Pileup Subtraction and Jet Energy Prediction Using Machine Learning

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.

Not ready for preprint, not enough physics based analysis. The data only involved simulation data

References in corpus (3)

• Pileup subtraction using jet areas
• Pileup Per Particle Identification
• Quantifying the performance of jet definitions for kinematic reconstruction at the LHC

Cited by in corpus (1)

• Supervised deep learning in high energy phenomenology: a mini review