Applications of Evolutionary Algorithms in Ultra-High Energy Neutrino Astrophysics

Created by W.Langdon from gp-bibliography.bib Revision:1.8311

@PhdThesis{Rolla_Dissertation,

• author = "Julie Rolla",
• title = "Applications of Evolutionary Algorithms in Ultra-High Energy Neutrino Astrophysics",
• school = "Ohio State University",
• year = "2021",
• address = "USA",
• keywords = "genetic algorithms, genetic programming, Karoo GP, Python, ultra-high energy neutrinos, Askaryan Radio Array, evolutionary algorithms, antenna optimization, Smith Chart, OSU GENETIS, PAEA Loop, AREA Loop, Remcom XFdtd",
• URL = "http://rave.ohiolink.edu/etdc/view?acc_num=osu163668789451345",
• URL = "https://etd.ohiolink.edu/acprod/odb_etd/ws/send_file/send?accession=osu163668789451345&disposition=inline",
• size = "224 pages",
• abstract = "The discovery of ultra-high energy (UHE) neutrinos provides a promising tool to probe the highly energetic hadronic accelerators at the far reaches of our universe with little deflection or interaction in their travels through the cosmos. However, detecting these neutrinos is rare and requires expansive detectors and medium, optimized hardware, and extensive analysis. This thesis explores potential applications for evolutionary algorithms to improve the experimental design and analysis of UHE neutrino experiments.

  The first project describes the use of genetic algorithms for designing antennas with better sensitivities to ultra-high energy neutrino-induced radio pulses than current designs. This thesis describes the history and work of the Genetically Evolving NEuTrIno TeleScopes (GENETIS) project. First, initial projects and exploratory GAs are described. Second, a discussion is given on the evolution of increasingly complex antenna designs optimized to improve the detection of UHE neutrinos. Finally, a project to optimize antenna response patterns are evolved for a given array geometry is presented.

  The second project described is an investigation using genetic programming to distinguish signals from instrumental or environmental noise triggers. Using Karoo GP, a genetic programming software suite, we present a study using genetic programming to classify signal and background in ARA data. In addition to variable exploration and feature extraction, coherently summed waveforms (CSWs) are also analyzed.

  Evolutionary algorithms are powerful techniques that are underused in astro-physics. With the potential to improve experimental design and analysis techniques, these algorithms could help lead the search in UHE neutrino detection and the field of multi-messenger astronomy.",

• notes = "id: osu163668789451345

  Supervisor: Amy Connolly",

}

Genetic Programming entries for Julie A Rolla

Citations