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

TL;DR

EmpiriciSN introduces Python tools for extreme deconvolution Gaussian mixture modeling to analyze supernova and host galaxy data, enabling conditioned sampling and improved simulations for astronomical surveys.

Contribution

The paper presents new open source Python tools for XD Gaussian mixture modeling with conditioned sampling capabilities, tailored for supernova and galaxy data analysis.

Findings

New Python tools for XD GMM with conditioning functionality.

Application of these tools to simulate supernovae based on galaxy properties.

Enhanced ability to predict supernova parameters from host data.

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 for using Gaussian mixtures to do density estimation of noisy data using extreme deconvolution (XD) algorithms that has functionality not available in other XD tools. It allows the user to select between the AstroML (Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting methods and is compatible with scikit-learn machine learning algorithms (Pedregosa et al. 2011). 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 conditioned on known values of other parameters. EmpiriciSN is an example application of this functionality that can be used for fitting an XDGMM model to observed supernova/host datasets and predicting likely supernova parameters using on a model conditioned on observed host properties. It is primarily intended for simulating realistic supernovae for LSST data simulations based on empirical galaxy properties.