Search NASA⌕ Search

Engineering topics

Martini, A.

Publications and source records attributed to Martini, A..

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

First Search for Dark Photon Dark Matter with a madmax Prototype

We report the first result from a dark photon dark matter search in the mass range from 78.62 to 83.95 μ eV / c 2 with a dielectric haloscope prototype for (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons would convert to detectable photons within a stack consisting of three sapphire disks and a mirror. The emitted power of this system is received by an antenna and successively digitized using a low-noise receiver. No significant signal attributable to dark photons has been observed above the expected background. Assuming unpolarized dark photon dark matter with a local density of ρ χ = 0.3 G eV cm 3 we exclude a dark photon to photon mixing parameter χ > 2.7 × 10 − 12 over the full mass range and χ > 1.1 × 10 − 13 at a mass of 80.57 μ eV / c 2 with a 95% confidence level. This is the first physics result from a prototype and exceeds previous constraints on χ in this mass range by up to almost three orders of magnitude. Published by the American Physical Society 2025

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

B-flavor tagging at Belle II

We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom (B) mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic B decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of 62.8fb -1 , collected at the Υ(4S) resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be ε eff = (30.0 ± 1.2(stat) ± 0.4(syst))% for a category-based algorithm and ε eff = (28.8 ± 1.2(stat) ± 0.4(syst))% for a deep learning-based algorithm.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Punzi-loss: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗