Abstract: Although the discovery of the Higgs Boson is often referred to as the completion of the Standard Model of Particle Physics, the many outstanding mysteries of our universe indicate that some unknown new physics is awaiting discovery. Machine learning has played an increasingly critical role in searching for this new physics, typically by better separating a physical process of interest (signal) from other Standard Model processes producing similar detector signatures (background). However, we can also cleverly utilize machine learning to better understand these background processes, opening up “impossible” regions of data for analysis. In this talk, I will present two examples of analyses from the ATLAS experiment utilizing machine learning to tackle especially challenging backgrounds. I will also discuss how future advances in machine learning in both data analysis and particle detector hardware will continue to open new avenues for probing for new physics.
Bio: Dr. Rachel Hyneman is currently a postdoctoral researcher working with Dr. Michael Kagan at SLAC National Accelerator Laboratory, where she studies physics at the smallest scales as part of the ATLAS Experiment at CERN. Her research has focused on taking advantage of machine learning techniques to search for evidence of new physics hiding in the behavior of the Higgs Boson, as well as the developing the construction procedure and readout of the upgraded ATLAS Inner Tracker detector for the High-Luminosity LHC program. She earned her PhD in physics from the University of Michigan, Ann Arbor, under the supervision of Dr. Tom Schwarz. Prior to her graduate studies, she earned her bachelors degree in physics with a minor in music from the College of William and Mary in Virginia. Outside of physics, Rachel enjoys playing double bass and venturing to mountains for hiking and skiing.”