Point source detection using the Spherical Mexican Hat Wavelet on simulated all-skyPlanckmaps

Abstract

We present an estimation of the point source (PS) catalogue that could be extracted from the forthcoming ESA Planck mission data. We have applied the Spherical Mexican Hat Wavelet (SMHW) to simulated all‐sky maps that include cosmic microwave background (CMB), Galactic emission (thermal dust, free–free and synchrotron), thermal Sunyaev–Zel'dovich effect and PS emission, as well as instrumental white noise. This work is an extension of the one presented in Vielva et al. We have developed an algorithm focused on a fast local optimal scale determination, that is crucial to achieve a PS catalogue with a large number of detections and a low flux limit. An important effort has been also done to reduce the CPU time processor for spherical harmonic transformation, in order to perform the PS detection in a reasonable time. The presented algorithm is able to provide a PS catalogue above fluxes: 0.48 Jy (857 GHz), 0.49 Jy (545 GHz), 0.18 Jy (353 GHz), 0.12 Jy (217 GHz), 0.13 Jy (143 GHz), 0.16 Jy (100 GHz HFI), 0.19 Jy (100 GHz LFI), 0.24 Jy (70 GHz), 0.25 Jy (44 GHz) and 0.23 Jy (30 GHz). We detect around 27 700 PS at the highest frequency Planck channel and 2900 at the 30‐GHz one. The completeness level are: 70 per cent (857 GHz), 75 per cent (545 GHz), 70 per cent (353 GHz), 80 per cent (217 GHz), 90 per cent (143 GHz), 85 per cent (100 GHz HFI), 80 per cent (100 GHz LFI), 80 per cent (70 GHz), 85 per cent (44 GHz) and 80 per cent (30 GHz). In addition, we can find several PS at different channels, allowing the study of the spectral behaviour and the physical processes acting on them. We also present the basic procedure to apply the method in maps convolved with asymmetric beams. The algorithm takes ∼72 h for the most CPU time‐demanding channel (857 GHz) in a Compaq HPC320 (Alpha EV68 1‐GHz processor) and requires 4 GB of RAM memory; the CPU time goes as O[N italicR oN3/2pix log(Npix)], where Npix is the number of pixels in the map and N italicR o is the number of optimal scales needed.

High-frequency predictions for number counts and spectral properties of extragalactic radio sources. New evidence of a break at mm wavelengths in spectra of bright blazar sources

“…In particular, the model by Toffolatti et al (1998) was capable to give a good fit of ERS number counts of WMAP sources as well, albeit with an offset of a factor of about 0.7 (see, e.g., Bennett et al 2003). Thus, this model was extensively exploited to estimate the radio source contamination of cosmic microwave background (CMB) maps (Vielva et al 2001(Vielva et al , 2003Tucci et al 2004) at cm/mm wavelengths. On the other hand, its very simple assumptions on the extrapolation of source spectra at mm wavelengths as well as new data published in the last ten years make it currently not upof-date for more predictions, although it is still very useful for comparisons -even at ν ≥ 100 GHz -after a simple rescaling (see, e.g., Marriage et al 2011).…”

Matrix Filters for the Detection of Extragalactic Point Sources in Cosmic Microwave Background Images

“…For this test, we use the "Planck Reference Sky" simulations 4 of the Low Frequency Instrument (LFI) Planck channels: 30, 44 and 70 GHz. The simulations include the following astrophysical components: CMB, Galactic synchrotron, free-free and dust emission, thermal Sunyaev-Zel'dovich effect from galaxy clusters, radio galaxies and infrared dusty galaxies.…”

Techniques for Compact Source Extraction in CMB Maps

“…[24,16]). In this section we will focus on one particular wavelet, the Mexican Hat Wavelet (MHW), that has been successfully implemented for the detection of point sources with a Gaussian profile in CMB simulated observations [18,87,89]. The MHW is the second derivative of the Gaussian function (see Fig.…”

“…The MHW technique has been implemented to deal with simulated Planck observations in flat patches of the sky [87] and also on the whole sphere [89]. The procedure to detect point sources on spherical data is very similar to the one outlined before, but, in this case, the Spherical Mexican Hat Wavelet is used to convolve the data, which is given by [64] …”