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Point Source Detection and False Discovery Rate Control on CMB Maps

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arxiv 1902.06636 v1 pith:LR72LU2F submitted 2019-02-18 astro-ph.CO

Point Source Detection and False Discovery Rate Control on CMB Maps

classification astro-ph.CO
keywords mapsfalsepointprocedurediscoverylocalmaximarate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We discuss a new procedure to search for point sources in Cosmic Microwave background maps; in particular, we aim at controlling the so-called False Discovery Rate, which is defined as the expected value of false discoveries among pixels which are labelled as contaminated by point sources. We exploit a procedure called STEM, which is based on the following four steps: 1) needlet filtering of the observed CMB maps, to improve the signal to noise ratio; 2) selection of candidate peaks, i.e., the local maxima of filtered maps; 3) computation of \emph{p-}values for local maxima; 4) implementation of the multiple testing procedure, by means of the so-called Benjamini-Hochberg method. Our procedures are also implemented on the latest release of Planck CMB maps.

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  1. BROOM: a python package for model-independent analysis of microwave astronomical data

    astro-ph.CO 2026-04 unverdicted novelty 4.0

    BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.