We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure 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.

United States

Abhijith Gandrakota

Amit Lath

Alexandre Morozov

Sindhu Murthy

Model selection and signal extraction using Gaussian Process regression

The CMS phase II track trigger may allow for a displaced dimuon vertex trigger with qualitatively lower <math><msub><mi>p</mi><mi>T</mi></msub></math> thresholds than existing dimuon triggers. With this technique, we show that the CMS reach for exotic <math><mi>B</mi></math>-meson decays involving a displaced dimuon resonance, such as a light, Higgs-mixed scalar, can be competitive with that of LHCb and Belle II.

Jared A. Evans

CERN

Simon Knapen

Hardik Routray

Searching for exotic <math><mi>B</mi></math>-meson decays enabled by the CMS L1 track trigger