On the classification and feature relevance of multiband light curves

With an expected torrent of data from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the need for automated identification of noisy and sparse light curves will increase drastically. In this paper, we performed classification of multiband astronomical light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) data set via boosted neural nets, boosted decision trees, and a voted classifier for 14 astronomical categories. In order to deal with noisy features, we used wavelet decomposition together with feature selection. We also performed a feature ranking method using a neural network. Our method may be considered an alternative to random forests, which is known to favor features with more categories as relevant. We also investigated the class importance with neural nets using a one-versus-all approach which reduces the multiclass problem to a binary class problem.