Pileup Subtraction and Jet Energy Prediction Using Machine Learning

2015/12/15 by Vein S. Kong, Kong, Vein S, Jiakun Li +3
Computer Science · Engineering · Physics and Astronomy · #Aerodynamics and Acoustics in Jet Flows #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Model Reduction and Neural Networks

paper · pdf · doi:10.48550/arxiv.1512.04672

openalex publication_date 2015/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to accurately estimate jet energy. Different machine learning methods were explored, including linear regression, support vector regression and decision tree. The best result is obtained by linear regression with predictive features and the performance is improved significantly from the baseline method.

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