  We present a novel computational approach for extracting weak signals, whose
exact location and width may be unknown, from complex background distributions
with an arbitrary functional form. We focus on datasets that can be naturally
presented as binned integer counts, demonstrating our approach on the CERN open
dataset from the ATLAS collaboration at the Large Hadron Collider, which
contains the Higgs boson signature. Our approach is based on Gaussian Process
(GP) regression - a powerful and flexible machine learning technique that
allowed us to model the background without specifying its functional form
explicitly, and to separate the background and signal contributions in a robust
and reproducible manner. Unlike functional fits, our GP-regression-based
approach does not need to be constantly updated as more data becomes available.
We discuss how to select the GP kernel type, considering trade-offs between
kernel complexity and its ability to capture the features of the background
distribution. We show that our GP framework can be used to detect the Higgs
boson resonance in the data with more statistical significance than a
polynomial fit specifically tailored to the dataset. Finally, we use Markov
Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of
the extracted Higgs signature.
