The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. As a modern cosmology analysis, all pre-submission methodological decisions were made with the flux scale and external comparison results blinded. In total, this analysis used 4289 standard-star spectra taken on photometric nights. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300 A to 9400 A as calibration for the Nearby Supernova Factory cosmology experiment. We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the HST-based CALSPEC flux system. Our open-source training code, public training data, model, and documentation are available at, and the model is integrated into the sncosmo and SNANA software packages. 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. 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. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low- z Foundation and CfA3 samples by 15% and 10%, respectively. The resulting trained SALT3.K21 model has an extended wavelength range 2000â11,000 à (1800 à redder) and reduced uncertainties compared to SALT2, enabling accurate use of low- z I and iz photometric bands. Our compilation is 2.5à larger than the SALT2 training sample and has greatly reduced calibration uncertainties. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. 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 an improved model framework, SALT3, which has several advantages over current models-including the leading SALT2 model (SALT2.4). The Supercal method will allowįuture analyses to tie past samples to the best calibrated sample.Ī 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. The $B-V$ calibration of the low-$z$ surveys. The size of this effect strongly depends on the error in This change is roughly half the size of current statisticalĬonstraints on $w$. Values for the dark energy equation-of-state parameter, $w$, by on average We find that correcting for these differences changes recovered We measureÄiscrepancies on average of 10 mmag, but up to 35 mmag, in various optical Taken by the following SN samples: PS1, SNLS, SDSS, CSP, and CfA1-4. We use this process to recalibrate optical observations PS1 has observed $3\pi$ of the sky and has a relativeĬalibration of better than 5 mmag (for $\sim15 Systematic uncertainties of the cosmological parameter estimation. Photometric calibration between different SN samples is one of the largest Single sample and increase the overall sample size. Observations combine SN samples to expand the redshift range beyond that of a Current cosmological analyses which use Type Ia supernova (SN Ia)
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