Accurate halo-galaxy mocks from automatic bias estimation and particle
  mesh gravity solvers
  Reliable extraction of cosmological information from clustering measurements
of galaxy surveys requires estimation of the error covariance matrices of
observables. The accuracy of covariance matrices is limited by our ability to
generate sufficiently large number of independent mock catalogs that can
describe the physics of galaxy clustering across a wide range of scales.
Furthermore, galaxy mock catalogs are required to study systematics in galaxy
surveys and to test analysis tools. In this investigation, we present a fast
and accurate approach for generation of mock catalogs for the upcoming galaxy
surveys. Our method relies on low-resolution approximate gravity solvers to
simulate the large scale dark matter field, which we then populate with halos
according to a flexible nonlinear and stochastic bias model. In particular, we
extend the \textsc{patchy} code with an efficient particle mesh algorithm to
simulate the dark matter field (the \textsc{FastPM} code), and with a robust
MCMC method relying on the \textsc{emcee} code for constraining the parameters
of the bias model. Using the halos in the BigMultiDark high-resolution $N$-body
simulation as a reference catalog, we demonstrate that our technique can model
the bivariate probability distribution function (counts-in-cells), power
spectrum, and bispectrum of halos in the reference catalog. Specifically, we
show that the new ingredients permit us to reach percentage accuracy in the
power spectrum up to $k\sim 0.4\; \,h\,{\rm Mpc}^{-1}$ (within 5\% up to $k\sim
0.6\; \,h\,{\rm Mpc}^{-1}$) with accurate bispectra improving previous results
based on Lagrangian perturbation theory.
