The determination of the supernovae parameters from their light curves using the machine learning

The paper discusses the application of the machine learning library, CatBoost, to determine the masses of radioactive isotopes from the supernova light curve at a later epochs. The synthetic light curve model used for the demonstration is based on the contribution of the five major radioactive decay chains starting with $^{56}$Ni, $^{57}$Ni, $^{44}$Ti, $^{22}$Na, $^{60}$Co. Separately, we considered sets of random light curves calculated for different isotope masses of both the three dominant chains ($^{56}$Ni, $^{57}$Ni, $^{44}$Ti) and all five. It is shown that the masses of dominant isotopes are determined with acceptable accuracy in both cases, even with the standard settings of the machine learning algorithm. In the second case, the accuracy of determining the masses of the other two isotopes ($^{22}$Na, $^{60}$Co) turns out to be unsatisfactory, probably due to their weak contribution to the total light curve.