Biparametric Adaptive Filter: detection of compact sources in complex microwave backgrounds
M. L\'opez-Caniego, P. Vielva

TL;DR
The paper introduces the Biparametric Adaptive Filter (BAF), a new analytical tool designed to enhance the detection of compact sources in complex microwave backgrounds like the CMB, outperforming previous methods such as MHW2.
Contribution
The BAF adaptively optimizes its parameters based on background statistics and source profiles, improving detection sensitivity and reducing false positives in microwave sky maps.
Findings
BAF increases the signal-to-noise ratio of detected sources.
BAF detects more sources than MHW2 across all frequencies.
BAF performs better near the galactic plane with fewer spurious detections.
Abstract
In this article we consider the detection of compact sources in maps of the Cosmic Microwave Background radiation (CMB) following the philosophy behind the Mexican Hat Wavelet Family (MHWn) of linear filters. We present a new analytical filter, the Biparametric Adaptive Filter (BAF), that is able to adapt itself to the statistical properties of the background as well as to the profile of the compact sources, maximizing the amplification and improving the detection process. We have tested the performance of this filter using realistic simulations of the microwave sky between 30 and 857 GHz as observed by the Planck satellite, where complex backgrounds can be found. We demonstrate that doing a local analysis on flat patches allows one to find a combination of the optimal scale of the filter R and the index of the filter g that will produce a global maximum in the amplification, enhancing…
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