RAINBOW: A colorful approach to multipassband light-curve estimation

Context . Time series generated by repeatedly observing astronomical transients are generally sparse, irregularly sampled, noisy, and multidimensional (obtained through a set of broad-band filters). In order to fully exploit their scientific potential, it is necessary to use this incomplete information to estimate a continuous light-curve behavior. Traditional approaches use ad hoc functional forms to approximate the light curve in each filter independently (hereafter, the M ONOCHROMATIC method). Aims . We present R AINBOW , a physically motivated framework that enables simultaneous multiband light-curve fitting. It allows the user to construct a 2D continuous surface across wavelength and time, even when the number of observations in each filter is significantly limited. Methods . Assuming the electromagnetic radiation emission from the transient can be approximated by a blackbody, we combined an expected temperature evolution and a parametric function describing its bolometric light curve. These three ingredients allow the information available in one passband to guide the reconstruction in the others, thus enabling a proper use of multisurvey data. We demonstrate the effectiveness of our method by applying it to simulated data from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) as well as to real data from the Young Supernova Experiment (YSE DR1). Results . We evaluate the quality of the estimated light curves according to three different tests: goodness of fit, peak-time prediction, and ability to transfer information to machine-learning (ML) based classifiers. The results confirm that R AINBOW leads to an equivalent goodness of fit (supernovae II) or to a goodness of fit that is better by up to 75% (supernovae Ibc) than the M ONOCHROMATIC approach. Similarly, the accuracy improves for all classes in our sample when the R AINBOW best-fit values are used as a parameter space in a multiclass ML classification. Conclusions . Our approach enables a straightforward light-curve estimation for objects with observations in multiple filters and from multiple experiments. It is particularly well suited when the light-curve sampling is sparse. We demonstrate its potential for characterizing supernova-like events here, but the same approach can be used for other classes by changing the function describing the light-curve behavior and temperature representation. In the context of the upcoming large-scale sky surveys and their pote