Effect of Fourier filters in removing periodic systematic effects from CMB data

TL;DR

This paper evaluates the effectiveness of high-pass Fourier filters in removing periodic systematic effects from CMB data and compares their performance with destriping methods, finding limited advantages for Fourier filters in complex signals.

Contribution

It provides a comparative analysis of Fourier filtering and destriping techniques for systematic noise removal in CMB datasets, highlighting the limitations of Fourier filters in realistic scenarios.

Findings

Fourier filters require normalization after application.

Filtering plus destriping yields similar results to destriping alone.

Fourier filters show limited usefulness for complex CMB signals.

Abstract

We consider the application of high-pass Fourier filters to remove periodic systematic fluctuations from full-sky survey CMB datasets. We compare the filter performance with destriping codes commonly used to remove the effect of residual 1/f noise from timelines. As a realistic working case, we use simulations of the typical Planck scanning strategy and Planck Low Frequency Instrument noise performance, with spurious periodic fluctuations that mimic a typical thermal disturbance. We show that the application of Fourier high-pass filters in chunks always requires subsequent normalisation of induced offsets by means of destriping. For a complex signal containing all the astrophysical and instrumental components, the result obtained by applying filter and destriping in series is comparable to the result obtained by destriping only, which makes the usefulness of Fourier filters questionable for removing this kind of effects.