arXiv subjects

Jeffrey G. Yepez

Publications and source records attributed to Jeffrey G. Yepez.

4 recordsLinked to original sources

arXiv2026

Deep-Ocean Application-Specific Neutrino Experiment

This report introduces the concept, prototype design, projected costs, and scientific goals of a mobile experiment for detecting geoneutrinos originating from uranium and thorium decay chains in the Earth's mantle. This will constrain the planet's radiogenic heat production and unearth its geochemical makeup. This design of a deep-ocean mobile neutrino experiment, which is not mirrored by any active or planned experiments, supports physics and geoscience's goal of multi-modal data on the Earth's internal composition and structure. Based on geoscientific studies, this design is expected to achieve a 50--100-fold reduction in crustal background compared to similarly sized continental detectors, thereby enabling direct measurements of mantle geoneutrinos. The multiple stereoscopic projections enabled by the detector's unique mobility can map spatial variations in heat-producing elements within the mantle. Beyond discussing the design, we report on our collaboration's most recent hardware developments in the active prototyping of this detector. We briefly highlight the potential multiuse and interdisciplinary nature of this detector.

physics.ins-det↗

arXiv2026

Enhancing Angular Sensitivity of Segmented Antineutrino Detectors for Reactor Monitoring

We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors. The previously developed method for quantifying directionality in monolithic and segmented detectors may be ambiguous in methodology. In this paper, we present a directionality algorithm and include error analysis. We have developed an algorithm based on a measure of ``distance'' between two matrices. We report findings for our research in reactor-antineutrino directionality, and emphasize that the algorithm has broad applications whenever one desires computationally efficient 2D pattern-matching. We treat data from Lithium-6-doped detector segments in the form of a matrix. The validation of our algorithm boils down to comparing a Monte Carlo generated ``empirical'' data set to a simulated data set. The empirical data set is generated for a particular orientation of the neutrino beam. We identify an optimal segmentation scale in the low-count regime. We also discuss the shortcomings of the conventional method and how this knowledge can be applied to segmented detectors, hybrid designs, and generalized validation, agnostic to the physics of detector design.

hep-ex↗

arXiv2026

Joint Diagnostics of Circumsolar Sky Brightness Using Coronagraphic Measurements and Aerosol Optical Inversions at Mauna Loa

Atmospheric aerosols strongly influence daytime sky quality for solar coronal imaging, yet few studies directly link aerosol properties and sky-brightness measurements within ~2° of the Sun. Here we compare externally occulted coronagraphic measurements of near-Sun radiance with aerosol-constrained inferences derived from direct-Sun and sky photometry. Our analysis focuses on Mauna Loa Observatory, a well-characterized high-altitude site for atmospheric and solar observations. We present coronagraphic measurements of near-Sun radiance at 1.54 +/- 0.77° from solar disk center acquired between 2006 and 2007 by an ATST Sky Brightness Monitor (SBM). These data are directly compared with circumsolar radiances inferred at 1.54° using AERONET almucantar measurements and aerosol optical retrievals. We find quantitative agreement between these two approaches, enabling extension to multi-decadal analyses of circumsolar radiance and its relationship to aerosol properties and related proxies (e.g., the Angstrom exponent) using AERONET data from 2000-2025. Near-Sun radiances are expressed relative to the solar disk-center radiance, facilitating direct comparison with related studies. Finally, we synthesize physically based true-color images of the circumsolar sky under representative aerosol conditions as an observational aid, in part to illustrate that visually enhanced solar aureoles do not necessarily imply poor infrared coronal observing conditions. This methodology provides an extended framework for assessing daytime coronal sky quality at existing and future observing sites.

astro-ph.SR↗

arXiv2026

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

physics.data-an↗