Enhancing Planck All-Sky Maps through Fourier Transform-Based Smoothing

Abstract

The microwave wavelength range of the sky encompasses a rich variety of astro-physical signals, including emissions from the Galactic interstellar medium and the Cosmic Microwave Background (CMB). The Planck satellite has significantly contributed to capturing these signals, providing a vast dataset in its Public Data Release 3 (PR3). This study focuses on the effective processing of this data, which consists of approximately 12.5 million points, through a binning method that reduces the data to 64,800 points, thus enhancing the efficiency of subsequent analysis. To further improve the signal quality, the Fast Fourier Transform (FFT) is applied, enabling noise reduction and smoothing of the data. The methodology highlights the transformation of intensity measurements to an astrophysically relevant format and the application of locally adaptive filtering techniques in the Fourier domain. The study specifically evaluates the performance of fitting a 2-dimensional Lorentzian model to both binned and FFT-processed maps, revealing that FFT maps demonstrate superior fitting efficiency with lower reduced chi-square values and reduced processing times. These results indicate the efficacy of FFT in enhancing data quality for analyzing large-scale structures within the all-sky map, ultimately facilitating a more accurate understanding of underlying astrophysical phenomena.