Point source detection and extraction from simulated Planck TOD using optimal adaptive filters

摘要

Wavelet-related techniques have proven useful in the processing and analysisof one and two dimensional data sets (spectra in the former case, images in thelatter). In this work we apply adaptive filters, introduced in a previous work(Sanz et al. 2001), to optimize the detection and extraction of point sourcesfrom a one-dimensional array of time-ordered data such as the one that will beproduced by the future 30 GHz LFI28 channel of the ESA Planck mission. At a$4sigma$ detection level 224 sources over a flux of 0.88 Jy are detected witha mean relative error (in absolute value) of 21% and a systematic bias of-7.7%. The position of the sources in the sky is determined with errorsinferior to the size of the pixel. The catalogue of detected sources iscomplete at fluxes $geq$ 4.3 Jy. The number of spurious detections is lessthan a 10% of the true detections. We compared the results with the onesobtained by filtering with a Gaussian filter and a Mexican Hat Wavelet of widthequal to the scale of the sources. The adaptive filter outperforms the otherfilters in all considered cases. We conclude that optimal adaptive filters arewell suited to detect and extract sources with a given profile embedded in abackground of known statistical properties. In the Planck case, they could beuseful to obtain a real-time preliminary catalogue of extragalactic sources,which would have a great scientific interest, e. g. for follow-up observations.