The Manticore Project: Field-Level Posterior Inference as a Laboratory for Cosmology and Galaxy Formation

Abstract

Traditional cosmological analyses describe ensemble-averaged properties of structure formation. Bayesian field-level inference offers a fundamentally different capability: by constructing posterior ensembles of initial conditions directly constrained by galaxy survey data, one recovers physically consistent realizations of the specific Universe we inhabit. This eliminates cosmic variance as a systematic and turns individual structures into controlled tests of physical models, a mode of inference unavailable to any statistical approach. I will present the Manticore project as a large-scale demonstration of this capability, spanning from methodology to concrete scientific consequences. The Manticore project applies this framework to two complementary datasets. Constrained by the all-sky 2MASS galaxy catalogue, the local reconstruction recovers the three-dimensional matter and velocity distribution within 200 Mpc, achieving the highest Bayesian evidence for the peculiar velocity field across five independent datasets and robustly identifying fourteen nearby galaxy clusters. Constrained by the combined SDSS and BOSS spectroscopic surveys, the reconstruction extends to a (4 h⁻¹ Gpc)³ volume at ∼4 h⁻¹ Mpc resolution, validated against observations withheld from the inference: a cross-correlation with Planck CMB lensing and a kinetic SZ detection from velocity-weighted cluster stacking. Having accurate, uncertainty-quantified models of the local density and velocity fields opens new avenues for precision cosmology. Peculiar velocity corrections are a dominant systematic in local H₀ measurements; replacing standard linear reconstructions with the Manticore velocity posterior in a SN-free Cepheid distance ladder analysis yields H₀ = 71.1 ± 1.4 km/s/Mpc with an 18% uncertainty reduction. The reconstructed density field further reveals that SN Ia rates in nearby superclusters exceed expectations from matter overdensities alone by factors of two to five, indicating that the local large-scale structure introduces correlated systematics into SN cosmology samples that standard analyses do not capture. Finally, constrained hydrodynamical simulations of individual nearby galaxy clusters, confronted against X-ray and SZ observations, expose failures in AGN feedback models that population-level statistics cannot detect. Together, these results illustrate what becomes possible when field-level inference is pushed beyond parameter estimation toward direct confrontation with the observed Universe, and outline an emerging programme for jointly constraining cosmology and astrophysical processes within a single coherent reconstruction.