Search

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 Large Underground Xenon (LUX) detector, signatures of low-mass DM interactions would be very low-energy (∼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 1.7-3 with improvement depending heavily on the size of ionization signals. We use the technique on events in an effective 5 tonne·day exposure from LUX's 2013 science operation to place strong limits on low-mass DM particles with masses in the range mχ∈0.15-10  GeV. This machine learning technique is expected to be useful for near-future experiments, such as LUX-ZEPLIN and XENONnT, which hope to perform low-mass DM searches with the stringent background control necessary to make a discovery. [ABSTRACT FROM AUTHOR]

In this paper, we explore beyond the Standard Model top-Higgs Yukawa couplings as a function of a CP-mixing parameter ξt at the 14 TeV HL-LHC in the process pp→thj. We observe that angular variables of the decay products of the top are nontrivially sensitive to ξt. This fact is exploited in a full detector level analysis that employs machine learning techniques to optimize signal sensitivity on a suite of variables, including lepton azimuthal angle. The key result of this study is an improved projected exclusion limit on ξt even when including the realistic effects of detector smearing and a conservative estimate of systematic error. [ABSTRACT FROM AUTHOR]