our data cubes. This ensures that the smoothed spectrum describes the large-scale shape of the baseline only. In Figure 2 we show the shape of the filter and demonstrate the convolution effect on simple functions, including the presence of edge effects. Within our analysis range of $\pm$500 km/s around the spectrum centre (channels 34 to 114), the S-G filter conserves the shape of the functions with a maximum deviation of less than 15%.

Before convolving the [[H]{}[<span style="font-variant:small-caps;">i</span> ]{}]{}spectra with the S-G filter we mask channels that belong to one of the [[H]{}[<span style="font-variant:small-caps;">i</span> ]{}]{}detections presented in Paper I (see also Sec. 3.2 below). We refer to these channels as the initial flagging set, and replace them with values interpolated from a first order polynomial fitting to the un-flagged part of spectrum. We calculate $\sigma_m$, the median absolute deviation of the original spectrum from the convolved spectrum, and update the mask by including all channels that deviate by more than $\pm$3 $\sigma_m$ from the convolved spectrum. A flagged channel is replaced by the value interpolated from a first order polynomial fit to the un-flagged part of the whole spectrum, if it is in the initial flagging set, or in a 