Wavelets Signal Processing for Energy Reconstruction of Radio Emission of Cosmic Rays

Abstract

Cosmic rays are particles coming from outer space with energies ranging from 108 eV up to beyond 1020 eV. The cosmic ray term is usually referred to as charged particles, such as an atom’s nucleus, but can also be referred to as neutrinos and photons, as they can also reach a high energy regime. When such particles arrive on Earth, they will hadronically interact with the atmosphere’s nuclei, generating a cascade of secondary particles. This chain reaction of particle production is known as the air shower. The observed particles can reach energies far beyond what can be produced in human-made accelerators, becoming a natural source of frontier physics discoveries, such as the origin of ultra-high-energy cosmic rays (UHECRs) and high-energy neutrinos detection [1]. The radio detection of a cosmic ray is a modern, solid, and low-cost technique that uses antennas to detect the electromagnetic component of the air shower [1], the radio emission of cosmic rays. The main challenge for this type of detection is the background present at the experimental site due to human-made radio noise, and the Galactic Gaussian noise [2]. This thesis proposes to explore signal processing techniques in those radio signals. First, the wavelets denoising technique is explored. It is a well-known technique used nowadays to denoise a signal corrupted with noise, whether Gaussian or impulsive. Although the denoising process’s advantage on a signal is straightforward, it is a central point for reconstructing the primary particle energy for the cosmic-ray-induced radio signal scenario. This thesis project proposes using a Stationary Wavelet Transform (SWT) to denoise cosmic-ray-induced radio signals and improve the energy reconstruction of the signal itself. This is expected to generate a better resolution when estimating the primary particle energy. This thesis also proposes using a Discrete Wavelet Transform (DWT) for event selection. Second, this work presents the efficiency of a trigger mechanism developed using the adaptive predictor filter technique. Adaptive filtering belongs to the realm of learning algorithms, so widely used in our daily life when we hear about machine learning, artificial intelligence, pattern recognition, etc. [3]. The trigger mechanism is a central task in radio detection experiments as it selects a cosmic ray-induced signal from all the voltage trace events that reach the antennas. The mechanism is also independent of an external detector, considering only the online temporal series that arrives in the antennas in a simulated data set and noise.