SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances

W. D. Kenworthy, D. O. Jones, M. Dai, R. Kessler, D. Scolnic, D. Brout, M. R. Siebert, J. D.R. Pierel, K. G. Dettman, G. Dimitriadis, R. J. Foley, S. W. Jha, Y. C. Pan, A. Riess, S. Rodney, C. Rojas-Bravo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model framework, SALT3, which has several advantages over current models—including the leading SALT2 model (SALT2.4). While SALT3 has a similar philosophy, it differs from SALT2 by having improved estimation of uncertainties, better separation of color and light-curve stretch, and a publicly available training code. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our compilation is 2.5× larger than the SALT2 training sample and has greatly reduced calibration uncertainties. The resulting trained SALT3.K21 model has an extended wavelength range 2000–11,000 Å (1800 Å redder) and reduced uncertainties compared to SALT2, enabling accurate use of low-z I and iz photometric bands. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low-z Foundation and CfA3 samples by 15% and 10%, respectively. To check for potential systematic uncertainties, we compare distances of low (0.01 < z < 0.2) and high (0.4 < z < 0.6) redshift SNe in the training compilation, finding an insignificant 3 ± 14 mmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained on optical data, our method can be used to build a model for rest-frame NIR samples from the Roman Space Telescope. Our open-source training code, public training data, model, and documentation are available at https://saltshaker.readthedocs.io/en/latest/, and the model is integrated into the sncosmo and SNANA software packages.

Original languageEnglish
Article number265
JournalAstrophysical Journal
Volume923
Issue number2
DOIs
StatePublished - 20 Dec 2021

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