Search

$^{180m}$Ta is a rare nuclear isomer whose decay has never been observed. Its remarkably long lifetime surpasses the half-lives of all other known $\beta$ and electron capture decays due to the large K-spin differences and small energy differences between the isomeric and lower energy states. Detecting its decay presents a significant experimental challenge but could shed light on neutrino-induced nucleosynthesis mechanisms, the nature of dark matter and K-spin violation. For this study, we repurposed the MAJORANA DEMONSTRATOR, an experimental search for the neutrinoless double-beta decay of $^{76}$Ge using an array of high-purity germanium detectors, to search for the decay of $^{180m}$Ta. More than 17 kilograms, the largest amount of tantalum metal ever used for such a search was installed within the ultra-low background detector array. In this paper we present results from the first year of Ta data taking and provide an updated limit for the $^{180m}$Ta half-life on the different decay channels. With new limits up to 1.5 x $10^{19}$ years, we improved existing limits by one to two orders of magnitude. This result is the most sensitive search for a single $\beta$ and electron capture decay ever achieved.

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., 13 pages, 9 figures

Neutron captures and delayed decays of reaction products are common sources of backgrounds in ultra-rare event searches. In this work, we studied $^{13}$C($α,n)^{16}$O reactions induced by $α$-particles emitted within the calibration sources of the \textsc{Majorana Demonstrator}. These sources are thorium-based calibration standards enclosed in carbon-rich materials. The reaction rate was estimated by using the 6129-keV $γ$-rays emitted from the excited $^{16}$O states that are populated when the incoming $α$-particles exceed the reaction Q-value. Thanks to the excellent energy performance of the \textsc{Demonstrator}'s germanium detectors, these characteristic photons can be clearly observed in the calibration data. Facilitated by \textsc{Geant4} simulations, a comparison between the observed 6129-keV photon rates and predictions by a TALYS-based software was performed. The measurements and predictions were found to be consistent, albeit with large statistical uncertainties. This agreement provides support for background projections from ($α,n$)-reactions in future double-beta decay search efforts., Published version

The MAJORANA DEMONSTRATOR is sensitive to rare events near its energy threshold, including bosonic dark matter, solar axions, and lightly ionizing particles. In this analysis, a novel training set of low energy small-angle Compton scatter events is used to determine the efficiency of pulse shape analysis cuts, and we present updated bosonic dark matter and solar axion results from an 11.17 kg-y dataset using a 5 keV analysis threshold., Comment: 4 pages, 4 figures. Proceedings of the 16th International Conference on Topics in Astroparticle and Underground Physics (TAUP 2019), September 9-13, 2019, Toyama, Japan