一种高性能神经网络参数优化的新算法

IF 0.4 Q4 PHYSICS, PARTICLES & FIELDS

Physics of Particles and Nuclei Letters Pub Date : 2025-10-03 DOI:10.1134/S1547477125700955

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

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引用次数: 0

摘要

本研究探索了利用人工神经网络识别大型强子对撞机中罕见过程\(pp \to tHbq\)的潜力，旨在改善信号和背景的分离。开发了一种基于神经网络的数学工具来改进信号清洗。该方法通过蒙特卡罗信号和背景事件模拟得到了验证。我们发现神经网络提出了一种很有前途的技术，可以增加信号的重要性，促进过程的检测 \(pp \to tHbq\)

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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

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 \(pp \to tHbq\)

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来源期刊

Physics of Particles and Nuclei Letters PHYSICS, PARTICLES & FIELDS-

CiteScore

0.80

自引率

20.00%

发文量

108

期刊介绍： The journal Physics of Particles and Nuclei Letters, brief name Particles and Nuclei Letters, publishes the articles with results of the original theoretical, experimental, scientific-technical, methodological and applied research. Subject matter of articles covers: theoretical physics, elementary particle physics, relativistic nuclear physics, nuclear physics and related problems in other branches of physics, neutron physics, condensed matter physics, physics and engineering at low temperatures, physics and engineering of accelerators, physical experimental instruments and methods, physical computation experiments, applied research in these branches of physics and radiology, ecology and nuclear medicine.