Built-in models

bayesn-td ships with several pre-trained BayeSN models. These are the same models available in the main BayeSN package, and define the spectral energy distribution surfaces, dust parameters, and intrinsic scatter that underpin the light-curve model.

Summary

Model	Wavelength range	Phase range	\(R_V\) type
`G26x_model`	2800–18500 Å	−10 to +85 days	population
`W22x_model`	2750–18500 Å	−10 to +50 days	fixed
`W22_model`	3000–18500 Å	−10 to +40 days	fixed
`T21_model`	3500–9500 Å	−10 to +40 days	fixed
`M20_model`	3000–18500 Å	−10 to +40 days	fixed

Available models

G26x_model

The Grayling et al. (2026, MNRAS, 548) extended model, covering 2800–18500 Å and −10 to +85 rest-frame days. This is the default model.

W22x_model

A variant of the W22 model, covering 2750–18500 Å and −10 to +50 rest-frame days.

W22_model

The Ward et al. (2023, ApJ, 956, 111) model, trained on combined Foundation and Avelino datasets with BgVrizYJH photometry spanning 3000–18500 Å and −10 to +40 rest-frame days.

T21_model

The Thorp et al. (2021, MNRAS, 508, 4310) model, trained on 157 SNe Ia from the Foundation survey using griz photometry, covering 3500–9500 Å and −10 to +40 rest-frame days.

M20_model

The Mandel et al. (2022, MNRAS, 510, 3939) model, trained on 86 SNe Ia with BVRIYJH photometry spanning 3000–18500 Å and −10 to +40 rest-frame days.

Choosing a model

The model’s wavelength range must cover the rest-frame wavelengths probed by your filters at the source redshift.

Using a custom model

You can use any BayeSN model by pointing load_model to a BAYESN.YAML file:

model = SEDmodel(load_model='/path/to/my_model/BAYESN.YAML')

The YAML file should contain the model parameters (W0, W1, L_SIGMA_EPSILON, M0, SIGMA0, TAUA, L_KNOTS, TAU_KNOTS, and either RV for a fixed-\(R_V\) model or MUR and SIGMAR for a population-\(R_V\) model).