Fast and Flexible Analysis of Direct Dark Matter Search Data with
  Machine Learning
  We present the results from combining machine learning with the profile
likelihood fit procedure, using data from the Large Underground Xenon (LUX)
dark matter experiment. This approach demonstrates reduction in computation
time by a factor of 30 when compared with the previous approach, without loss
of performance on real data. We establish its flexibility to capture non-linear
correlations between variables (such as smearing in light and charge signals
due to position variation) by achieving equal performance using pulse areas
with and without position-corrections applied. Its efficiency and scalability
furthermore enables searching for dark matter using additional variables
without significant computational burden. We demonstrate this by including a
light signal pulse shape variable alongside more traditional inputs such as
light and charge signal strengths. This technique can be exploited by future
dark matter experiments to make use of additional information, reduce
computational resources needed for signal searches and simulations, and make
inclusion of physical nuisance parameters in fits tractable.
