Differentiable Programming for End-to-End Optimization of Experiments - Lecture 7

Explore differentiable programming for end-to-end optimization of experiments in this lecture by Tommaso Dorigo, part of the "Statistical Methods and Machine Learning in High Energy Physics" program. Delve into advanced techniques for optimizing experimental design and analysis in high energy physics research. Learn how differentiable programming can be applied to improve data collection, processing, and interpretation in complex particle physics experiments. Gain insights into cutting-edge approaches for maximizing the efficiency and effectiveness of experimental setups at facilities like the Large Hadron Collider. Discover how these methods can contribute to the search for new physics and the precise measurement of fundamental particles' properties. Suitable for PhD students, postdoctoral researchers, and professionals working in theoretical or experimental particle physics and astro-particle physics with programming experience and knowledge of event generation and data analysis frameworks.