1. A novel radio imaging method for physical spectral index modelling
- Author
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Ceccotti, E., Offringa, A. R., Koopmans, L. V. E., Timmerman, R., Brackenhoff, S. A., Gehlot, B. K., Mertens, F. G., Munshi, S., Pandey, V. N., van Weeren, R. J., Yatawatta, S., Zaroubi, S., Ceccotti, E., Offringa, A. R., Koopmans, L. V. E., Timmerman, R., Brackenhoff, S. A., Gehlot, B. K., Mertens, F. G., Munshi, S., Pandey, V. N., van Weeren, R. J., Yatawatta, S., and Zaroubi, S.
- Abstract
We present a new method, called "forced-spectrum fitting", for physically-based spectral modelling of radio sources during deconvolution. This improves upon current common deconvolution fitting methods, which often produce inaccurate spectra. Our method uses any pre-existing spectral index map to assign spectral indices to each model component cleaned during the multi-frequency deconvolution of WSClean, where the pre-determined spectrum is fitted. The component magnitude is evaluated by performing a modified weighted linear least-squares fit. We test this method on a simulated LOFAR-HBA observation of the 3C196 QSO and a real LOFAR-HBA observation of the 4C+55.16 FRI galaxy. We compare the results from the forced-spectrum fitting with traditional joined-channel deconvolution using polynomial fitting. Because no prior spectral information was available for 4C+55.16, we demonstrate a method for extracting spectral indices in the observed frequency band using "clustering". The models generated by the forced-spectrum fitting are used to improve the calibration of the datasets. The final residuals are comparable to existing multi-frequency deconvolution methods, but the output model agrees with the provided spectral index map, embedding correct spectral information. While forced-spectrum fitting does not solve the determination of the spectral information itself, it enables the construction of accurate multi-frequency models that can be used for wide-band calibration and subtraction., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in MNRAS
- Published
- 2023
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