data using the [Ws-Clean]{} algorithm introduced by @Offringa2014, specifically its multi-scale version [@Offringa2017]. The multi-scale [Ws-Clean]{} is an advanced deconvolution algorithm with a multi-scale, multi-frequency capability [@Offringa2017]. Another advantage of the [Ws-Clean]{} as opposed to the [Casa]{} implementation is the channel-dependent deconvolution threshold. As the noise level changes appreciably with frequency due to atmospheric lines, this prevents us from introducing noise-peaks from the affected channels into the reconstructed images.

For the line imaging, we first subtract the continuum by linearly interpolating the line-free channels in SPWs 1, 2, and 3 and subtract the continuum slope from the line-containing channels. The continuum channels overlapping with the atmospheric lines were flagged before the continuum subtraction. For the continuum imaging, we discard the entire SPW 0 and the line-containing channels in SPW1, as well as the channels affected by the atmospheric feature around 496.35 GHz.

The data were deconvolved on a sky-plane grid of 1024 $\times$ 1024 5-mas pixels (total FoV size = 5.115$\times$5.115 arcsec), using natural weighting. We use the automatic SNR-based masking, with `auto-mask` SNR threshold of 2.

For consistency, we re-image the CO(3–2) data of @CR18 using the exactly same procedure as for the \[\] data; these result in minor 