Overcoming the challenges of quantum interference in Higgs physics with Neural Simulation-Based Inference

Speaker: Aishik Ghosh (UC Irvine)

Date: Wednesday, November 13, 2024

Time: 4:00 – 5:00 pm

Location: 472 Willamette Hall (IFS Seminar Room)

Abstract: Non-linear effects such as from quantum interference pose significant challenges to the established statistical methods employed at the Large Hadron Collider (LHC). These are of particular concern in some of the most important measurements in collider physics, including that of the Higgs width. Neural Simulation-Based Inference is a powerful class of machine learning-based methods for statistical inference that naturally handle these challenges by performing high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. I will discuss these challenges in Higgs physics and the solution developed, first in a phenomenology study, and then implemented in the ATLAS experiment. The dramatic improvement in sensitivity for a flagship Higgs measurement promises significant gains to be had in several other studies at the LHC and more generally in particle physics with the use of this newly developed method.