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February 2022
Alissa Monte (UCSB) “Using Active Vetoes and Hydrogen Doping to Strengthen Dark Matter Discovery Potential with Xenon”
Alissa Monte (UCSB) "Using Active Vetoes and Hydrogen Doping to Strengthen Dark Matter Discovery Potential with Xenon" Abstract: The fact that 85% of the gravitating mass in the universe is invisible is one of the biggest unsolved mysteries in fundamental physics today. If this dark matter was not present, galaxies would fly apart and the universe would not develop the right amount of structure at the right time for you to exist and attend this RPM. Despite its abundance, dark…
Find out more »RPM – Physics Division | Aaron Manalaysay (LBNL) “Xe: The Element Designed to Detect Dark Matter”
Abstract: Despite nearly four decades of direct searches, the identity of dark matter in the universe is still unknown. The hunt for dark matter is especially alluring because, not only is this abundant substance extremely elusive, it also holds promise: we must invoke physics beyond the Standard Model to explain its existence. The search for the WIMP, one of the most popular and well-motivated candidates for dark matter, has been dominated by experiments that utilize liquid xenon. I will explain…
Find out more »Scott Kravitz (LBNL) “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…
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