Dr. Scott Kravitz, Lawrence Berkeley National Laboratory, Berkeley

"Pushing Dark Matter Detection to the Limit with Machine Learning and Solid Xenon"

Abstract: Discovering the particle properties of dark matter is a global undertaking, necessitating many experimental approaches to cover the vast range of possible models. Massive particles such as WIMPs remain leading candidates, and may be within the reach of state-of-the-art direct detection experiments such as the recently-commissioned xenon-based LZ detector. I will present efforts to use machine learning in xenon detectors to improve data quality, reduce backgrounds, make analysis faster and more flexible, and expand the range of physics models that LZ can effectively probe. Looking beyond this generation of experiments, continued improvement in dark matter sensitivity at the current rate is likely to require more creative solutions than simply increasing detector size. In the second part of my talk, I will discuss the possibility of upgrading LZ after its completion, using the novel solid xenon time projection chamber technology I have helped develop, to achieve an experiment where solar neutrinos form the dominant background.