Using Large Scale Structure to test Multifield Inflation
  Primordial non-Gaussianity of local type is known to produce a
scale-dependent contribution to the galaxy bias. Several classes of multi-field
inflationary models predict non-Gaussian bias which is stochastic, in the sense
that dark matter and halos don't trace each other perfectly on large scales. In
this work, we forecast the ability of next-generation Large Scale Structure
surveys to constrain common types of primordial non-Gaussianity like $f_{NL}$,
$g_{NL}$ and $\tau_{NL}$ using halo bias, including stochastic contributions.
We provide fitting functions for statistical errors on these parameters which
can be used for rapid forecasting or survey optimization. A next-generation
survey with volume $V = 25 h^{-3}$Gpc$^3$, median redshift $z = 0.7$ and mean
bias $b_g = 2.5$, can achieve $\sigma(f_{NL}) = 6$, $\sigma(g_{NL}) = 10^5$ and
$\sigma(\tau_{NL}) = 10^3$ if no mass information is available. If halo masses
are available, we show that optimally weighting the halo field in order to
reduce sample variance can achieve $\sigma(f_{NL}) = 1.5$, $\sigma(g_{NL}) =
10^4$ and $\sigma(\tau_{NL}) = 100$ if halos with mass down to $M_{min} =
10^{11}$ $h^{-1} M_\odot $ are resolved, outperforming Planck by a factor of 4
on $f_{NL}$ and nearly an order of magnitude on $g_{NL}$ and $\tau_{NL}$.
Finally, we study the effect of photometric redshift errors and discuss
degeneracies between different non-Gaussian parameters, as well as the impact
of marginalizing Gaussian bias and shot noise.
