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 will show how 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 full-scale physics analyses, 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 analyses. I will also discuss how this approach simplifies effective field theory interpretations in the high-dimensional space of theory parameters and future prospects.

Bio: Dr. Aishik Ghosh is a postdoctoral scholar at UC Irvine and an affiliate at 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, trigger algorithms 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 University of Paris-Saclay also on the ATLAS experiment.