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Abstract:
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With the advent of large dedicated Type Ia supernova (SN Ia) surveys, it is possible to empirically quantify the SN Ia spectral energy distribution (SED) in detail. A data-driven statistical approach conducting such a task can facilitate the utilization of the data product and lead to a better understanding of SN Ia intrinsic properties. Based on a collection of SN Ia spectra collected by a public and open-source relational database Kaepora, we propose an empirical SN Ia SED model for sparse and irregularly spaced functional data using functional principal component analysis. Under this framework, the SED surface is represented by a linear combination of several components, and a novel low-rank representation of SN Ia is given by the corresponding coefficients. These coefficients are used to establish relations between SED properties and supernova physical quantities such as intrinsic color, interstellar dust reddening, and light curve parameters.
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