Rainbow: a colorful approach on multi-passband light curve estimation
We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of observations in each filter is significantly limited. Assuming the electromagnetic radiation emission from the transient can be approximated by a black-body, 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 multi-survey 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 real data from the Young Supernova Experiment (YSE DR1). We evaluate the quality of the estimated light curves according to three different tests: goodness of fit, time of peak prediction and ability to transfer information to machine learning (ML) based classifiers. Results confirm that Rainbow leads to equivalent (SNII) or up to 75% better (SN Ibc) goodness of fit when compared to the Monochromatic approach. Similarly, accuracy when using Rainbow best-fit values as a parameter space in multi-class ML classification improves for all classes in our sample. An efficient implementation of Rainbow has been publicly released as part of the light curve package at https://github.com/light-curve/light-curve-python. Our approach enables straight forward light curve estimation for objects with observations in multiple filters and from multiple experiments. It is particularly well suited for situations where light curve sampling is sparse.
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