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

arXiv 2501.06896 ↗First posted 12 Jan 2025cs.LG hep-ex physics.data-ancited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 0 of its 14 references that are in the map. First posted on arXiv 12 Jan 2025.

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Citing papers per year: 2025: 2; 2026: 1.

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