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Abstract <p>This study explores the potential of using artificial neural networks to identify the rare process <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(pp \to tHbq\)</EquationSource> <!--PhysPNLt2570095Boyko-m1--> </InlineEquation> at the Large Hadron Collider, aiming to improve the separation of the signal and background. A&#xa0;neural network-based mathematical tool was developed to improve signal cleaning. This approach was validated using Monte Carlo simulation of signal and background events. We find that neural networks suggest a promising technique for increasing the significance of the signal, facilitating the detection of the process <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(pp \to tHbq\)</EquationSource> <!--PhysPNLt2570095Boyko-m2--> </InlineEquation></p>

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A New Algorithm for Optimizing the Parameters of a High-Performance Neural Network

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • I. R. Boyko,
  • N. A. Huseynov,
  • V. I. Kiseeva

摘要

Abstract

This study explores the potential of using artificial neural networks to identify the rare process \(pp \to tHbq\) at the Large Hadron Collider, aiming to improve the separation of the signal and background. A neural network-based mathematical tool was developed to improve signal cleaning. This approach was validated using Monte Carlo simulation of signal and background events. We find that neural networks suggest a promising technique for increasing the significance of the signal, facilitating the detection of the process \(pp \to tHbq\)