Statistical methods for Direct Dark Matter Searches with LZ

Profile likelihood ratio (PLR) analyses are utilized in many physics searches to quantify the certainty of an observation, or set a limit. Over the lifetime of an experiment, the software to complete such searches evolves, and needs be both maintained and operated for the successful completion of physics analyses. In particular, searches that look at effective field theory operators usually must run this limit code 28 times, compared to the 2 times for the basic spin- independent and dependent cases. Thus the efficiency of such code is of greater import in such studies. This student will assist in the maintenance, evaluation, and use of the profile likelihood analysis code in LZ, including options in both ROOT?C+ and python. The LZ detector intends to be the most well understood dark matter detector built to date, as it continues in a technology that has evolved in multiple iterations. This allows the limit setting code to be increasingly specific in what nuisance parameters are used, what their uncertainties are, and how detector parameters evolve over the lifetime of the experiment. Incorporating such levels of detail into the PLR, while still allowing for reasonable computation time, will be the primary activity of this project. The student will be trained during initial science runs of the experiment, and will take a leading role in running the PLR code on later science runs.

The student will have some flexibility in choosing other analysis tasks dependent on their interests and the collaboration needs. As background and signal models, calibrations, and detector livetime all feed directly into the PLR operation, there are tight logical ties to almost any task a student self selects. With the focus on effective field theory models, where the search is extended to higher energy nuclear recoils, there are new backgrounds (multi-scatter single-ionization events) and detector effects (PMT saturation) that must be taken into account, and new attention to data quality cuts. For instance, and energy dependent fiducial cut to remove backgrounds from the walls of the detector will need to be established to maximize sensitivity beyond the standard SI WIMP search.

The student will also gain hardware experience with detector R&D for the next generation of direct dark matter experiments, working on tasks as needed within Oxford's expertise in sensors, electronics, high voltage, as well as readout, cabling, radiopurity and cleanliness. This will include vacuum and gas system handling, and with the larger LZ collaboration, cryogenics and photosensors.