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Lorentz Group Equivariant Neural Network for Particle Physics
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We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use CNNs and point cloud approaches. The competitive performance of the network is demonstrated on a public classification dataset [27] for tagging top quark decays given energy-momenta of jet constituents produced in proton-proton collisions.
Forward citations
Cited by 3 Pith papers
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Predict before you train: Scaling Laws for particle physics foundation models
A Chinchilla-style law fit on ParticleViT runs below 10^19 FLOPs predicts held-out pretraining loss within ~1% at >100× compute and tracks downstream jet-tagging rejection.
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Explicit or Implicit? Encoding Physics at the Precision Frontier
On three precision classification tasks — reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection — a Lorentz-equivariant transformer and a pretrained foundation model perform...
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Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.
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