CPF Seminar with Aishik Ghosh

Abstract: 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. Similar challenges are also posed by systematic uncertainties or the presence of multiple parameters of interest. I show that for such analyses, no single observable can capture all the relevant information needed to perform optimal inference of theory parameters from data collected in our experiments. I then design an optimal data analysis strategy for the Higgs width measurement with high-dimensional statistical inference enabled by neural networks. This Neural Simulation-Based Inference (NSBI) approach naturally handles high dimensional data, avoiding the need for low-dimensional summary histograms. Next, 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 a generalization of the standard frequentist statistical inference framework used in particle physics, and is applicable to a wide range of physics analysis. It also enables the design of analysis that were not feasible before due to the computational complexities and investment in human time required. I will also show an example of how I applied the same concepts for high-dimensional Bayesian inference in astrophysics.

Bio: 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. Besides physics, Aishik works on algorithmic biases and trustworthy AI.