May 15 – 21, 2022
America/New_York timezone

The First Machine Learning Analysis for Majorana Demonstrator Experiment

May 16, 2022, 6:00 PM
1h
Arcade Ballroom: Hallway

Arcade Ballroom: Hallway

Poster Double beta decay: experiments and nuclear matrix elements Poster Session

Speaker

Aobo Li (UNC Chapel Hill)

Description

Neutrinoless Double Beta Decay(0νββ) is one of the major research interests in neutrino physics. The discovery of 0νββ would answer persistent puzzles in the standard model. In the search of 0νββ, The Majorana Demonstrator experiment retains the best energy resolution and one of the lowest backgrounds at the region of interest. Data is collected from 63.9kg of enriched and natural Germanium-76 crystals operating as detector arrays of both p-type point-contact detectors and inverted-coaxial point-contact detectors. Several pulse shape parameters have been developed to reject backgrounds. To collectively analyze those pulse shape parameters, we developed a fully interpretable boosted decision tree (BDT) model that has the potential to outperform the traditional selection criteria. By interpreting the BDT, we find that it uses parameter correlation to identify known background event categories that have required supplementary cuts in the traditional analysis. We demonstrated that the BDT analysis and traditional analysis benefit each other in a reciprocal way. This material is supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, the Particle Astrophysics and Nuclear Physics Programs of the National Science Foundation, and the Sanford Underground Research Facility.

Primary author

Aobo Li (UNC Chapel Hill)

Presentation materials