Data Science in Science

Data Science for Particle and Nuclear Physics

Dear Colleagues,
Recent decades have seen exciting breakthroughs in the rapidly-evolving field of particle and nuclear physics, including the discovery of the Higgs boson and a growing understanding of rare phenomena. With massive planned developments on the High-Luminosity Large Hadron Collider, the Electron-Ion Collider and other facilities, there is an increasing need for novel data science methods that enable confident and timely scientific discoveries with massive and complex data. This Special Issue of Data Science in Science invites both original research and review articles that bring together state-of-the-art data science, machine learning, and AI methodology for high-impact applications in experimental, phenomenological or theoretical particle and nuclear physics. For original research, we welcome unpublished submissions on novel data science methods that investigate relevant research hypotheses using potentially new and/or previously untapped data sources. For review articles, we welcome submissions that define data science challenges in particle and/or nuclear physics, and current progress towards effective solutions and applications.
Topics of interest to this call include but are not limited to the following:

• High-dimensional inference and uncertainty quantification for parameter estimation and/or unfolding.
• Surrogate modeling for emulation and uncertainty quantification of computationally expensive event generators.
• Simulation-based inference, likelihood-free inference and unbinned likelihood analysis for particle physics applications.
• Multi-scale and/or multi-fidelity approaches for model-to-data comparisons in particle and/or nuclear physics.
• Scalable anomaly and novelty detection for discovery of new particles, experimental diagnostics and/or data quality monitoring.
• Foundation models for data analysis in particle and/or nuclear physics applications.
• Inference in the presence of nuisance parameters, systematic uncertainties and model misspecification in particle and/or nuclear physics analyses.
• Decorrelation and algorithmic fairness in particle and/or nuclear physics.
• High-dimensional model validation.
• Experimental design, including end-to-end detector design optimization.
• Combination and comparison of measurements from multiple experiments.
• Interpretability of high-dimensional models in particle and/or nuclear physics.

Sincerely yours,
Special Issue Editors, Data Science in Particle and Nuclear Physics