A new realease of SuperNNova is in the main branch.
For DES-5yr analysis please use the branch SNANA_DES5yr
(and any other analysis using the syntax: python run.py)
SuperNNova is an open-source photometric time-series classification framework.
The framework includes different RNN architectures (LSTM, GRU, Bayesian RNNs) and can be trained with simulations in .csv and SNANA FITS format. SNN is part of the PIPPIN end-to-end cosmology pipeline.
You can train your own model for time-series classification (binary or multi-class) using photometry and additional features.
Please include the full citation if you use this material in your research: A Möller and T de Boissière, MNRAS, Volume 491, Issue 3, January 2020, Pages 4277–4293.
https://supernnova.readthedocs.io
Clone this repository (preferred)
git clone https://github.com/supernnova/supernnova.gitand configure environment using this documentation
Please include the full citation if you use this material in your research: A Möller and T de Boissière, MNRAS, Volume 491, Issue 3, January 2020, Pages 4277–4293.
To reproduce Möller & de Boissière, 2019 MNRAS switch to paper branch and build documentation.
To reproduce the Dark Energy Survey analyses use commit fcf8584b64974ef7a238eac718e01be4ed637a1d. For more recent analyses of DES branch SNANA_DES5yr (should be PIPPIN backward compatible).
- Möller et al. 2022 MNRAS
- Möller et al. 2024 MNRAS
- Vincenzi et al. 2023 MNRAS
- DES Collaboration 2024 ApJ
To reproduce Fink analyses until 2024 use commit fcf8584b64974ef7a238eac718e01be4ed637a1d and check Fink's github.
cd docs && make clean && make html && cd ..
firefox docs/_build/html/index.html
This package has been updated to a recent pytorch and updated CI/CD through the ADACS Merit allocation program 2023-2024.