MACK: Mismodeling Addressed with Contrastive Knowledge

Autor:	Sheldon, Liam Rankin, Rankin, Dylan Sheldon, Harris, Philip
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics. Comment: 13 pages, 4 figures, Submission to SciPost
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.13947 Zobrazit plný text záznamu View this record from Arxiv