A novel radio imaging method for physical spectral index modelling

TL;DR

This paper introduces 'forced-spectrum fitting', a new radio imaging deconvolution method that improves spectral accuracy by integrating prior spectral index maps, enhancing calibration and modeling of radio sources.

Contribution

The paper presents a novel deconvolution technique that incorporates pre-existing spectral index maps for more accurate spectral modeling during radio imaging.

Findings

Models with forced-spectrum fitting match spectral index maps accurately.

The method improves calibration quality in radio observations.

Residuals are comparable to existing multi-frequency deconvolution methods.

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.