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        "author": "Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer",
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        "description": "\n# MadMiner: Machine learning\u2013based inference for particle physics\n\n**By Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer**\n\n[![PyPI version](https://badge.fury.io/py/madminer.svg)](https://badge.fury.io/py/madminer)\n[![Build Status](https://travis-ci.com/diana-hep/madminer.svg?branch=master)](https://travis-ci.com/diana-hep/madminer)\n[![Documentation Status](https://readthedocs.org/projects/madminer/badge/?version=latest)](https://madminer.readthedocs.io/en/latest/?badge=latest)\n[![Gitter](https://badges.gitter.im/madminer/community.svg)](https://gitter.im/madminer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n[![Docker pulls](https://img.shields.io/docker/pulls/madminertool/docker-madminer.svg)](https://hub.docker.com/r/madminertool/docker-madminer)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1489147.svg)](https://doi.org/10.5281/zenodo.1489147)\n\n\n## Introduction\n\nParticle physics processes are usually modeled with complex Monte-Carlo simulations of the hard process, parton shower,\nand detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially\nhigh-dimensional) set of observables, it is usually not possible to calculate the probability of these observables\nfor some model parameters. Particle physicisists usually tackle this problem of \"likelihood-free inference\" by\nhand-picking a few \"good\" observables or summary statistics and filling histograms of them. But this conventional\napproach discards the information in all other observables and often does not scale well to high-dimensional problems.\n\nIn the three publications\n[\"Constraining Effective Field Theories With Machine Learning\"](https://arxiv.org/abs/1805.00013),\n[\"A Guide to Constraining Effective Field Theories With Machine Learning\"](https://arxiv.org/abs/1805.00020), and\n[\"Mining gold from implicit models to improve likelihood-free inference\"](https://arxiv.org/abs/1805.12244),\na new approach has been developed. In a nut shell, additional information is extracted from the simulations that is\nclosely related to the matrix elements that determine the hard process. This\n\"augmented data\" can be used to train neural networks to efficiently approximate arbitrary likelihood ratios. We\nplayfully call this process \"mining gold\" from the simulator, since this information may be hard to get, but turns out\nto be very valuable for inference.\n\nBut the gold does not have to be hard to mine: MadMiner automates these modern multivariate inference strategies. It\nwraps around the simulators MadGraph and Pythia, with different options for the detector simulation. It streamlines all\nsteps in the analysis chain from the simulation to the extraction of the augmented data, their processing, the training\nand evaluation of the neural networks, and the statistical analysis are implemented.\n\n\n## Resources\n\n\n### Paper\n\nOur main publication [MadMiner: Machine-learning-based inference for particle physics](https://arxiv.org/abs/1907.10621)\nprovides an overview over this package. We recommend reading it first before jumping into the code.\n\n\n### Installation instructions\n\nPlease have a look at our [installation instructions](https://madminer.readthedocs.io/en/latest/installation.html).\n\n\n### Tutorials\n\nIn the [examples](examples/) folder in this repository, we provide two tutorials. The first at\n[examples/tutorial_toy_simulator/tutorial_toy_simulator.ipynb](examples/tutorial_toy_simulator/tutorial_toy_simulator.ipynb)\nis based on a toy problem rather than a full particle-physics simulation. It demonstrates\ninference with MadMiner without spending much time on the more technical steps of running the simulation. The second,\nat [examples/tutorial_particle_physics](examples/tutorial_particle_physics), shows all steps of a particle-physics\nanalysis with MadMiner.\n\n\n### Documentation\n\nThe madminer API is documented on [readthedocs](https://madminer.readthedocs.io/en/latest/?badge=latest).\n\n\n### Support\n\nIf you have any questions, please\nchat to us [in our Gitter community](https://gitter.im/madminer/community) or write us at \n[johann.brehmer@nyu.edu](johann.brehmer@nyu.edu).\n\n\n## Citations\n\nIf you use MadMiner, please cite our main publication,\n```\n@article{Brehmer:2019xox,\n      author         = \"Brehmer, Johann and Kling, Felix and Espejo, Irina and\n                        Cranmer, Kyle\",\n      title          = \"{MadMiner: Machine learning-based inference for particle\n                        physics}\",\n      year           = \"2019\",\n      eprint         = \"1907.10621\",\n      archivePrefix  = \"arXiv\",\n      primaryClass   = \"hep-ph\",\n      SLACcitation   = \"%%CITATION = ARXIV:1907.10621;%%\"\n}\n```\n\nThe code itself can be cited as\n```\n@misc{MadMiner_code,\n      author         = \"Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle\",\n      title          = \"{MadMiner}\",\n      doi            = \"10.5281/zenodo.1489147\",\n      url            = {https://github.com/diana-hep/madminer}\n}\n```\n\nThe main references for the implemented inference techniques are the following:\n\n- CARL: [1506.02169](https://arxiv.org/abs/1506.02169)\n- MAF: [1705.07057](https://arxiv.org/abs/1705.07057)\n- CASCAL, RASCAL, ROLR, SALLY, SALLINO, SCANDAL: [1805.00013](https://arxiv.org/abs/1805.00013), [1805.00020](https://arxiv.org/abs/1805.00020), [1805.12244](https://arxiv.org/abs/1805.12244)\n- ALICE, ALICES: [1808.00973](https://arxiv.org/abs/1808.00973)\n\n\n## Acknowledgements\n\nWe are immensely grateful to all contributors and bug reporters! In particular, we would like to thank Zubair Bhatti,\nLukas Heinrich, Alexander Held, and Samuel Homiller.\n\nThe SCANDAL inference method is based on [Masked Autoregressive Flows](https://arxiv.org/abs/1705.07057), and our\nimplementation is a pyTorch port of the original code by George Papamakarios et al., which is available at\n[https://github.com/gpapamak/maf](https://github.com/gpapamak/maf).\n\nThe [setup.py](setup.py) was adapted from\n[https://github.com/kennethreitz/setup.py](https://github.com/kennethreitz/setup.py).\n\n\n",
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