Background Mitigation in Dual Phase Xenon Time Projection Chambers

The focus of this thesis is on the identification and removal of background events in the
dual-phase xenon TPC experiments of LUX and LZ. In particular, this effort focuses on a
few hard to classify background types that appear in the traditional WIMP search and
in the EFT search. The first half of the original research presented in this thesis pertains
to backgrounds expected to be present in LZ. This includes multiple scatter events which
only deposit a proportion of their energy in the LXe, and multiple scatter events which only
produce a single ionisation signal. The thesis then goes on to examine how backgrounds
such as these, and other backgrounds occurring close to the edge of the LXe can be reduced
with improved modelling of ionisation electron paths in the LXe, and describes how
this modelling was used by the LZ collaboration. The second half of this thesis focuses
exclusively on these multiple scatter events which only produce a single ionisation signal
but in the context of LUX data. It begins by examining how previous studies have identified
and removed them; then presents a novel method for the identification and removal of these
events using a machine learning algorithm called a BDT. Finally, the thesis concludes after
using data and simulations to compare this new BDT cut against previously developed cuts
for this class of events, and shows this new BDT cut to be the best option for the ongoing EFT
analysis of LUX.