What can Gaussian Processes really tell us about supernova lightcurves? Consequences for Type II b morphologies and genealogies by H. F. Stevance et al. on Wednesday 30 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2206.14816v2