Carnegie Supernova Project: Classification of Type Ia Supernovae
Anthony Burrow, E. Baron, Chris Ashall, Christopher R. Burns, N., Morrell, Maximilian D. Stritzinger, Peter J. Brown, G. Folatelli, Wendy L., Freedman, Llu\'is Galbany, P. Hoeflich, Eric Y. Hsiao, Kevin Krisciunas, M., M. Phillips, Anthony L. Piro, Nicholas B. Suntzeff

TL;DR
This study uses spectroscopy and photometry of 97 Type Ia supernovae to classify them into groups based on multiple parameters, revealing correlations and distinctions among subclasses with improved accuracy.
Contribution
It introduces a four-dimensional parameter space and Gaussian mixture models to refine classification of Type Ia supernovae beyond traditional methods.
Findings
Original four groups in the Branch diagram are well-defined.
Most clustering information is contained in 2-D space.
Full 4-D analysis slightly alters group definitions and improves classification accuracy.
Abstract
We use the spectroscopy and homogeneous photometry of 97 Type Ia supernovae obtained by the \emph{Carnegie Supernova Project} as well as a subset of 36 Type Ia supernovae presented by Zheng et al. (2018) to examine maximum-light correlations in a four-dimensional (4-D) parameter space: -band absolute magnitude, , \ion{Si}{2}~ velocity, \vsi, and \ion{Si}{2} pseudo-equivalent widths pEW(\ion{Si}{2}~) and pEW(\ion{Si}{2}~). It is shown using Gaussian mixture models (GMMs) that the original four groups in the Branch diagram are well-defined and robust in this parameterization. We find three continuous groups that describe the behavior of our sample in [, \vsi] space. Extending the GMM into the full 4-D space yields a grouping system that only slightly alters group definitions in the [, \vsi] projection, showing that most of the…
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