Implementation of machine learning techniques to predict impact\n parameter and transverse spherocity in heavy-ion collisions at the LHC Article Swipe

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A. N. Mishra , Neelkamal Mallick , S. Tripathy , Suman Deb , R. Sahoo · YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2110.04026

Machine learning techniques have been quite popular recently in the\nhigh-energy physics community and have led to numerous developments in this\nfield. In heavy-ion collisions, one of the crucial observables, the impact\nparameter, plays an important role in the final-state particle production. This\nbeing extremely small (i.e. of the order of a few fermi), it is almost\nimpossible to measure impact parameter in experiments. In this work, we\nimplement the ML-based regression technique via Gradient Boosting Decision\nTrees (GBDT) to obtain a prediction of impact parameter in Pb-Pb collisions at\n$\\sqrt{s_{NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. After\nits successful implementation in small collision systems, transverse\nspherocity, an event shape observable, holds an opportunity to reveal more\nabout the particle production in heavy-ion collisions as well. In the absence\nof any experimental exploration in this direction at the LHC yet, we suggest an\nML-based regression method to estimate centrality-wise transverse spherocity\ndistributions in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV by training the\nmodel with minimum bias collision data. Throughout this work, we have used a\nfew final state observables as the input to the ML-model, which could be easily\nmade available from collision data. Our method seems to work quite well as we\nsee a good agreement between the simulated true values and the predicted values\nfrom the ML-model.\n

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preprint

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http://arxiv.org/abs/2110.04026

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https://arxiv.org/pdf/2110.04026

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green

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10

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https://openalex.org/W4327856541

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https://openalex.org/W4327856541

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DOI

https://doi.org/10.48550/arxiv.2110.04026

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Title

Implementation of machine learning techniques to predict impact\n parameter and transverse spherocity in heavy-ion collisions at the LHC

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preprint

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2021

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2021-10-08

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A. N. Mishra, Neelkamal Mallick, S. Tripathy, Suman Deb, R. Sahoo

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https://arxiv.org/abs/2110.04026

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https://arxiv.org/pdf/2110.04026

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Yes

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green

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https://arxiv.org/pdf/2110.04026

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Concepts

Large Hadron Collider, Observable, Physics, Collision, Gradient boosting, Nuclear physics, Transverse plane, Heavy ion, Work (physics), Impact parameter, Particle physics, Centrality, Statistical physics, Fermi Gamma-ray Space Telescope, Computational physics, Ion, Computer science, Statistics, Machine learning, Mathematics, Quantum mechanics, Random forest, Engineering, Computer security, Structural engineering

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10

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