  Bolometric light curves play an important role in understanding the
underlying physics of various astrophysical phenomena, as they allow for a
comprehensive modeling of the event and enable comparison between different
objects. However, constructing these curves often requires the approximation
and extrapolation from multicolor 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 multicolor light curves were approximated using vector Gaussian
processes. Subsequently, under the black-body 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 analyzing light curve data, opening new possibilities for astrophysical
research.
