High Energy Physics Seminar

Quantum interference between signal and background Feynman diagrams produce non-linear effects that challenge core assumptions going into the statistical analysis methodology in particle physics. I show that for such cases, no single observable can capture all the relevant information needed to perform optimal inference of theory parameters from data collected in our experiments. The optimal data analysis strategy is to perform statistical inference directly on high-dimensional data, without relying on summary histograms. Neural Simulation-Based Inference (NSBI) is a class of techniques that naturally handle high dimensional data, avoiding the need to design low-dimensional summary histograms. We design a general purpose statistical framework in the ATLAS experiment that enables the application of NSBI to a full-scale physics analysis, leading to the most precise measurement of the Higgs width by the experiment to date. This work develops several innovative solutions to introduce uncertainty quantification and enhance robustness and interpretability in NSBI. The developed method is an extension of the standard frequentist statistical inference framework used in particle physics and is therefore applicable to a wide range of physics analysis.

The talk is in 469 Lauritsen.

Contact theoryinfo@caltech.edu by January 3rd 2025 for Zoom link.