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Improving HPGe Detector-Grade Crystal Growth for Rare Event Searches Using Machine Learning

ORAL

Abstract

This presentation provides an overview of recent advancements in High Purity Germanium (HPGe) crystal growth at the University of South Dakota (USD). The focus of this research is on the precise characterization of HPGe crystals, addressing critical parameters such as impurity concentration, dislocation density, and diameter control during growth.

Our goal is to enhance the efficiency and performance of HPGe detectors, driven by the demands of rare event searches. These detectors are crucial for probing elusive phenomena such as neutrinoless double-beta decay and dark matter interactions, which require exceptional sensitivity and precision. Achieving this level of performance is contingent upon the growth of high-quality crystals with a homogeneous net impurity concentration ranging from 5×109 to 3×1010 cm−3 .

The most challenging aspect of HPGe crystal growth is consistently achieving a large detector-grade yield. This presentation will detail our recent efforts to apply machine learning tools to optimize crystal quality, aiming to improve consistency in detector-grade production.

Presenters

  • Sanjay Bhattarai

    University of South Dakota

Authors

  • Sanjay Bhattarai

    University of South Dakota

  • Dongming Mei

    University of South Dakota

  • Narayan Budhathoki

    University of South Dakota

  • Kunming Dong

    University of South Dakota

  • Austin Warren

    University of South Dakota