Anja Butter(Centre National de la Recherche Scientifique (FR)), Fabio Catalano(University and INFN Torino (IT)), Julian Garcia Pardinas(CERN), Lorenzo Moneta(CERN), Michael Kagan(SLAC National Accelerator Laboratory (US)), DrPietro Vischia(Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA)), Simon Akar(University of Cincinnati (US)), Stefano Carrazza(CERN)
Generative transformers and how to evaluate them25m
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  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 
 ML4PS @ NeurIPS 2020, https://arxiv.org/abs/2012.00173
 NeurIPS 2021, https://arxiv.org/abs/2106.11535
 2022, https://arxiv.org/abs/2211.10295
Raghav Kansal(Univ. of California San Diego (US))
Anomaly detection and self-supervised representation learning25m
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 Physics25m
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.