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Abstract <p>We apply a unified machine-learning framework based on normalizing flows (NFs) for the event-by-event reconstruction of invisible momenta and the subsequent evaluation of spin-sensitive observables in top-quark pair and dark-matter (DM) associated production processes. Building on recent studies in single-top + DM topologies, we extend the research to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(t\bar{t}+DM\)</EquationSource> <!--BPhysMGU2570259Abasov-m1--> </InlineEquation> final state. Inputs to our networks combine low-level four-momenta and missing transverse energy with high-level kinematic and angular variables. We compare a baseline multilayer perceptron (MLP) regressor, an autoregressive flow and the conditional ‘‘<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\nu\)</EquationSource> <!--BPhysMGU2570259Abasov-m2--> </InlineEquation>-Flows’’ model–trained to learn the full conditional density. In these final states all the models perform well and demonstrate high reconstruction quality in independent regions split by <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(m_{t\bar{t}}\)</EquationSource> <!--BPhysMGU2570259Abasov-m3--> </InlineEquation> for validation purposes. We highlight the potential of this approach to be extended to 3 and 4 top-quark production.</p>

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Applying Normalizing Flows for Spin Correlations Reconstruction in Associated Top-Quark Pair and Dark Matter Production

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • E. Abasov,
  • L. Dudko,
  • E. Iudin,
  • A. Markina,
  • P. Volkov,
  • G. Vorotnikov,
  • M. Perfilov,
  • A. Zaborenko

摘要

Abstract

We apply a unified machine-learning framework based on normalizing flows (NFs) for the event-by-event reconstruction of invisible momenta and the subsequent evaluation of spin-sensitive observables in top-quark pair and dark-matter (DM) associated production processes. Building on recent studies in single-top + DM topologies, we extend the research to \(t\bar{t}+DM\) final state. Inputs to our networks combine low-level four-momenta and missing transverse energy with high-level kinematic and angular variables. We compare a baseline multilayer perceptron (MLP) regressor, an autoregressive flow and the conditional ‘‘ \(\nu\) -Flows’’ model–trained to learn the full conditional density. In these final states all the models perform well and demonstrate high reconstruction quality in independent regions split by \(m_{t\bar{t}}\) for validation purposes. We highlight the potential of this approach to be extended to 3 and 4 top-quark production.