Overcoming challenges of quantum interference at LHC with neural simulation-based inference and a full implementation in ATLAS

About

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.

Dr. Aishik Ghosh is a postdoctoral scholar at UC Irvine and Berkeley Lab with a focus on Higgs physics at the ATLAS experiment using novel statistical analysis methods and uncertainty quantification tools. His current efforts focus on the Higgs width and Higgs self-coupling measurements, and he also developed the first generation of deep generative models for fast simulation of the ATLAS calorimeter in 2018. Previously, he obtained his PhD in particle physics from the Université Paris-Saclay also on the ATLAS experiment.

Coffee will be served at 10:30. .

0:00:00 Slide 1

0:01:07 Slide 2

0:02:23 Slide 3

0:04:07 Slide 4

0:05:12 Slide 5

0:08:27 Slide 6

0:10:10 Slide 7

0:11:30 Slide 8

0:13:50 Slide 9