News

• Anja Butter (Centre National de la Recherche Scientifique (FR))
• Stefano Carrazza (CERN)
• Simon Akar (University of Cincinnati (US))
• Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
• Michael Kagan (SLAC National Accelerator Laboratory (US))
• Lorenzo Moneta (CERN)
• Fabio Catalano (University and INFN Torino (IT))
• Julian Garcia Pardinas (CERN)

Room: 6/2-024 - BE Auditorium Meyrin

Generative transformers and how to evaluate them

• Raghav Kansal (Univ. of California San Diego (US))

Room: 6/2-024 - BE Auditorium Meyrin With the increase in luminosity and detector granularity, simulation will be a significant computational challenge in the HL-LHC. To tackle this, we present developments in machine learning (ML) graph- [1, 2] and attention-based [3] models for generating jets at the LHC using sparse and efficient point cloud representations of our data, which offer a three-orders-of-magnitude improvement in latency compared to full (Geant4) simulation. We also present studies on metrics for validating ML-based simulations, including the novel Frechet and kernel physics distances, which are found to be highly sensitive to typical mismodelling by ML generative models [3] [1] ML4PS @ NeurIPS 2020, https://arxiv.org/abs/2012.00173 [2] NeurIPS 2021, https://arxiv.org/abs/2106.11535 [3] 2022, https://arxiv.org/abs/2211.10295

Anomaly detection and self-supervised representation learning

• Luigi Favaro (University of Heidelberg)

Room: 6/2-024 - BE Auditorium Meyrin Autoencoders are an effective analysis tool for model-agnostic searches at the LHC. Unfortunately, it is known that their OOD detection performance is not robust and heavily depends on the compressibility of the signals. Even if a neural network can learn the physical content of the low-level data, the gain in sensitivity on features of interest can be hindered by redundant information already explainable in terms of known physics. This poses the problem of constructing a representation space where known physical symmetries are manifest and discriminating features are retained. I’ll present ideas in both directions. I’ll introduce a Normalized Auto-Encoder (NAE), a robust OOD detector based on an energy model, and how a self-supervised contrastive learning training can produce optimized observables for jet tagging (JetCLR) and anomaly detection (AnomCLR).

Versatile Energy-Based Models for High Energy Physics

• Taoli Cheng (University of Montreal)

Room: 6/2-024 - BE Auditorium Meyrin Energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In accordance with these signs of progress, we build a versatile energy-based model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicational aspects, it can serve as a powerful parameterized event generator, a generic anomalous signal detector, and an augmented event classifier.