Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of 76Ge to Excited States of 76Se

The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76Ge can decay into three E.S.s of 76Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 2.1) ⋅ 1024 y. In particular, for the 2v transition to the first 0+ E.S. of 76Se, a lower half-life limit of 0.68 ⋅ 1024 at 90% CL was achieved

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion (ADC) cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the Majorana Demonstrator 76Ge neutrinoless double-beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearities. A simple measurement protocol was developed that did not require sophisticated equipment or lengthy data-taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearities by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value

Exotic Dark Matter Search with the Majorana Demonstrator

With excellent energy resolution and ultralow-level radiogenic backgrounds, the high-purity germanium detectors in the Majorana Demonstrator 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 Demonstrator between May 2016 and November 2019 using a set of ^{76}Ge-enriched detectors whose surface exposure time was carefully controlled, resulting in extremely low levels of cosmogenic activation

Synthesis of 2-azidoethyl α-d-mannopyranoside orthogonally protected and selective deprotections

4 páginas, 1 figura, 2 esquemas.We present the synthesis of a fully orthogonally protected mannosyl glycoside 1 and the corresponding methods for selective deprotections. Mannosyl glycoside 1 contains a functionalized linker at the anomeric position to allow for the attachment of carbohydrate units to scaffolds in order to prepare carbohydrate multivalent systems.We would like to thank FIS (PI030093), for financial supportPeer reviewe

Measurement of hadron and lepton-pair production at 161 GeV<root s<172 GeV at LEP

Contains fulltext : 26256.pdf (publisher's version ) (Open Access

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 (HPGe) detectors. 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 feed back to the traditional analysis. In this work, we present 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