  Potential evidence for primordial non-Gaussianity (PNG) is expected to lie in
the largest scales mapped by cosmological surveys. Forthcoming 21cm intensity
mapping experiments will aim to probe these scales by surveying neutral
hydrogen (HI) within galaxies. However, foreground signals dominate the faint
21cm emission, meaning foreground cleaning is required to recover the
cosmological signal. The effect this has is to damp the HI power spectrum on
the largest scales, especially along the line-of-sight. Whilst there is
agreement that this contamination is potentially problematic for probing PNG,
it is yet to be fully explored and quantified. In this work we carry out the
first forecasts on $f_\text{NL}$ that incorporate simulated foreground maps
that are removed using techniques employed in real data. Using an MCMC
analysis, we demonstrate that foreground cleaned data recovers hugely biased
values ($f_\text{NL} = -102.1_{-7.96}^{+8.39}$ [68% CL]) on our $f_\text{NL}=0$
fiducial input. Introducing a model with fixed parameters for the foreground
contamination allows us to recover unbiased results ($f_\text{NL} =
-2.94_{-11.9}^{+11.4}$). However, it is not clear that we will have sufficient
understanding of foreground contamination to allow for such rigid models.
Treating the main parameter $k_\parallel^\text{FG}$ in our foreground model as
a nuisance parameter and marginalizing over it, still recovers unbiased results
but at the expense of much larger errors ($f_\text{NL} =
0.75^{+40.2}_{-44.5}$), that can only be reduced by imposing the Planck 2018
prior. Our results show that significant progress on understanding and
controlling foreground removal effects is necessary in order to study PNG with
HI intensity mapping.
