Data analysis for the LUX-ZEPLIN dark matter search

Lead Research Organisation: University of Oxford
Department Name: Oxford Physics

Abstract

LUX-ZEPLIN (LZ) is a Dark Matter search experiment that is being assembled and commissioned (2018 - early 2020) at the Sanford Underground Research Facility (SURF) in Lead, South Dakota (USA). The two-phase time projection chamber uses 10 tonnes of liquid xenon and approx. 500 photomultiplier tubes (PMT) to detect extremely rare events of dark matter interactions with regular matter. For the unambiguous detection of the elusive dark matter particle called Weakly Interacting Massive Particle (WIMP) extremely robust analysis procedures, supported by a rigorous detector calibration programme, are required. Dark matter is cited in many different contexts, often also outside core astro particle physics. Dark matter is a priority topic in the roadmaps of astro particle and particle physics, and their funding agencies. The level of support reflects the general and broad interest in the topic not only by experts but also the wider public. LZ is the current flagship experiment of the UK in the area of direct dark matter searches. The sensitivity reach of LZ has the potential to produce a world-leading result, which will be transformational in the case of a WIMP discovery. Even if a null result is returned, the additional sensitivity reach of LZ will cover a significantly enlarged parameter space compared to predecessors. Rare-event experiments require exquisitely a well-calibrated detector as well as extremely accurate simulations tuned on existing calibration data, to model the detector response. The calibration data will provide a rich repository of detector events, representing an excellent training set for the data extraction and reduction software. The algorithms performing the identification of individual photons and their attribution to scintillation / ionization events will have to be very robust. A crucial step towards this is the extraction and identification of single photons from the pulse trains that the digitizers record. Algorithms to do this exist and have been tested at a level suitable for past and running experiments, however, further improvements are possible by incorporating new techniques. The main aim of this project is to optimize elements of the data analysis chain in LZ. For this to succeed, detailed simulations will be carried out to provide an optimal understanding of the detector response to calibration sources. This is in preparation of the experiment using a suite of internally dispersed (83mKr, 131Xe, 220Rn and CH4 labelled tritium) and externally deployed sealed neutron (AmLi, 205,206BiBe, 252Cf, 88YeBe) and gamma (57Co, 22Na, 133Ba, 228Th) radiation sources to provide high statistics calibration of the response to background events and WIMP signal. The project objective is to simulate events from neutron and gamma sources used in the LZ experiment and to reconstruct events' position and energy spectra, which is a key part of the detector calibration. Improving on photon detection, reconstruction and triggering effort will involve generating simulations of particular events in a xenon time projection chamber (re-using calibration events, for example), events from data recorded in previous experiments and using all possible information that is available in order to provide and validate suitably enhanced algorithms.The novel aspect in the approach in this thesis is the exploration of the benefit that developing and applying artificial intelligence, e. g. machine-learning, may have. If successful, this would be a major step forward towards dealing with the ever increasing sophistication of dark matter detectors and the data these produce.
The project is carried out within the LZ collaboration of which Oxford is a member. The collaboration comprises (in 2018) 38 research groups with over 250 members. The project is computer based, uses GEANT-based simulations, the ROOT data analysis package and the LZAp analysis framework.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/S505845/1 01/10/2018 30/09/2022
2115721 Studentship ST/S505845/1 01/10/2018 31/12/2022 Eilish Kieran Gibson