Enhancing generalization in high energy physics using white-box adversarial attacks
This addresses generalization issues for particle physicists using supervised learning, but it is incremental as it builds on existing adversarial attack methods.
The paper tackled the problem of supervised models in high energy physics overfitting to artifacts in Monte Carlo simulations, which limits generalization to real data, by applying white-box adversarial attacks to reduce local minima sharpness, resulting in significantly improved generalization performance with increased computational complexity.
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.