Main content start
PhD Defenses


Wed August 2nd 2023, 12:00 - 1:00pm
Varian 355

Ph.D. Candidate:  Tyler Anderson

Research Advisor:  Daniel Akerib and Maria Elena Monzani

Date: August 2nd, 2023

Time: 12:00 pm

Location: Varian 355

Zoom Link:

Zoom Password: email nickswan [at] (nickswan[at]stanford[dot]edu) for password

The LZ Dark Matter WIMP Search and Treatment of Fundamental Signals

The true nature of dark matter is currently one the most prominent mysteries in the field of particle physics; while we can observe its effects gravitationally, we have yet to determine its exact physical properties through non-gravitational means. Out of the broad spectrum of plausible dark matter candidates, one of the most historically favored models is the weakly-interacting massive particle (WIMP). The LUX-ZEPLIN (LZ) dark matter experiment seeks to detect WIMPs through the use of a dual-phase time projection chamber (TPC) filled with a 7 tonne active xenon target. LZ's first WIMP search began in December 2021 and ended in April 2022, after which LZ published world-leading limits on WIMP-nucleon cross-sections for WIMP masses above 9 GeV/c^2. In addition to summarizing the full WIMP search, this talk discusses the development of key efforts including the treatment of fixed and time-varying conditions within both reconstruction and analysis frameworks, studies of high-activity periods likely resulting from fluorescence of the TPC walls, and the development of novel methods to model backgrounds and test analysis cut efficiencies. While this first WIMP search was unblinded, LZ's future WIMP searches will incorporate a form of bias mitigation known as "salting" in which unknown quantities of fake signal events are injected into WIMP-search data. After motivating this choice of bias mitigation, this talk discusses the methods used to reliably model, create, and inject fake yet unidentifiable signal events into LZ's data stream. In contrast to the process of adding known signal events, this talk concludes with results from the application of autoencoders on PMT waveforms to remove unknown backgrounds.