New Physic

New physics research focuses on identifying deviations from the Standard Model of particle physics using advanced data analysis techniques. Current efforts leverage machine learning, particularly employing neural networks (including convolutional and transformer architectures), kernel methods, and generative models like normalizing flows and diffusion models, to analyze high-energy physics data and efficiently search for anomalous signals. These methods aim to improve the sensitivity and model-independence of searches for new particles and interactions, potentially revolutionizing our understanding of fundamental physics. The development of robust and efficient anomaly detection algorithms is crucial for extracting meaningful insights from the massive datasets generated by experiments like the Large Hadron Collider.