Bayesian Analysis of the Radio Signals Produced by Ultra-High-Energy Cosmic Rays
Ryan Thong
Department of Physics & Astronomy
Faculty Supervisor: Oscar Macias
To understand the violent Universe, we analyze signals emitted from the sources of ultra-high-energy cosmic rays (UHECRs) through their interactions with the cosmic microwave background (CMB). With a large-scale radio detection array to capture cosmogenic neutrinos and gamma ray signals, we will be able to trace them back to their origins. However, a challenge arises when quantifying radio signals induced by astroparticles generating cascades. These signals are inherently superposed with irreducible noise, making accurate energy fluence reconstruction particularly difficult, especially at low signal-to-noise ratios (SNR). Conventional noise subtraction methods in the time domain introduce significant biases and underestimate uncertainties, even at high SNR values, often necessitating the imposition of SNR threshold cuts that exclude valuable data. My work aims to resolve these limitations by implementing a Bayesian noise characterization method that enables precise uncertainty estimation and reduces reconstruction bias. Specifically, I focus on fluence estimation in the frequency domain, which enhances the accuracy of signal characterization while eliminating the need for arbitrary SNR cuts.