Abstract
Context. Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. Aims. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. Methods. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. Conclusions. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.
Highlights
Type Ia supernovae (SNe Ia) are excellent cosmological probes: they are very luminous objects that are visible up to a redshift of z ∼ 2 (Guillochon et al 2017), and their luminosity dispersion is naturally low and can be further reduced by an appropriate standardization process
The model above is trained on the 105 SNe Ia that remain in the training sample
In this paper we have presented a new spectra-temporal empirical model of SNe Ia, named SUpernova Generator And Reconstructor (SUGAR)
Summary
Type Ia supernovae (SNe Ia) are excellent cosmological probes: they are very luminous objects that are visible up to a redshift of z ∼ 2 (Guillochon et al 2017), and their luminosity dispersion is naturally low and can be further reduced by an appropriate standardization process. As precise distance indicators, comparing their luminosity and their redshift allowed Perlmutter & Aldering (1998), Perlmutter et al (1999), Riess et al (1998), and Schmidt et al (1998) to demonstrate that the expansion of the Universe is accelerating; a feature that gave rise to the dark energy paradigm This result, obtained with a small sample of SNe Ia, has since been repeatedly confirmed with larger samples (Astier et al 2006; Guy et al.2010; Suzuki et al 2012; Rest et al 2014; Betoule et al 2014; Scolnic et al 2018), and in combination with other cosmological probes like the cosmological microwave background (CMB; Planck Collaboration XIII 2016), baryon acoustic oscillations (BAO; Delubac et al 2015), and cosmic shear (Troxel et al 2018) led to the so-called concordance model, the flat-Λ cold dark matter model. Systematic and statistical uncertainties are already of the same order of magnitude (Betoule et al 2014; Scolnic et al 2018), a better understanding of systematics will be needed to improve the accuracy of SNe Ia as a cosmology probe
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