jetnet 0.2.0

Jets + ML integration

For developing and reproducing ML + HEP projects.

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JetNet • Installation • Quickstart • Docs • Citation • References

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JetNet

JetNet is an effort to increase accessibility and reproducibility in jet-based machine learning.

Currently we provide:

• Easy-to-access and standardised interfaces for the following datasets:
  • JetNet
  • TopTagging
• Standard implementations of generative evaluation metrics (Ref. [1]):
  • Fréchet ParticleNet Distance (FPND)
  • Wasserstein-1 (W1)
  • coverage and minimum matching distance (MMD)
• Loss functions:
  • Differentiable implementation of the energy mover's distance [2]
• And more general jet utilities.

Additional functionality is under development, and please reach out if you're interested in contributing!

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).

Quickstart

Datasets can be downloaded and accessed quickly, for example:

from jetnet.datasets import JetNet, TopTagging
# as numpy arrays:
particle_data, jet_data = JetNet.getData(jet_type=["g", "q"], data_dir="./datasets/jetnet/")
# or as a PyTorch dataset:
dataset = TopTagging(jet_type="all", , data_dir="./datasets/toptagging/", split="train")

Evaluation metrics can be used as such:

generated_jets = np.random.rand(50000, 30, 3)
fpnd_score = jetnet.evaluation.fpnd(generated_jets, jet_type='g')

Loss functions can be initialized and used similarly to standard PyTorch in-built losses such as MSE:

emd_loss = jetnet.losses.EMDLoss(num_particles=30)
loss = emd_loss(real_jets, generated_jets)
loss.backward()

Documentation

The full 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.

Citation

If you find this library useful for your research, please consider citing our original paper which introduces it [1].

Additionally, if you use our EMD loss implementation, please cite the respective qpth or cvxpy libraries, depending on the method used (qpth by default).

References

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