Designing Optimized Antennas with Greater Sensitivity to Ultra-High Energy Neutrinos Using the Nebulous Evolutionary Algorithm Approach
Oral-Virtual · Withdrawn
Abstract
Ultra-high energy (UHE) neutrinos require highly-sensitive antennas for detection, and evolutionary algorithms (EAs) can be used to optimize the design of these antennas. EAs are an optimization approach that mimic the process of biological evolution, continually generating variation in a population and selecting individuals to create the next population until a desired set of solutions is reached. The individuals constructed and evolved by the algorithm are represented by geometric shapes that combine to form the antenna. With the Askaryan Radio Array (ARA) experiment as a baseline, we use EAs to improve upon an antenna design for in-ice neutrino detectors, optimizing for its effective volume. We present results showcasing an 11% increase in simulated sensitivity to UHE neutrinos compared to the ARA design. Future work will continue to increase the complexity of antenna designs and optimize the algorithmic efficiency.
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Publication: Julie Rolla, Bryan Reynolds, Jacob Weiler, Dylan Wells, Max Foreback, Amy Connolly, Emily Dolson, Charles Ofria. Designing Optimized Antennas for Science Applications Using Evolutionary Algorithms. The Interplanetary Network Progress Report, Volume 42-242, pp. 1-29, August 15, 2025.
Presenters
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Christina Shao
- Ohio State University