien, Thomas W. -S., Marshall, Philip J., and Wechsler, Risa H.. EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model. United States: N. p., 2017. Web. doi:10.3847/1538-3881/aa68a1.
Holoien, Thomas W. -S., Marshall, Philip J., & Wechsler, Risa H.. EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model. United States. doi:10.3847/1538-3881/aa68a1.
Holoien, Thomas W. -S., Marshall, Philip J., and Wechsler, Risa H.. Thu . "EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model". United States. doi:10.3847/1538-3881/aa68a1. https://www.osti.gov/servlets/purl/1362075.
@article{osti_1362075,
title = {EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model},
author = {Holoien, Thomas W. -S. and Marshall, Philip J. and Wechsler, Risa H.},
abstractNote = {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.},
doi = {10.3847/1538-3881/aa68a1},
journal = {Astronomical Journal (Online)},
number = 6,
volume = 153,
place = {United States},
year = {Thu May 11 00:00:00 EDT 2017},
month = {Thu May 11 00:00:00 EDT 2017}
}
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