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学领域正变得越来越普及。其中，使用带标签的蒙特卡洛模拟的监督学习，仍然是用于识别超出标准模型信号的最广泛使用的方法之一。然而，本文指出，监督模型可能过度依赖于蒙特卡洛模拟中的伪影和近似，这可能会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最小值的尖锐程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，四种不同的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。为了研究和量化不同局部最小值的尖锐程度，本文提出了两种分析方法：梯度上升法和约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。