Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. I show how such methods are promising for measurements at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. Within the experiment, we then develop an NSBI framework that enables the application of NSBI to a full-scale LHC analysis, by developing several new innovative solutions to enhance robustness and interpretability of our neural inference method. The method is a generalisation of the standard statistical framework at the LHC, and can benefit a large number of physics analyses, such as the measurement of the Higgs width in ATLAS.
Bio:
Dr. Aishik Ghosh is a particle physicist at UC Irvine and Berkeley Lab where he studies Higgs physics at the ATLAS experiment using novel statistical and uncertainty quantification methods, that he develops. His current efforts focus on the Higgs width measurement, and he is also working on calorimeter simulation and trigger algorithms. He obtained his PhD from the Université Paris-Saclay for developing simulation-based-inference methods for the Higgs width measurement and deploying the first deep generative model for fast simulation of the ATLAS detector. Dr. Ghosh also works with the Organisation for Economic Co-operation and Development on matters of AI and science policy.