15 citations · 31 across the 7 of their papers we have counts for
7 papers
Deep-Ocean Application-Specific Neutrino Experiment
Takumi Araki, Simran Chauhan, Lyla Choi +17
This report introduces the concept, prototype design, projected costs, and scientific goals of a mobile experiment for detecting geoneutrinos originating from uranium and thorium d…
Towards imaging Earth's large-scale structures by directional geoneutrino detection with Ocean Bottom Detector
Zhihao Xu, Takumi Araki, Simran Chauhan +17
Geoneutrinos, electron antineutrinos produced by radioactive decays of heat-producing elements (HPEs) within the Earth, provide unique insights into Earth's interior and heat budge…
Enhancing Angular Sensitivity of Segmented Antineutrino Detectors for Reactor Monitoring
Brian C. Crow, Max A. A. Dornfest, John G. Learned +4
We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors. The previously developed method for…
Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm
Jeffrey G. Yepez, Jackson D. Seligman, Max A. A. Dornfest +3
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…
Directional Response of Several Geometries for Reactor-Neutrino Detectors
Mark J. Duvall, Brian C. Crow, Max A. A. Dornfest +4
We report simulation studies of six low-energy electron-antineutrino detector designs, with the goal of determining their ability to resolve the direction to an antineutrino source…
HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Benjamin Horowitz, Max Dornfest, Zarija Lukić +1
Generating large volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next generation observations. In this work, we con…
Fast, high-fidelity Lyman forests with convolutional neural networks
Peter Harrington, Mustafa Mustafa, Max Dornfest +2
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we…