Speaker
Description
The LUX-ZEPLIN (LZ) dark matter direct detection experiment employs a 7-tonne dual phase xenon time projection chamber (TPC), with the primary goal to detect nuclear recoils from Weakly Interacting Massive Particles (WIMPs). The neutron background must be minimised in LZ, as neutron single scatters produce nuclear recoil signals which are indistinguishable from WIMP interactions. The LZ Outer Detector (OD) plays a crucial role in vetoing neutrons through the detection of coincident signals between the TPC and OD. The OD, which surrounds the TPC, consists of 17-tonnes of radiopure gadolinium-loaded liquid scintillator distributed across 10 segmented acrylic tanks, with further shielding provided by the encasing ultra-pure water tank. Signals in the OD are detected by 120 inward-facing Hamamatsu R5912 photomultiplier tubes. Position reconstruction in the OD could help improve constraints on the neutron background rate and reduce the detector dead-time by ensuring that coincident neutron signals are spatially correlated with TPC signals. To this effect, convolutional neural networks (CNN) have been developed for position reconstruction in the LZ OD. This talk will provide an overview of the OD and its performance, and the development of CNN algorithms for improved OD position reconstruction