The rise of machine learning in large scientific facilities
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REFERENCES
[1] Boggia L., Cocha C., Giasemis F., et al. (2025). Review of machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb. arXiv preprint. DOI:10.48550/arXiv.2506.14578 [2] Zhu Y., Liang H., Wang Y., et al. (2025). Holistic approach and advanced color singlet identification for physics measurements at the high-energy frontier. arXiv preprint. DOI:11783.10.48550/arXiv.2506.11783 [3] Krause C.G., Faucci Giannelli M., Kasieczka G., et al. (2025). CaloChallenge 2022: A community challenge for fast calorimeter simulation. arXiv preprint. DOI:10.48550/arXiv.2410.21611 [4] Fan J. and Wang J. (2024). Online reinforcement learning control for maintaining an optimum beam collision on an electron–positron collider. Phys. Rev. Accel. Beams 27:122801. DOI:10.1103/PhysRevAccelBeams.27.122801 [5] Harilal A., Park K. and Paulini M. (2025). Anomaly detection based on machine learning for the CMS electromagnetic calorimeter online data-quality monitoring. EPJ Web Conf. 320:00048. DOI:10.1051/epjconf/202532000048 -
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Qu H., Gao F., Ruan M., et al. (2025). The rise of machine learning in large scientific facilities. The Innovation Informatics 1:100006. https://doi.org/10.59717/j.xinn-inform.2025.100006
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Qu H., Gao F., Ruan M., et al. (2025). The rise of machine learning in large scientific facilities. The Innovation Informatics 1:100006. https://doi.org/10.59717/j.xinn-inform.2025.100006
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Figure 1.
Schematic overview of machine learning applications in large scientific facilities.
