用对抗攻击提升粒子物理模型泛化能力，减少对模拟数据的依赖。
Enhancing generalization in high energy physics using white-box adversarial attacks
- 引入四种白盒对抗攻击，分权重空间与特征空间两类
- 对抗训练使模型在真实数据上准确率提升12.3%（相对基线）
- 适合关注模型鲁棒性与真实数据泛化的高能物理研究者
机器学习在粒子物理中日益普及。监督学习依赖标注的蒙特卡洛（MC）模拟数据，仍是探测标准模型之外信号的主流方法。但本文指出，此类模型可能过度依赖模拟中的伪影和近似，从而限制其在真实数据上的泛化能力。本研究旨在通过降低局部极小值的尖锐度来增强监督模型的泛化性能。研究回顾了四类白盒对抗攻击在希格斯玻色子衰变信号分类任务中的应用，分为权重空间攻击与特征空间攻击。为分析和量化不同局部极小值的尖锐度，提出两种方法：梯度上升法与约化海森矩阵特征值分析。结果表明，白盒对抗攻击显著提升模型泛化性能，尽管计算开销增加。
原文摘要 · Abstract (English)
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.
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