Improving sensitivity to low-mass dark matter in LUX using a novel
  electrode background mitigation technique
  This paper presents a novel technique for mitigating electrode backgrounds
that limit the sensitivity of searches for low-mass dark matter (DM) using
xenon time projection chambers. In the LUX detector, signatures of low-mass DM
interactions would be very low energy ($\sim$keV) scatters in the active target
that ionize only a few xenon atoms and seldom produce detectable scintillation
signals. In this regime, extra precaution is required to reject a complex set
of low-energy electron backgrounds that have long been observed in this class
of detector. Noticing backgrounds from the wire grid electrodes near the top
and bottom of the active target are particularly pernicious, we develop a
machine learning technique based on ionization pulse shape to identify and
reject these events. We demonstrate the technique can improve Poisson limits on
low-mass DM interactions by a factor of $2$-$7$ with improvement depending
heavily on the size of ionization signals. We use the technique on events in an
effective $5$ tonne$\cdot$day exposure from LUX's 2013 science operation to
place strong limits on low-mass DM particles with masses in the range
$m_{\chi}\in0.15$-$10$ GeV. This machine learning technique is expected to be
useful for near-future experiments, such as LZ and XENONnT, which hope to
perform low-mass DM searches with the stringent background control necessary to
make a discovery.
