arXiv subjects

Daniel Behrend-Uriarte

Publications and source records attributed to Daniel Behrend-Uriarte.

2 recordsLinked to original sources

arXiv2025

Improved limit on the effective electron neutrino mass with the ECHo-1k experiment

The effective electron neutrino mass can be determined by analyzing the endpoint region of the $^{163}$Ho electron capture spectrum, provided a measurement with high energy resolution and high statistics using calorimetric techniques. Here, the Electron Capture in $^{163}$Ho collaboration, ECHo, presents an analysis of the most precise $^{163}$Ho spectrum currently available, obtained with the ECHo-1k experiment and comprising about 200 million events. A very low background rate of $B=9.1(1.3)\times 10^{-6}$ /eV/pixel/day was achieved allowing for a reliable analysis of the endpoint region. The derived endpoint energy $Q = 2862(4)$ eV is in excellent agreement with the one independently determined via Penning-trap mass spectrometry of $Q=2863.2(6)$ eV [1]. The upper limit of the effective electron neutrino mass is improved by almost a factor 2 compared to the lowest current value [2], reaching $m_{ν_\mathrm{e}} < 15 $ eV/c${^2}$ (90\% credible interval).

hep-ex↗

arXiv2025

Learning Distances from Data with Normalizing Flows and Score Matching

Density-based distances (DBDs) provide a principled approach to metric learning by defining distances in terms of the underlying data distribution. By employing a Riemannian metric that increases in regions of low probability density, shortest paths naturally follow the data manifold. Fermat distances, a specific type of DBD, have attractive properties, but existing estimators based on nearest neighbor graphs suffer from poor convergence due to inaccurate density estimates. Moreover, graph-based methods scale poorly to high dimensions, as the proposed geodesics are often insufficiently smooth. We address these challenges in two key ways. First, we learn densities using normalizing flows. Second, we refine geodesics through relaxation, guided by a learned score model. Additionally, we introduce a dimension-adapted Fermat distance that scales intuitively to high dimensions and improves numerical stability. Our work paves the way for the practical use of density-based distances, especially in high-dimensional spaces.

cs.LG↗