fiestaEM 0.0.1

Fast inference of electromagnetic signals with JAX

fiesta 🎉

fiesta: Fast Inference of Electromagnetic Signals and Transients with jAx

NOTE: fiesta is currently under development -- stay tuned!

Installation

pip installation is currently work in progress. Install from source by cloning this Github repository and running

pip install -e .

NOTE: This is using an older and custom version of flowMC. Install by cloning the flowMC version at this fork (branch fiesta).

Training surrogate models

To train your own surrogate models, have a look at some of the example scripts in the repository for inspiration, under trained_models

• train_Bu2019lm.py: Example script showing how to train a surrogate model for the POSSIS Bu2019lm kilonova model.
• train_afterglowpy_tophat.py: Example script showing how to train a surrogate model for afterglowpy, using a tophat jet structure.

Examples

• run_AT2017gfo_Bu2019lm.py: Example where we infer the parameters of the AT2017gfo kilonova with the Bu2019lm model.
• run_GRB170817_tophat.py: Example where we infer the parameters of the GRB170817 GRB with a surrogate model for afterglowpy's tophat jet. NOTE This currently only uses one specific filter. The complete inference will be updated soon.

Acknowledgements

The logo was created by ideogram AI.