Breaking the color-reddening degeneracy in type Ia supernovae

TL;DR

This paper introduces a PCA-based spectral metric space that isolates intrinsic supernova color from dust reddening, enabling better understanding of supernova properties and improving distance measurements.

Contribution

It presents a novel spectral analysis method that separates intrinsic supernova features from dust effects, enhancing the study of supernova luminosity and color variability.

Findings

Spectral PCA space is insensitive to dust reddening.

Multivariate PLS regression predicts intrinsic light and color curves.

Average host-galaxy RV consistent with Milky Way values.

Abstract

A new method to study the intrinsic color and luminosity of type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis (PCA) on spectral series SNe Ia between -12.5 and +17.5 days from B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of color excess due to dust-extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic color variability. Such metric space is a good predictor of the epoch when the maximum in the B-V color curve is reached. Multivariate Partial Least Square (PLS) regression predicts the intrinsic B band light-curve and the intrinsic B-V color curve up to a month after maximum. This allows to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV in the host-galaxy of SNe Ia is found, on average, to be consistent with typical Milky-Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with large sample publicly available. Future automated surveys as LSST will provide a large number of light curves. The analysis shows that observing accompaning spectra for a significative number of SNe will be important even in the case of "normal" SNe Ia.