Metadata-Version: 2.1
Name: madminer
Version: 0.7.3
Summary: Mining gold from MadGraph to improve limit setting in particle physics.
Home-page: https://github.com/johannbrehmer/madminer
Author: Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer
Author-email: johann.brehmer@nyu.edu
License: MIT
Description: 
        # MadMiner: Machine learning–based inference for particle physics
        
        **By Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer**
        
        [![PyPI version](https://badge.fury.io/py/madminer.svg)](https://badge.fury.io/py/madminer)
        [![Build Status](https://travis-ci.com/diana-hep/madminer.svg?branch=master)](https://travis-ci.com/diana-hep/madminer)
        [![Documentation Status](https://readthedocs.org/projects/madminer/badge/?version=latest)](https://madminer.readthedocs.io/en/latest/?badge=latest)
        [![Gitter](https://badges.gitter.im/madminer/community.svg)](https://gitter.im/madminer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1489147.svg)](https://doi.org/10.5281/zenodo.1489147)
        [![arXiv](http://img.shields.io/badge/arXiv-1907.10621-B31B1B.svg)](https://arxiv.org/abs/1907.10621)
        
        
        ## Introduction
        
        Particle physics processes are usually modeled with complex Monte-Carlo simulations of the hard process, parton shower,
        and detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially
        high-dimensional) set of observables, it is usually not possible to calculate the probability of these observables
        for some model parameters. Particle physicisists usually tackle this problem of "likelihood-free inference" by
        hand-picking a few "good" observables or summary statistics and filling histograms of them. But this conventional
        approach discards the information in all other observables and often does not scale well to high-dimensional problems.
        
        In the three publications
        ["Constraining Effective Field Theories With Machine Learning"](https://arxiv.org/abs/1805.00013),
        ["A Guide to Constraining Effective Field Theories With Machine Learning"](https://arxiv.org/abs/1805.00020), and
        ["Mining gold from implicit models to improve likelihood-free inference"](https://arxiv.org/abs/1805.12244),
        a new approach has been developed. In a nutshell, additional information is extracted from the simulations that is
        closely related to the matrix elements that determine the hard process. This
        "augmented data" can be used to train neural networks to efficiently approximate arbitrary likelihood ratios. We
        playfully call this process "mining gold" from the simulator, since this information may be hard to get, but turns out
        to be very valuable for inference.
        
        But the gold does not have to be hard to mine: MadMiner automates these modern multivariate inference strategies. It
        wraps around the simulators MadGraph and Pythia, with different options for the detector simulation. It streamlines all
        steps in the analysis chain from the simulation to the extraction of the augmented data, their processing, the training
        and evaluation of the neural networks, and the statistical analysis are implemented.
        
        
        ## Resources
        
        
        ### Paper
        
        Our main publication [MadMiner: Machine-learning-based inference for particle physics](https://arxiv.org/abs/1907.10621)
        provides an overview over this package. We recommend reading it first before jumping into the code.
        
        
        ### Installation instructions
        
        Please have a look at our [installation instructions](https://madminer.readthedocs.io/en/latest/installation.html).
        
        
        ### Tutorials
        
        In the [examples](examples/) folder in this repository, we provide two tutorials. The first at
        [examples/tutorial_toy_simulator/tutorial_toy_simulator.ipynb](examples/tutorial_toy_simulator/tutorial_toy_simulator.ipynb)
        is based on a toy problem rather than a full particle-physics simulation. It demonstrates
        inference with MadMiner without spending much time on the more technical steps of running the simulation. The second,
        at [examples/tutorial_particle_physics](examples/tutorial_particle_physics), shows all steps of a particle-physics
        analysis with MadMiner.
        
        These examples are the basis of [an online tutorial](https://cranmer.github.io/madminer-tutorial/intro) built with on Jupyter Books. It also walks through how to run MadMiner using docker so that you don't have to install Fortran, MadGraph, Pythia, Delphes, etc. You can even run it with no install using binder. 
        
        ### Documentation
        
        The madminer API is documented on [readthedocs](https://madminer.readthedocs.io/en/latest/?badge=latest).
        
        
        ### Support
        
        If you have any questions, please
        chat to us [in our Gitter community](https://gitter.im/madminer/community) or write us at 
        [johann.brehmer@nyu.edu](johann.brehmer@nyu.edu).
        
        
        ## Citations
        
        If you use MadMiner, please cite our main publication,
        ```
        @article{Brehmer:2019xox,
              author         = "Brehmer, Johann and Kling, Felix and Espejo, Irina and
                                Cranmer, Kyle",
              title          = "{MadMiner: Machine learning-based inference for particle
                                physics}",
              journal        = "Comput. Softw. Big Sci.",
              volume         = "4",
              year           = "2020",
              number         = "1",
              pages          = "3",
              doi            = "10.1007/s41781-020-0035-2",
              eprint         = "1907.10621",
              archivePrefix  = "arXiv",
              primaryClass   = "hep-ph",
              SLACcitation   = "%%CITATION = ARXIV:1907.10621;%%"
        }
        ```
        
        The code itself can be cited as
        ```
        @misc{MadMiner_code,
              author         = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle",
              title          = "{MadMiner}",
              doi            = "10.5281/zenodo.1489147",
              url            = {https://github.com/diana-hep/madminer}
        }
        ```
        
        The main references for the implemented inference techniques are the following:
        
        - CARL: [1506.02169](https://arxiv.org/abs/1506.02169)
        - MAF: [1705.07057](https://arxiv.org/abs/1705.07057)
        - 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)
        - ALICE, ALICES: [1808.00973](https://arxiv.org/abs/1808.00973)
        
        
        ## Acknowledgements
        
        We are immensely grateful to all contributors and bug reporters! In particular, we would like to thank Zubair Bhatti,
        Philipp Englert, Lukas Heinrich, Alexander Held, Samuel Homiller, and Duccio Pappadopulo.
        
        The SCANDAL inference method is based on [Masked Autoregressive Flows](https://arxiv.org/abs/1705.07057), 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](https://github.com/gpapamak/maf).
        
        The [setup.py](setup.py) was adapted from
        [https://github.com/kennethreitz/setup.py](https://github.com/kennethreitz/setup.py).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: examples
Provides-Extra: docs
