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 including the jet datasets in MOD HDF5 format used in Ref. [4].

To install EnergyFlow with pip, simply execute:

pip3 install energyflow

The documentation is maintained at https://energyflow.network.

[1] P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Polynomials: A complete linear basis for jet substructure, JHEP 04 (2018) 013 [1712.07124].

[2] P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121 [1810.05165].

[3] P. T. Komiske, E. M. Metodiev, and J. Thaler, The Metric Space of Collider Events, Phys. Rev. Lett. 123 (2019) 041801 [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 [1908.08542].

[5] P. T. Komiske, E. M. Metodiev, and J. Thaler, Cutting Multiparticle Correlators Down to Size, Phys. Rev. D 101 (2020) 036019 [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 [1911.09107].

[7] P. T. Komiske, E. M. Metodiev, and J. Thaler, The Hidden Geometry of Particle Collisions, JHEP 07 (2020) 006 [2004.04159].