LZ detector performance evaluation and event characterization

Noah will be working on the LZ experiment which will run through 2028.

His LZ analysis will consist of two primary tasks: evaluating the LZ detector performance against design goals, and understanding the population distribution of events in the detector.

In evaluating detector performance, especially over the years of LZ-runtime, the student will particularly study the xenon flow, charge and background event accumulation on the PTFE walls, resistor performance, and a variety of other topics that can inform the XLZD fieldcage design and materials handling.

These tasks allow the student to develop experience with the full complement of LZ software tools: Monte Carlo simulations, event reconstruction, calibration dataset use, high level event analysis, and final statistical inference results.

Following these initial studies that may be lightly updated with new LZ data, the student will embark upon his more targeted thesis topic. LZ analyses to date have focused upon physics events: calibrations, backgrounds, and signals in the WIMP search ROI.

This student project will expand our understanding of a wider range of triggered events that may be due to a variety of detector effects to 1) build models of these events and 2) cuts against them for wider physics ROIs than those primarily studied by LZ, and 3) predict what these event populations may be in a future XLZD experiment.

The project will support the future LZ low mass dark matter searches by aiding event reconstruction for more complicated waveforms, and by working to reduce accidental events in the final dataset.

The Oxford group has had a significant effort put into understanding accidental events through the history of LZ, and Noah will continue the efforts in this important area educated by the past efforts, and with a larger dataset at hand, taken with a variety of detector conditions to better segregate different causes of accidental events and identify different parameter space boundaries that distinguish these different populations, a task only available with our longer datasets that hold more of these events.

This work will benefit from the improvements to the LZ signal simulations carried out by another Oxford student, and efforts led by a postdoc at Oxford to lower our analysis threshold with improved reconstruction below threshold.

These studies of detector effects and accidental populations will play a role both in the final LZ data analysis and in plans for XLZD's design and its projected sensitivity.