Representing Core-collapse Supernova Light Curves Analytically with Symbolic Regression
Keywords: symbolic regression, supernovae, astronomy, light curves, interpretable machine learning
TL;DR: In this work, we use symbolic regression to derive an analytic expression for the luminosity of the most common core-collapse supernova as a function of time and physical parameters (for the first time).
Abstract: Radiative transfer simulations of cosmic transients–the rapidly evolving terminal events of stars–are computationally expensive, making Bayesian inference infeasible on even a single events. Yet, astronomical surveys have discovered tens-of-thousands of these events. In this work, we use symbolic regression to derive an analytic expression for the luminosity of the most common core-collapse supernova (the explosive death of a massive star) as a function of time and physical parameters – an analytical expression for these events has eluded the literature for a century. This expression is trained from a set of simulated bolometric light curves (measured luminosity as a function of time) generated from six input physical parameters. We find that a single analytic expression can reproduce $\sim$70\% of light curves in our dataset with less than $\sim$7.5\% fractional error; we additionally present a small set of analytical expressions to reproduce the full set of light curves. By deriving an analytic relation between physical parameters and light curve luminosities, we create an interpretable parametric model and emulate the more expensive simulator. This work demonstrates promising preliminary results for future efforts to build interpretable emulators within time-domain astrophysics.
Submission Track: Original Research
Submission Number: 171
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