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Abstract: Abridged We present a new method for detecting and measuring compactsources in conditions of intense, and highly variable, fore-background. Whileall most commonly used packages carry out the source detection over the signalimage, our proposed method builds from the measured image a -curvature- imageby double-differentiation in four different directions. In this way point-likeas well as resolved, yet relatively compact, objects are easily revealed whilethe slower varying fore-background is greatly diminished. Candidate sources arethen identified by looking for pixels where the curvature exceeds, in absoluteterms, a given threshold; the methodology easily allows us to pinpointbreakpoints in the source brightness profile and then derive reliable guessesfor the sources extent. Identified peaks are fit with 2D elliptical Gaussiansplus an underlying planar inclined plateau, with mild constraints on size andorientation. Mutually contaminating sources are fit with multiple Gaussianssimultaneously using flexible constraints. We ran our method on simulatedlarge-scale fields with 1000 sources of different peak flux overlaid on arealistic realization of diffuse background. We find detection rates in excessof 90% for sources with peak fluxes above the 3-sigma signal noise limit; forabout 80% of the sources the recovered peak fluxes are within 30% of theirinput values.



Author: Sergio Molinari, Eugenio Schisano, Fabiana Faustini, Michele Pestalozzi, Anna Maria DiGiorgio, Scige John Liu

Source: https://arxiv.org/







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