News

• Riccardo Torre (CERN)
• Loukas Gouskos (CERN)
• Andrea Wulzer (CERN and EPFL)
• Gian Michele Innocenti (CERN)
• Pietro Vischia (Universite Catholique de Louvain (UCL) (BE))
• Lorenzo Moneta (CERN)
• David Rousseau (LAL-Orsay, FR)

Room: 40/S2-C01 - Salle Curie

Machine learning for lattice field theory

• Phiala Shanahan (MIT)

Room: 40/S2-C01 - Salle Curie I will describe avenues to accelerate and enable lattice field theory calculations using machine learning. I will focus in particular on the role of generative models, and requirements such as guarantees of exactness in sampling and the incorporation of complex symmetries (e.g., gauge symmetry) into ML architectures.

Deep Learning as a Tool for Generic Searches at Colliders

• Miguel Crispim Romao (LIP)

Room: 40/S2-C01 - Salle Curie In this talk, we will walk through current applications of Deep Learning as a tool for generic searches for New Physics at colliders. We will show recent results on how Deep Learning discriminators perform on new signal unseen during training and on unsupervised methods to search for New Physics in a complete signal agnostic approach.

Graph Neural Networks for 2D Calorimetric Cluster Reconstruction

• Blaise Raheem Delaney (University of Cambridge (GB))

Room: 40/S2-C01 - Salle Curie In this work, we seek to explore the use of graph neural networks (GNNs) to perform cluster reconstruction from 2D readouts of a calorimeter system. We leverage the ability of GNNs to learn arbitrary detector geometries to regress in a multi-tasked fashion the energy and centroid coordinated of simulated deposits. This talk will focus on the preliminary results of a proof-of-concept study, amounting to training the algorithm on a simulated dataset loosely inspired by the LHCb electromagnetic calorimeter.