Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture

Autor:	Katel, Subash, Li, Haoyang, Zhao, Zihan, Kansal, Raghav, Mokhtar, Farouk, Duarte, Javier
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations. Comment: 5 pages, 2 figures. Accepted to Machine Learning for Physical Sciences NeurIPS 2024 workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.05333 Zobrazit plný text záznamu View this record from Arxiv