NSF-BSF: Elementary Particle Physics with ATLAS

New York University, New York NY

Investigators

Abstract

This award will provide support for the NYU group working 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. One of their primary objectives was to find the Higgs Boson, the last particle in the historically successful "Standard Model" (SM) that accounts for so much of the existence of, and forces between, known particles forming the matter in the universe. This effort has been successful. 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 experiments are currently in the midst of analyzing data from Run 2, at almost twice the energy explored earlier and with significantly increased event samples. It is possible that evidence for BSM physics could emerge at this higher energy and with the higher event statistics from the current run and by means of further upgrades of the LHC and ATLAS to follow. The NYU group will be studying Higgs decays, searching for evidence of supersymmetry and exotic new states of matter through several approaches, including the reaction process known as Vector Boson Fusion, and will be developing novel approaches to the reconstruction and study of high energy Jets. NYU will partner with the Weizmann Institute of Science through support from the US-Israel Binational Science Foundation program to search for the decays of Higgs boson decays to charm quark pairs, through heavy flavor tagging. Here the joint NYU-Weizmann effort will exploit advanced machine learning techniques to identify and select such challenging final states in Higgs decay. Technical projects include improvements and refinements of the ATLAS trigger for missing energy, a key variable in the searches for new physics, and track triggering, an essential technique for dealing with reconstruction of events in very high rate collisions, which are endemic at the LHC. The group also helps to lead a smaller experimental effort called milliQan, whose objective is to search for fractionally charged particles that might be produced at the LHC. The group has a strong Machine Learning effort to further the precision of the Higgs measurements and to clarify BSM discovery in channels such as new Higgs particles in the so-called Hidden Valley suggested by string theory. The NYU group's broader impacts efforts are extensive, including a collaboration with the National Museum of Mathematics and Brookhaven National Laboratory designed to bring to the general public the excitement of particle physics and a better understanding of how a basic understanding of statistics can be used to assess conclusions that might be drawn from experiments or medical tests. The group's use of machine learning and simulation-based inference will continue to impact scientific areas beyond particle physics including neuroscience, epidemiology, and algorithmic fairness, and to foster collaboration between academia and industry. Additionally, the analytical projects of the group, which include statistical applications, machine learning, and analysis preservation tools and techniques, can be extended to fields outside of physics, and young researchers supported under this program will have training in such innovative and cross-cutting techniques. 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.