Uncertainty Quantification for Neural Networks in Particle Physics

Learn how to quantify uncertainties in neural network predictions for particle physics applications in this comprehensive lecture by Anja Butter. Explore the critical importance of understanding and measuring the reliability of machine learning models when applied to high-energy physics data analysis. Discover various techniques for uncertainty quantification including Bayesian neural networks, ensemble methods, and dropout-based approaches specifically tailored for particle physics experiments. Examine real-world case studies demonstrating how uncertainty estimates can improve the interpretation of experimental results and enhance the robustness of physics analyses. Understand the challenges unique to particle physics applications, such as dealing with high-dimensional data, complex detector responses, and the need for precise statistical inference. Gain insights into best practices for implementing uncertainty quantification methods in neural networks used for tasks like particle identification, event classification, and parameter estimation in modern particle physics research.