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Abstract <p>This work is devoted to studying the potential of machine learning techniques in relativistic nuclear physics for distinguishing between various physical theories and, consequently, gaining a deeper comprehension of the underlying physical processes in ultra-relativistic nuclear collisions. Recent findings on the modeling of <i>p</i> + <i>p</i> and <i>A</i> + <i>A</i> interactions within the framework of the color string fusion model suggest that it is feasible to describe the experimentally observed event-by-event azimuthal asymmetry in a unified manner across various colliding systems. Such a description has become possible by considering two mechanisms of string interaction: (1) changes in the magnitude of the colour field in the region of string overlap in the transverse collision plane (2) Lorentz boosts applied to particles emerging as a result of string motion due to their mutual attraction. We demonstrate that it is feasible to train machine learning algorithms using <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11497_2025_10011_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="20" /> </InlineMediaObject> <EquationSource Format="TEX">\({{p}_{{\text{T}}}}\)</EquationSource> <!--PhysPNLt2470189Andronov-m1--> </InlineEquation>–<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11497_2025_10011_Article_IEq2.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="16" /> </InlineMediaObject> <EquationSource Format="TEX">\(\phi \)</EquationSource> <!--PhysPNLt2470189Andronov-m2--> </InlineEquation> distributions from event-by-event data to distinguish between the proposed sources of collective behaviour.</p>

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Probing Collectivity in String Models via Machine Learning

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • E. Andronov

摘要

Abstract

This work is devoted to studying the potential of machine learning techniques in relativistic nuclear physics for distinguishing between various physical theories and, consequently, gaining a deeper comprehension of the underlying physical processes in ultra-relativistic nuclear collisions. Recent findings on the modeling of p + p and A + A interactions within the framework of the color string fusion model suggest that it is feasible to describe the experimentally observed event-by-event azimuthal asymmetry in a unified manner across various colliding systems. Such a description has become possible by considering two mechanisms of string interaction: (1) changes in the magnitude of the colour field in the region of string overlap in the transverse collision plane (2) Lorentz boosts applied to particles emerging as a result of string motion due to their mutual attraction. We demonstrate that it is feasible to train machine learning algorithms using \({{p}_{{\text{T}}}}\) – \(\phi \) distributions from event-by-event data to distinguish between the proposed sources of collective behaviour.