Optimal Transport Reconstruction of Biased Tracers in Primordial Non-Gaussian Fields

Abstract

Optimal transport provides an efficient method to infer the displacement of objects by mapping their initial positions to their present-day locations over cosmic time; equivalently, it enables the reconstruction of initial positions from measurements taken at later times. The method has been shown to be accurate even if positions for only a biased subset of the particles are measured, provided that the initial displacement field was Gaussian. The method does not rely on the assumption of a Gaussian displacement field, and thus may be extended to the reconstruction of non-Gaussian initial conditions. Here, we demonstrate how this is achieved for a class of "local" primordial non-Gaussian fields of current interest in cosmology. For these models, there is a distinctive signature in the large scale clustering of biased tracers which depends on the product of the primordial amplitude $f_{\rm NL}$ and the nature of the tracers $b_ϕ$. Our method exploits the fact that this signature is not present in the full field; it is only present in biased fields. Therefore, the mass that is not in the biased subset, what we call the "dust", also has a characteristic scale-dependence, albeit of a different amplitude. We show that the quality of the optimal transport reconstruction improves as the model for this dust becomes more realistic.