Real particle physics analysis by UK secondary school students using the ATLAS Open Data: an illustration through a collection of original student research
Abstract
Since the 2020 release of 10fb-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10 \hbox { fb}^{-1}$$\end{document} of integrated luminosity of proton–proton collision data to the public by the ATLAS experiment, significant potential for its use for youth engagement in physics and citizen science has been present. In particular, this article aims to address whether, if provided adequate training and resources, high school students are capable of leveraging the ATLAS Open Data to semi-autonomously develop their own original research projects. To this end, a repository of interactive Python Jupyter notebook training materials was developed, incrementally increasing in difficulty; in the initial instalments no prior knowledge of particle physics or Python coding is assumed, while in the latter stages students emulate the steps of a real Higgs boson search using ATLAS data. This programme was implemented in secondary schools throughout the UK during the 2022/23 academic year and is presented in this article through a collection of research projects developed by a selection of participating students.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.