From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into
open questions in particle physics. However, detector effects must be corrected before …

There has been enormous progress in the last few years in designing neural networks that
respect the fundamental symmetries and coordinate freedoms of physical law. Some of …

In this work we introduce ν 2-flows, an extension of the ν-flows method to final states
containing multiple neutrinos. The architecture can natively scale for all combinations of …

We propose masked particle modeling (MPM) as a self-supervised method for learning
generic, transferable, and reusable representations on unordered sets of inputs for use in …

In this article, we review the application of modern machine learning (ML) techniques to
boost the search for processes involving the top quarks at the LHC. We revisit the formalism …

Methods for processing point cloud information have seen a great success in collider
physics applications. One recent breakthrough in machine learning is the usage of …

We present a new approach, the Topograph, which reconstructs underlying physics
processes, including the intermediary particles, by leveraging underlying priors from the …

In practice, encoding invariances into models improves sample complexity. In this work, we
study this phenomenon from a theoretical perspective. In particular, we provide minimax …

Abstract We present $\nu $-Flows, a novel method for restricting the likelihood space of
neutrino kinematics in high-energy collider experiments using conditional normalising flows …