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Developed an auto-encoder model equivariant to Lorentz transformations of the input. We find it outperforms graph and convolutional neural networks on jet reconstruction and anomaly detection tasks.

A systematic investigation of evaluation metrics for fast simulations, including two new ones we propose, the Fréchet and kernel physics distances, which we find to be the most sensitive. We also introduce the generative adversarial particle transformer (GAPT) model, which is significantly faster than MPGAN.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and …

We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We …

Search for events with two high momentum Higgs bosons, using graph neural networks to find Higgs jets. We set the strongest constraints to date on di-Higgs production and the two-vector-boson coupling.

One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, …

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of …

We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and …