Kernel PCA for Type Ia supernovae photometric classification

Abstract

The problem of supernova photometric identification will be extremely important for large surveys in the next decade. In this work, we propose the use of Kernel Princi-pal Component Analysis (KPCA) combined with k = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classification. The method does not rely on information about redshift or local environmental variables, so it is less sensitive to bias than its template fitting counterparts. The classification is en-tirely based on information within the spectroscopic confirmed sample and each new light curve is classified one at a time. This allows us to update the principal compo-nent (PC) parameter space if a new spectroscopic light curve is available while also avoids the need of re-determining it for each individual new classification. We applied the method to different instances of the Supernova Photometric Classification Chal-lenge (SNPCC) data set. Our method provide good purity results in all data sample analysed, when SNR>5. As a consequence, we can state that if a sample as the post-SNPCC was available today, we would be able to classify ≈ 15 % of the initial data set