Bumblebee: Foundation Model for Particle Physics Discovery

Autor:	Wildridge, Andrew J., Rodgers, Jack P., Colbert, Ethan M., yao, Yao, Jung, Andreas W., Liu, Miaoyuan
Rok vydání:	2024
Předmět:	High Energy Physics - Experiment Computer Science - Machine Learning High Energy Physics - Phenomenology
Druh dokumentu:	Working Paper
Popis:	Bumblebee is a foundation model for particle physics discovery, inspired by BERT. By removing positional encodings and embedding particle 4-vectors, Bumblebee captures both generator- and reconstruction-level information while ensuring sequence-order invariance. Pre-trained on a masked task, it improves dileptonic top quark reconstruction resolution by 10-20% and excels in downstream tasks, including toponium discrimination (AUROC 0.877) and initial state classification (AUROC 0.625). The flexibility of Bumblebee makes it suitable for a wide range of particle physics applications, especially the discovery of new particles. Comment: 5 pages, 3 figures, submitted to Machine Learning and the Physical Sciences Workshop, NeurIPS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.07867 Zobrazit plný text záznamu View this record from Arxiv