semvec/pypi-packages huggingface dataset:
EnergyFlow 1.4.0
Python package for the Energy Flow suite of particle physics tools
Requires Python: >=3.9
Keywords: CMS, Deep Sets, EFM, EFN, EFP, EMD, Earth Mover Distance, Energy Flow Moment, Energy Flow Network, Energy Flow Polynomial, MOD, Open Data, PFN, Particle Flow Network, Wasserstein, architecture, collider, correlator, energy flow, energyflow, jets, metric, multigraph, neural network, open, physics, polynomial, substructure

# EnergyFlow
[![Build Status](https://github.com/thaler-lab/EnergyFlow/actions/workflows/test-energyflow.yml/badge.svg)](https://github.com/thaler-lab/EnergyFlow/actions/workflows/test-energyflow.yml?query=branch%3Amaster)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/thaler-lab/EnergyFlow/master)

EnergyFlow is a Python package that computes Energy Flow Polynomials (EFPs) as defined in Ref. [1], implements Energy Flow Networks (EFNs) and Particle Flow Networks (PFNs) as defined in Ref. [2], computes Energy Mover's Distances as defined in Ref. [3], and provides access to some particle physics [datasets hosted on Zenodo](https://zenodo.org/search?page=1&size=20&q=komiske&sort=title) including the jet datasets in MOD HDF5 format used in Ref. [4].

#### Installation

To install EnergyFlow with pip, simply execute:

```console
python -m pip install energyflow
```

#### Documentation

The documentation is maintained at [https://energyflow.network](https://energyflow.network).

##### References

[1] P. T. Komiske, E. M. Metodiev, and J. Thaler, _Energy Flow Polynomials: A complete linear basis for jet substructure_, [JHEP __04__ (2018) 013](https://doi.org/10.1007/JHEP04(2018)013) [[1712.07124](https://arxiv.org/abs/1712.07124)].

[2] P. T. Komiske, E. M. Metodiev, and J. Thaler, _Energy Flow Networks: Deep Sets for Particle Jets_, [JHEP __01__ (2019) 121](https://doi.org/10.1007/JHEP01(2019)121) [[1810.05165](https://arxiv.org/abs/1810.05165)].

[3] P. T. Komiske, E. M. Metodiev, and J. Thaler, _The Metric Space of Collider Events_, [Phys. Rev. Lett. __123__ (2019) 041801](https://doi.org/10.1103/PhysRevLett.123.041801) [[1902.02346](https://arxiv.org/abs/1902.02346)].

[4] P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik, and J. Thaler, _Exploring the Space of Jets with CMS Open Data_, [Phys. Rev. D **101** (2020) 034009](https://doi.org/10.1103/PhysRevD.101.034009) [[1908.08542](https://arxiv.org/abs/1908.08542)].

[5] P. T. Komiske, E. M. Metodiev, and J. Thaler, _Cutting Multiparticle Correlators Down to Size_, [Phys. Rev. D **101** (2020) 036019](https://doi.org/10.1103/PhysRevD.101.036019) [[1911.04491](https://arxiv.org/abs/1911.04491)].

[6] A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, _OmniFold: A Method to Simultaneously Unfold All Observables_, [Phys. Rev. Lett. __124__ (2020) 182001](https://doi.org/10.1103/PhysRevLett.124.182001) [[1911.09107](https://arxiv.org/abs/1911.09107)].

[7] P. T. Komiske, E. M. Metodiev, and J. Thaler, _The Hidden Geometry of Particle Collisions_, [JHEP __07__ (2020) 006](https://doi.org/10.1007/JHEP07(2020)006) [[2004.04159](https://arxiv.org/abs/2004.04159)].