RUI: casting light on a semi-visible higgs boson with novel triggers at atlas

This Project Grant from the National Science Foundation supports the development of machine learning tools and outreach programs related to the ATLAS experiment at CERN's Large Hadron Collider. Awarded $200,000 on July 15, 2022 and set to be completed by June 30, 2025, the funding is provided under the NSF's Mathematical and Physical Sciences program (CFDA 47.049).

Specifically, the award to Westmont College will develop novel triggers using machine learning techniques to improve the selectivity of the ATLAS detector for detecting new physics beyond the Standard Model in Higgs decays to two photons and other channels. Students supported by this Research in Undergraduate Institutions award will directly analyze LHC data and learn new machine learning techniques. They will also create outreach programs with local high schools to promote progress in the mathematical and physical sciences.

Abstract

This award will provide support for work on the atlas experiment at the large hadron collider (LHC) at cern, a particle physics laboratory in geneva, switzerland. The LHC machine and atlas, a large particle detector facility, were built as basic science tools using funds from NSF and other agencies around the world. The 2012 discovery, at the LHC, of a higgs boson with mass close to 125 GEV represents both the crowning achievement of the standard model of particle physics and a hint beyond IT - how can a light higgs boson possibly survive huge, destabilizing quantum effects without new, undiscovered physics? The next step in the experiments is to look for evidence for physics beyond the standard model (BSM) that might, for instance, account for the presence of the mysterious dark matter that makes up so much of the mass of the universe. The LHC is currently just starting a new phase, run 2, at higher energy and increased event samples. IT is possible that evidence for BSM physics could emerge in the next couple of years. This award will develop tools for detecting new BSM physics by improving the trigger selection using machine learning (ML) techniques. These new triggers will improve the selectivity of the atlas detector for BSM physics in the higgs decays to two photons and other channels where new BSM particles could show up. This RUI will allow students to directly take part in the analysis of the LHC data and learn new ML techniques. They will also develop outreach programs with local high schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.