jetnet 0.0.2

Jets + ML integration

JetNet

A library for developing and reproducing jet-based machine learning (ML) projects.

JetNet provides common standardized PyTorch-based datasets, evaluation metrics, and loss functions for working with jets using ML. Currently supports the flagship JetNet dataset, and the Fréchet ParticleNet Distance (FPDN), Wasserstein-1 (W1), coverage and minimum matching distance (MMD) metrics all introduced in Ref. [1], as well as jet utilities and differentiable implementation of the energy mover's distance [2] for use as a loss function. Additional functionality is currently under development.

Installation

JetNet can be installed with pip:

pip install jetnet

To use the differentiable EMD loss jetnet.losses.EMDLoss, additional libraries must be installed via

pip install "jetnet[emdloss]"

Finally, PyTorch Geometric must be installed independently for the Fréchet ParticleNet Distance metric jetnet.evaluation.fpnd (Installation instructions).

Documentation

The API reference is available at jetnet.readthedocs.io.

More detailed information about each dataset can (or will) be found at jet-net.github.io.

Tutorials for datasets and functions are coming soon.

References

[1] R. Kansal et al. Particle Cloud Generation with Message Passing Generative Adversarial Networks (2021) [2106.11535]

[2] P. T. Komiske, E. M. Metodiev, and J. Thaler, The Metric Space of Collider Events, Phys. Rev. Lett. 123 (2019) 041801 [1902.02346].