AI Could Speed Up Search for New Physics by Reconstructing Particle Collisions
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Scientists propose using AI to quickly and accurately reconstruct paths of particles created in collisions at particle accelerators. This could help detect new physics.
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The AI method involves training a deep neural network on simulated collision data. Initial tests show it reconstructs tracks as well as classical methods.
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The AI could debut in the MUonE experiment that is investigating a discrepancy related to muons that could indicate new physics.
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MUonE aims to better determine a "hadronic correction" parameter that will help reveal if muon behavior differs from the Standard Model prediction.
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If proven effective under real conditions, AI particle track reconstruction could mark the start of new detection techniques in physics experiments.