Wavelets Applied to the Detection of Point Sources of UHECRs

TL;DR

This paper investigates how wavelet-based smoothing techniques can enhance the detection of point sources in ultra-high-energy cosmic ray maps, comparing their effectiveness to Gaussian smoothing.

Contribution

It introduces a method using Mexican hat wavelets for analyzing cosmic ray arrival directions and evaluates their performance with simulated data and realistic detector acceptance.

Findings

Wavelet smoothing improves signal-to-noise ratio detection.

Wavelets outperform Gaussian kernels in certain anisotropy scenarios.

Analysis tailored for Pierre Auger Observatory conditions.

Abstract

In this work we analyze the effect of smoothing maps containing arrival directions of cosmic rays with a gaussian kernel and kernels of the mexican hat wavelets of orders 1, 2 and 3. The analysis is performed by calculating the amplification of the signal-to-noise ratio for several anisotropy patterns (noise) and different number of events coming from a simulated source (signal) for an ideal detector capable of observing the full sky with equal probability. We extend this analysis for a virtual detector located within the array of detectors of the Pierre Auger Observatory, considering an acceptance law.