consisting of 1000 values of average $\mHIcand$. var1 is calculated as the variation of this distribution. The final error is calculated as $\sqrt{var1+\overline{var2}}$. We derive an average $\mHIcand$ of $\sim1.1\times10^8\ms$ for the 40 cubes within the whole analysis volume.

The question is, however, whether and how significantly $\mHIcand$ is affected by remaining artefacts in the data. Asymmetrically distributed noise can come from weak RFI residuals that are not completely removed during data reduction, and can also be produced in the continuum subtraction and cleaning processes. Hence in the following sub-sections we undertake a series of tests to identify and quantify these effects, before moving on to the scientific interpretation in section \[sec:result\].

Influence of synthesis data reduction on the mass in candidates {#sec:testing}
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We begin this series of tests by verifying that the white noise in the data does not accumulate to a significant net signal. We test this by applying the same source finding and $\mHIcand$ measurements presented in section \[sec:scfinding\] to the Stokes Q cubes. Since no polarised signal in the line data is expected, the result is expected to be 0 unless indeed a positive accumulative noise signal is present. Figure \[fig:mhidistr\_qq\] confirms that $\mHIcand$ of 