# Quark-Gluon Tagging: Machine Learning vs Detector

## TL;DR

This paper compares machine learning methods for quark-gluon discrimination at the LHC, demonstrating improved performance with a 4-vector-based LoLa tagger even after detector effects, and shows benefits in specific search scenarios.

## Contribution

It introduces a 4-vector-based LoLa tagger for quark-gluon tagging and evaluates its performance with detector effects, highlighting advantages over standard analysis in LHC searches.

## Key findings

- LoLa tagger outperforms traditional methods
- Detector effects reduce but do not eliminate performance gains
- Enhanced sensitivity in mono-jet and di-jet resonance searches

## Abstract

Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.