# SALTShaker and SALT3¶

## Overview¶

SALT is a model of Type Ia supernovae (SNe Ia) that accounts for spectral variations as a function of shape and color (Guy et al., 2007; Guy et al., 2010; Betoule et al., 2014). With SALTShaker we have developed an open-source model training framework and created the “SALT3” model. We more than doubled the amount of photometric and spectroscopic data used for model training and have extended the SALT framework to 11,000 Angstroms. SALT3 will make use of iz data from PS1, the Vera Rubin Observatory, and the Nancy Grace Roman Space Telescope and can be re-trained easily in the coming years as more SN Ia data become available.

Please report bugs, issues and requests via the SALTShaker GitHub page.

## SALT3 Model and Training Data¶

The first version of the SALT3 model has been released in:

Kenworthy et al., 2021, ApJ, submitted

The SALT3 model files are linked here. SALT3 light curve fits can be performed using sncosmo (currently the latest version on GitHub is required) or SNANA with the SALT3.K21 model, with a brief sncosmo example given below.

The SALT3 training data is also fully public and included here. This release includes all photometry and spectra along with everything required to run the code. Once SALTShaker has been installed via the instructions in Installation, the SALT3 model can be (re)trained with the following command:

trainsalt -c Train_SALT3_public.conf


## Example SALT3 Fit¶

Fitting SN Ia data with SALT3 can be done through the sncosmo or SNANA software packages. With sncosmo, the fitting can be performed in nearly the exact same way as SALT2. Here is the example from the sncosmo documentation, altered to use the SALT3 model. First, install the latest version of sncosmo; SALT3 is included beginning in version 2.5.0:

conda install -c conda-forge sncosmo


or:

pip install sncosmo


Then, in a python terminal:

import sncosmo