[[H]{}[<span style="font-variant:small-caps;">i</span> ]{}]{}cube (top) and that after applying the improved continuum subtraction discussed in section \[sec:flattening\] (bottom). The displayed grey scale ranges from -3 to 3 $\sigma$ around the mean of the cube. []{data-label="fig:projection"}](plot/projection40-159.ps){width="7.5cm"}

Source finding and statistical detection of HI in the environment {#sec:scfinding}
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We extract sources (both real detections as well as noise peaks to be used for our statistical analysis) from the improved [[H]{}[<span style="font-variant:small-caps;">i</span> ]{}]{}data cubes using the source detection software SoFiA. We refer to Serra et al. (2015) for a detailed description of the software. Here, we only outline the most relevant steps and describe the key parameter choices.

SoFiA first convolves the data with various smoothing kernels and selects voxels with absolute values above a certain threshold. We use smoothing kernels with a width of 0 and 3 pixels in the projected sky direction (roughly the size of the synthesised beam), and a width of 0, 3 and 5 pixels (corresponding to 0, 36 and 60 km$/$s) in the velocity direction (smoothing with a kernel with width of 0 is equivalent to no smoothing). Each channel of the cube is weighted according to the noise, so that noise variations along the velocity axis 