Estimating elliptic flow coefficient in heavy ion collisions using deep learning Article Swipe

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Neelkamal Mallick , Suraj Prasad , A. N. Mishra , R. Sahoo , F. Barile ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1103/physrevd.105.114022

Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.

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Large Hadron Collider

Artificial Intelligence

Concepts

Large Hadron Collider Robustness (evolution) Observable Elliptic flow Physics Deep learning Energy (signal processing) Particle physics Noise (video) Work (physics) Nuclear physics Centrality Heavy ion Statistical physics Computer science Artificial intelligence Ion Mathematics Statistics Quantum mechanics Image (mathematics) Biochemistry Gene Chemistry

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Type: article
Language: en
Landing Page: https://doi.org/10.1103/physrevd.105.114022
PDF: http://link.aps.org/pdf/10.1103/PhysRevD.105.114022
OA Status: hybrid
References: 56
Related Works: 10
OpenAlex ID: https://openalex.org/W4221147929

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DOI: https://doi.org/10.1103/physrevd.105.114022

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Title: Estimating elliptic flow coefficient in heavy ion collisions using deep learning

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-06-17

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Authors: Neelkamal Mallick, Suraj Prasad, A. N. Mishra, R. Sahoo, F. Barile

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Landing page: https://doi.org/10.1103/physrevd.105.114022

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Concepts: Large Hadron Collider, Robustness (evolution), Observable, Elliptic flow, Physics, Deep learning, Energy (signal processing), Particle physics, Noise (video), Work (physics), Nuclear physics, Centrality, Heavy ion, Statistical physics, Computer science, Artificial intelligence, Ion, Mathematics, Statistics, Quantum mechanics, Image (mathematics), Biochemistry, Gene, Chemistry

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publication_year	2022
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