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 MONOCHROMATIC method).
Aims: We present RAINBOW, 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 RAINBOW 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 MONOCHROMATIC approach. Similarly, the accuracy improves for all classes in our sample when the RAINBOW 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 potential for multisurvey analysis, this represents an important milestone in the path to enable population studies of photometric transients.

An efficient implementation of RAINBOW has been publicly released as part of the light_curve package, https://github.com/light-curve/light-curve-python