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What is ML Techniques for Impact Parameter in Heavy-Ion Collisions about?

This study utilizes machine learning techniques, specifically boosted decision trees, to estimate the impact parameter and transverse spherocity in heavy-ion collisions at LHC energies. The authors predict these quantities in Pb-Pb collisions at 2.76 and 5.02 TeV using multiphase transport models and find that their predictions align well with simulated data. The research highlights the potential of machine learning to extract meaningful observables from heavy-ion collision experiments, which are typically

Author

Aditya Nath Mishra

Language

EN