# The Tracking Machine Learning challenge : Accuracy phase

## TL;DR

This paper discusses the results of the 2018 Tracking Machine Learning Challenge, which used crowd-sourced machine learning methods to improve particle trajectory tracking in high energy physics experiments at the LHC.

## Contribution

It introduces a large-scale machine learning challenge for particle tracking, demonstrating the effectiveness of crowd-sourced algorithms in high energy physics data analysis.

## Key findings

- Top algorithms achieved high accuracy in particle trajectory reconstruction
- Diverse machine learning approaches were effective in solving the tracking problem
- The competition score reliably identified the best algorithms

## Abstract

This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document