What can Gaussian Processes really tell us about supernova lightcurves? Consequences for Type II(b) morphologies and genealogies

TL;DR

This paper critically examines the use of Gaussian Process regression for analyzing supernova light-curves, highlighting its limitations and proposing improved strategies for classifying supernova types and understanding their morphologies.

Contribution

It provides a detailed assessment of Gaussian Process regression limitations in supernova light-curve analysis and suggests alternative approaches for more reliable classification and morphological studies.

Findings

Gaussian Process regression has limitations in fitting supernova light-curves.

Uncertainties in certain parameters (dm1, dm2) make them unreliable for astrophysical inference.

High-quality light-curve samples and analytical fitting improve classification accuracy.

Abstract

Machine learning has become widely used in astronomy. Gaussian Process (GP) regression in particular has been employed a number of times to fit or re-sample supernova (SN) light-curves, however by their nature typical GP models are not suited to fit SN photometric data and they will be prone to over-fitting. Recently GP re-sampling was used in the context of studying the morphologies of type II and IIb SNe and they were found to be clearly distinct with respect to four parameters: the rise time (t$_{\rm rise}$), the magnitude difference between 40 and 30 days post explosion ($\Delta m_{\rm 40-30}$), the earliest maximum (post-peak) of the first derivative (dm1) and minimum of the second derivative (dm2). Here we take a close look at GP regression and its limitations in the context of SN light-curves in general, and we also discuss the uncertainties on these specific parameters, finding that dm1 and dm2 cannot give reliable astrophysical information. We do reproduce the clustering in t$_{\rm rise}$--$\Delta m_{\rm 40-30}$ space although it is not as clear cut as previously presented. The best strategy to accurately populate the t$_{\rm rise}$-- $\Delta m_{\rm 40-30}$ space will be to use an expanded sample of high quality light-curves (such as those in the ATLAS transient survey) and analytical fitting methods. Finally, using the BPASS fiducial models, we predict that future photometric studies will reveal clear clustering of the type IIb and II light curve morphologies with a distinct continuum of transitional events.