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Modelling very long baseline interferometric images with cross-entropy global optimization technique


We present a new technique to obtain model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between model and observed images. The model image is constructed by summing N_s elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude and orientation angle of the major axis. We present the results for the fitting of two main benchmark jets: the first one, constructed from three individual Gaussian sources, while the second synthetic image is formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinity signal-to-noise regimes, the background noise chosen to mimic those found in interferometric radio maps. Those images were built to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy of that obtained from the very traditional AIPS task IMFIT when the image is relatively simple (e.g., few number of components). For more complex interferometric maps, our method presented a superior performance in recovering the parameters of the jet components. Besides, our methodology is able to point out quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is also shown and discussed. Our results indicate that our CE model fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though our method is substantially slower than the current model fitting tasks (at least ten thousand slower for a single processor, depending on the number of the sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images construct from bad sampled (u,v) plane. (AU)

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