A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation
Authors:
Sarah E. Wessinger,
Leslie N. Smith,
Jacob Gull,
Jonathan Gehman,
Zachary Beever,
Andrew J. Kammerer
Abstract:
Accurately estimating the refractive environment over multiple frequencies within the marine atmospheric boundary layer is crucial for the effective deployment of radar technologies. Traditional parabolic equation simulations, while effective, can be computationally expensive and time-intensive, limiting their practical application. This communication explores a novel approach using deep neural ne…
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Accurately estimating the refractive environment over multiple frequencies within the marine atmospheric boundary layer is crucial for the effective deployment of radar technologies. Traditional parabolic equation simulations, while effective, can be computationally expensive and time-intensive, limiting their practical application. This communication explores a novel approach using deep neural networks to estimate the pattern propagation factor, a critical parameter for characterizing environmental impacts on signal propagation. Image-to-image translation generators designed to ingest modified refractivity data and generate predictions of pattern propagation factors over the same domain were developed. Findings demonstrate that deep neural networks can be trained to analyze multiple frequencies and reasonably predict the pattern propagation factor, offering an alternative to traditional methods.
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Submitted 4 September, 2025; v1 submitted 21 May, 2025;
originally announced May 2025.
TRANSLATE -- A Monte Carlo Simulation of Electron Transport in Liquid Argon
Authors:
Zach Beever,
David Caratelli,
Angela Fava,
Francesco Pietropaolo,
Francesca Stocker,
Jacob Zettlemoyer
Abstract:
The microphysics of electron and photon propagation in liquid argon is a key component of detector design and calibrations needed to construct and perform measurements within a wide range of particle physics experiments. As experiments grow in scale and complexity, and as the precision of their intended measurements increases, the development of tools to investigate important microphysics effects…
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The microphysics of electron and photon propagation in liquid argon is a key component of detector design and calibrations needed to construct and perform measurements within a wide range of particle physics experiments. As experiments grow in scale and complexity, and as the precision of their intended measurements increases, the development of tools to investigate important microphysics effects impacting such detectors becomes necessary. In this paper we present a new time-domain Monte Carlo simulation of electron transport in liquid argon. The simulation models the TRANSport in Liquid Argon of near-Thermal Electrons (TRANSLATE) with the aim of providing a multi-purpose software package for the study and optimization of detector environments, with a particular focus on ongoing and next generation liquid argon neutrino experiments utilizing the time projection chamber technology. TRANSLATE builds on previous work of Wojcik and Tachiya, amongst others, introducing additional cross-section processes up to ionization, thus modeling the full range of drift electron scattering interactions. The simulation is validated by benchmarking its performance with swarm parameters from data collected in experimental setups operating in gas and liquid.
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Submitted 4 January, 2024; v1 submitted 22 November, 2022;
originally announced November 2022.