Machine Learning based jet momentum reconstruction in heavy-ion collisions

TL;DR

This paper introduces a machine learning approach to improve jet momentum reconstruction in heavy-ion collisions, addressing background fluctuations and enabling measurements at lower transverse momenta than previously possible.

Contribution

A novel machine learning-based method for jet momentum correction that outperforms standard estimators in heavy-ion collision data.

Findings

Superior performance over standard background estimators in simulations

Enables measurement of jets at lower transverse momenta

Potential for more precise jet studies in heavy-ion physics

Abstract

The precise reconstruction of jet transverse momenta in heavy-ion collisions is a challenging task. A major obstacle is the large number of (mainly) low-$p_{\rm T}$ particles overlaying the jets. Strong region-to-region fluctuations of this background complicate the jet measurement and lead to significant uncertainties. In this paper, a novel approach to correct jet momenta (or energies) for the underlying background in heavy-ion collisions is introduced. The proposed method makes use of common Machine Learning techniques to estimate the jet transverse momentum based on several parameters, including properties of the jet constituents. Using a toy model and HIJING simulations, the performance of the new method is shown to be superior to the established standard area-based background estimator. The application of the new method to data promises the measurement of jets down to extremely low transverse momenta, unprecedented thus far in data on heavy-ion collisions.