2023MNRAS.526.1822K

Bolometric light curves play an important role in understanding the underlying physics of various astrophysical phenomena, as they allow for a comprehensive modelling of the event and enable comparison between different objects. However, constructing these curves often requires the approximation and extrapolation from multicolour photometric observations. In this study, we introduce vector Gaussian processes as a new method for reduction of supernova light curves. This method enables us to approximate vector functions, even with inhomogeneous time-series data, while considering the correlation between light curves in different passbands. We applied this methodology to a sample of 29 superluminous supernovae (SLSNe) assembled using the Open Supernova Catalog. Their multicolour light curves were approximated using vector Gaussian processes. Subsequently, under the blackbody assumption for the SLSN spectra at each moment of time, we reconstructed the bolometric light curves. The vector Gaussian processes developed in this work are accessible via the PYTHON library GP-MULTISTATE-KERNEL on GitHub. Our approach provides an efficient tool for analysing light curve data, opening new possibilities for astrophysical research.