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

H. F. Stevance, A. Lee

Submitted on 29 June 2022


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 (trise), the magnitude difference between 40 and 30 days post explosion (Δm4030), and the earliest maxima of the first and second derivative of the light curve (dm1 and dm2). Here we take a close look at Gaussian process regression and its limitations in the context of SNe 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 trise--Δm4030 space although it is not as clear cut as previously presented. The best strategy to accurately populate the trise-- Δm4030 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.


Comment: 13 pages, 11 figures, 2 tables, submitted to MNRAS

Subjects: Astrophysics - Solar and Stellar Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysics