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Othman, G.

Publications and source records attributed to Othman, G..

5 records
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DOE OSTI2024

Exotic Dark Matter Search with the Majorana Demonstrator

With excellent energy resolution and ultralow-level radiogenic backgrounds, the high-purity germanium detectors in the enable searches for several classes of exotic dark matter (DM) models. In this work, we report new experimental limits on keV-scale sterile neutrino DM via the transition magnetic moment from conversion to active neutrinos ν s → ν a . We report new limits on fermionic dark matter absorption ( χ + A → ν + A ) and sub-GeV DM-nucleus 3 → 2 scattering ( χ + χ + A → ϕ + A ), and new exclusion limits for bosonic dark matter (axionlike particles and dark photons). These searches utilize the (1–100)-keV low-energy region of a 37.5-kg y exposure collected by the between May 2016 and November 2019 using a set of Ge 76 -enriched detectors whose surface exposure time was carefully controlled, resulting in extremely low levels of cosmogenic activation. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Charge trapping correction and energy performance of the Majorana Demonstrator

P-type point contact (PPC) high-purity germanium detectors are an important technology in astroparticle and nuclear physics due to their superb energy resolution, low noise, and pulse shape discrimination capabilities. Analysis of data from the Majorana Demonstrator, a neutrinoless double-β decay experiment deploying PPC detectors enriched in 76 Ge, has led to several novel improvements in the analysis of PPC signals. Here, in this work we discuss charge trapping in PPC detectors and its effect on energy resolution. Small dislocations or impurities in the crystal lattice result in trapping of charge carriers from an ionization event of interest, attenuating the signal, and degrading the measured energy. We present a modified digital pole-zero correction to the signal energy estimation that counters the effects of charge trapping and improves the energy resolution of the Majorana Demonstrator by approximately 30 % to around 2.4 keV full width at half-maximum at 2039 keV, the 76 Ge Q value. An alternative approach achieving similar resolution enhancement is also presented.

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↗