paper

The Manticore Project II: Bayesian digital twins of cosmic structure across the SDSS and BOSS volumes

arXiv:2606.10020 · doi:10.1093/mnras/stag1366

Abstract

We present Manticore-Deep, a high-resolution Bayesian field-level reconstruction of cosmic large-scale structure over a comoving volume of to at ~Mpc/h resolution. Extending the companion Manticore-Local analysis (Paper~I), Manticore-Deep jointly constrains five galaxy redshift surveys within a single hierarchical Bayesian framework using the BORG algorithm. The inference reconstructs primordial initial conditions evolved under gravity, yielding a posterior ensemble of three-dimensional density and velocity fields that causally reproduce the observed large-scale structure. A novel tiled inference strategy extends the reconstructed volume by more than an order of magnitude beyond Paper~I. Posterior realisations are consistent with LCDM, reproducing Gaussian isotropic initial conditions and the expected matter power spectrum, bispectrum, and halo mass function over the resolved scales. We validate the reconstruction using two independent template-free posterior-predictive tests against observations excluded from the inference. Cross-correlation with the \textit{Planck} PR3 CMB lensing map yields a cumulative detection significance of 7.4 , while velocity-weighted stacking of galaxy clusters on the \textit{Planck} 217~GHz map detects the kinetic Sunyaev--Zel'dovich effect at , with a model-independent approach--recession split confirming the inferred velocities. Together, these tests validate both the projected-density and three-dimensional velocity fields recovered by Manticore-Deep. The BOSS Great Wall is recovered as a overdensity consistent with LCDM across the posterior ensemble. Manticore-Deep establishes a benchmark for survey-depth constrained cosmological digital twins and reproducible field-level validation of large-scale structure reconstructions.

29 pages, 21 figures. Published in MNRAS. Data will become available at www.cosmictwin.org. This is a Learning the Universe publication