Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations, potentially limiting their ability to generalize well to real data. This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima. It reviews the application of four distinct white-box adversarial attacks in the context of classifying Higgs boson decay signals. The attacks are divided into weight space attacks, and feature space attacks. To study and quantify the sharpness of different local minima this paper presents two analysis methods: gradient ascent and reduced Hessian eigenvalue analysis. The results show that white-box adversarial attacks significantly improve generalization performance, albeit with increased computational complexity.

翻译：机器学习在粒子物理领域的应用日益广泛。使用带标签蒙特卡洛模拟的监督学习，仍然是识别超出标准模型信号最常用的方法之一。然而，本文指出监督模型可能过度依赖蒙特卡洛模拟中的伪影和近似，这可能限制其向真实数据良好泛化的能力。本研究旨在通过降低局部极小值的锐度来增强监督模型的泛化特性。本文回顾了在希格斯玻色子衰变信号分类背景下四种不同白盒对抗攻击的应用。这些攻击分为权重空间攻击和特征空间攻击。为研究和量化不同局部极小值的锐度，本文提出了两种分析方法：梯度上升法和简化海森矩阵特征值分析。结果表明，白盒对抗攻击显著提高了泛化性能，尽管计算复杂度有所增加。