# Jet-Parton Assignment in ttH Events using Deep Learning

## TL;DR

This paper explores deep learning methods for accurately assigning jets to partons in simulated ttH events, achieving over 50% correctness, to improve top-Higgs coupling measurements.

## Contribution

It introduces a deep learning approach for jet-parton assignment in ttH events, outperforming traditional methods and enhancing reconstruction accuracy.

## Key findings

- Deep learning achieves over 50% correct assignments.
- Compared methods show improved accuracy over traditional techniques.
- Enhances the measurement of the top-Higgs coupling.

## Abstract

The direct measurement of the top quark-Higgs coupling is one of the important questions in understanding the Higgs boson. The coupling can be obtained through measurement of the top quark pair-associated Higgs boson production cross-section. Of the multiple challenges arising in this cross-section measurement, we investigate the reconstruction of the partons originating from the hard scattering process using the measured jets in simulated ttH events. The task corresponds to an assignment challenge of m objects (jets) to n other objects (partons), where m>=n. We compare several methods with emphasis on a concept based on deep learning techniques which yields the best results with more than 50% of correct jet-parton assignments.