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Chan, Y-D

Publications and source records attributed to Chan, Y-D.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Search for charge non-conservation and Pauli exclusion principle violation with the M AJORANA D EMONSTRATOR

Charge conservation and the Pauli exclusion principle result from fundamental symmetries in the standard model of particle physics, and are typically taken as axiomatic. High-precision tests for small violations of these symmetries could point to new physics. Here we consider three models for violation of these processes, which would produce detectable ionization in the high-purity germanium detectors of the M AJORANA D EMONSTRATOR experiment. Using a 37.5 kg yr exposure, we report a lower limit on the electron mean lifetime, improving the previous best limit for the e → v e $\overline{v_e}$ v e decay channel by more than an order of magnitude. Here, we also present searches for two types of violation of the Pauli exclusion principle, setting limits on the probability of an electron to be found in a symmetric quantum state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Interpretable boosted-decision-tree analysis for the Majorana Demonstrator

The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study reveals the machine's decision-making logic, allowing us to learn from the machine to feedback to the traditional analysis. In this work, we have presented the first machine learning analysis of the data from the Majorana Demonstrator; this is also the first interpretable machine learning analysis of any germanium detector experiment. Two gradient boosted decision tree models are trained to learn from the data, and a game-theory-based model interpretability study is conducted to understand the origin of the classification power. By learning from data, this analysis recognizes the correlations among reconstruction parameters to further enhance the background rejection performance. By learning from the machine, this analysis reveals the importance of new background categories to reciprocally benefit the standard Majorana analysis. This model is highly compatible with next-generation germanium detector experiments like LEGEND since it can be simultaneously trained on a large number of detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗