"The LUX-ZEPLIN Outer Detector Performance and Position Reconstruction with Machine Learning Algorithms" Tea Hall, University of Liverpool, on behalf of the LUX-ZEPLIN Collaboration

The LUX-ZEPLIN (LZ) dark matter experiment employs a 7-tonne liquid xenon dual phase time projection chamber (TPC), with the primary goal to detect nuclear recoils from Weakly Interacting Massive Particles (WIMPs). To claim a discovery, the neutron background must be maximally reduced, as neutron single scatters produce nuclear recoil signals which are indistinguishable from WIMPs. The LZ Outer Detector (OD) plays a vital role in vetoing neutrons through the detection of TPC-OD coincident signals. The OD, which surrounds the TPC, consists of 17-tonnes of gadolinium-loaded liquid scintillator distributed across segmented acrylic tanks, with further shielding provided by the encasing water tank. Signals in the OD are detected by 120 inward-facing Hamamatsu R5912 photomultiplier tubes. To improve constraints on the neutron background rate, position reconstruction must be implemented in the OD, ensuring that coincident neutron signals are spatially correlated. 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.