Speaker
Description
For dual-phase xenon time projection chambers such as LUX, signatures of low-mass DM interactions would be $\sim$keV scatters 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 backgrounds that have long been observed in this class of detector. Noticing backgrounds from the electrodes were particularly prevalent, a machine learning technique based on ionisation pulse shape was developed to identify and reject these events. The technique was shown to improve Poisson limits by a factor of $2$-$7$, and was applied to LUX events in an effective $5$ tonne$\cdot$day exposure to place strong limits on DM with masses $m_{\chi}\in0.15$-$10$ GeV. The 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.
