Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning

TL;DR

This paper introduces how open data from the Large Hadron Collider can be converted into accessible formats for computer scientists, promoting interdisciplinary collaboration and advancing machine learning applications in particle physics.

Contribution

It demonstrates converting LHC open data from ROOT to pandas DataFrames and provides an introductory overview to facilitate collaboration between physicists and computer scientists.

Findings

Data conversion from ROOT to pandas DataFrames completed successfully.

Provides a clear overview of LHC data content and interpretation.

Facilitates interdisciplinary collaboration in machine learning applications.

Abstract

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.