bayesn-td

BayeSN for strongly-lensed Type Ia supernovae time-delay cosmography.

bayesn-td extends the BayeSN hierarchical SED model to jointly fit multiply-imaged Type Ia supernovae, inferring time delays, magnification ratios, and (optionally) achromatic microlensing effects using JAX/NumPyro.

The method is described in Grayling et al. (2026), MNRAS, 548, 2 (arXiv:2510.11719).

Documentation

Full documentation is hosted at bayesn-td.readthedocs.io.

Installation

pip install .

For development:

pip install -e .

Requirements

• Python >= 3.11
• JAX, NumPyro, NumPy, SciPy, pandas, Matplotlib, Astropy, h5py, extinction, ArviZ, ruamel.yaml, tqdm

Usage

A complete working example with simulated LSST photometry of a 3-image lensed SN Ia is bundled in examples/sim_lensed_sn/.

Command line

run_bayesn_td examples/sim_lensed_sn/input.yaml

Python API

from bayesn_td import SEDmodel

model = SEDmodel()
samples = model.fit_lensed_sn(
    photometry='examples/sim_lensed_sn/photometry.ecsv',
    output='results/sim_lensed_sn',
)

See examples/sim_lensed_sn/README.md for the true input parameters used to generate the simulation.

Citation

If you use bayesn-td in your research, please cite Grayling et al. (2026):

@article{Grayling2026,
    title        = {BayeSN-TD: Time Delay and $H_0$ Estimation for Lensed SN H0pe},
    author       = {Grayling, M. and Thorp, S. and Mandel, K. S. and Pascale, M. and Pierel, J. D. R. and Hayes, E. E. and Larison, C. and Agrawal, A. and Narayan, G.},
    journal      = {Monthly Notices of the Royal Astronomical Society},
    volume       = {548},
    number       = {2},
    year         = {2026},
    doi          = {10.1093/mnras/stag340},
    eprint       = {2510.11719},
    archivePrefix = {arXiv},
    primaryClass = {astro-ph.CO},
}

License

MIT