Using Genetic Algorithms to Evolve Antenna Gain Patterns with Greater Sensitivity to Ultra-High Energy Neutrinos

Evolutionary algorithms utilize principles of evolution to efficiently determine solutions to defined problems. These algorithms are especially proficient at finding solutions to complex optimization problems that would likely be inaccessible through traditional techniques. The GENETIS Collaboration is developing genetic algorithms (GAs) to design antennas that are more sensitive to ultra-high energy neutrino-induced radio pulses than current detectors. Improving antenna performance is critical because UHE neutrinos are rare, with experiments requiring either massive detector volumes with stations dispersed over hundreds of km, or extraordinarily long livetimes. Optimally performing antennas are imperative to ensuring that these rare UHE neutrino events have the highest possible chance to be detected when they occur. One technique for exploring antenna response with GAs is the Antenna Response Evolutionary Algorithm (AREA), developed by GENETIS. This algorithm evolves antenna gain patterns directly with the aim of determining the optimal antenna response for a specified science goal, independent of design constraints. This research could help quantify the maximum improvement to sensitivity, which can be compared to current design capabilities and inform future improvements. This proceeding will report on advancements to the algorithm, initial results, and planned future improvements and use cases.