References¶
If you use MadMiner, please cite this code as
@misc{MadMiner,
author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle",
title = "{MadMiner}",
doi = "10.5281/zenodo.1489147",
url = {https://github.com/johannbrehmer/madminer}
}
For the inference methods, there are three main references. Two introduce most of the methods in a particle physics setting:
@article{Brehmer:2018kdj,
author = "Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and
Pavez, Juan",
title = "{Constraining Effective Field Theories with Machine
Learning}",
journal = "Phys. Rev. Lett.",
volume = "121",
year = "2018",
number = "11",
pages = "111801",
doi = "10.1103/PhysRevLett.121.111801",
eprint = "1805.00013",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
@article{Brehmer:2018eca,
author = "Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and
Pavez, Juan",
title = "{A Guide to Constraining Effective Field Theories with
Machine Learning}",
journal = "Phys. Rev.",
volume = "D98",
year = "2018",
number = "5",
pages = "052004",
doi = "10.1103/PhysRevD.98.052004",
eprint = "1805.00020",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
In addition, the inference techniques are discussed in a more general setting, and the SCANDAL family of methods is added in:
@article{Brehmer:2018hga,
author = "Brehmer, Johann and Louppe, Gilles and Pavez, Juan and
Cranmer, Kyle",
title = "{Mining gold from implicit models to improve
likelihood-free inference}",
year = "2018",
eprint = "1805.12244",
archivePrefix = "arXiv",
primaryClass = "stat.ML",
SLACcitation = "%%CITATION = ARXIV:1805.12244;%%"
}
Some inference methods are introduced in other papers, including CARL, Masked Autoregressive Flows, and ALICE(S).
Acknowledgements¶
We are immensely grateful to all contributors and bug reporters! In particular, we would like to thank Zubair Bhatti, Alexander Held, and Duccio Pappadopulo. A big thanks to Lukas Heinrich for his help with workflows and Docker containers.
The SCANDAL inference method is based on Masked Autoregressive Flows, and our implementation is a pyTorch port of the original code by George Papamakarios et al., which is available at https://github.com/gpapamak/maf.
The setup.py was adapted from https://github.com/kennethreitz/setup.py.