  The future of time-domain optical astronomy relies on the development of
techniques and software capable of handling a rising amount of data and
gradually complementing, or replacing if necessary, real observations. Next
generation surveys, like the Large Synoptic Survey Telescope (LSST), will open
the door to the new era of optical astrophysics, creating, at the same time, a
deficiency in spectroscopic data necessary to confirm the nature of each event
and to fully recover the parametric space. In this framework, we developed Core
collApse Supernovae parameTers estimatOR (CASTOR), a novel software for data
analysis. CASTOR combines Gaussian Process and other Machine Learning
techniques to build time-series templates of synthetic spectra and to estimate
parameters of core collapse supernovae for which only multi-band photometry is
available. Techniques to build templates are fully data driven and
non-parametric through empirical and robust models, and rely on the direct
comparison with a training set of 111 core collapse supernovae from the
literature. Furthermore, CASTOR employees the real photometric data and the
reconstructed synthetic spectra of an event to estimate parameters that belong
to the supernova ejecta, to the stellar progenitor and to the event itself, in
a rapid and user-friendly framework. In this work we provide a demonstration of
how CASTOR works, studying available data from SN2015ap and comparing our
results with those available in literature.
