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Cosmicflows Constrained Local UniversE Simulations

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arxiv 1510.04900 v1 pith:B47WMJ6J submitted 2015-10-16 astro-ph.CO

classification astro-ph.CO
keywords simulationsconstrainedlocaluniverseneighborhoodrandomscalescatter
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This paper combines observational datasets and cosmological simulations to generate realistic numerical replicas of the nearby Universe. These latter are excellent laboratories for studies of the non-linear process of structure formation in our neighborhood. With measurements of radial peculiar velocities in the Local Universe (cosmicflows-2) and a newly developed technique, we produce Constrained Local UniversE Simulations (CLUES). To assess the quality of these constrained simulations, we compare them with random simulations as well as with local observations. The cosmic variance, defined as the mean one-sigma scatter of cell-to-cell comparison between two fields, is significantly smaller for the constrained simulations than for the random simulations. Within the inner part of the box where most of the constraints are, the scatter is smaller by a factor 2 to 3 on a 5 Mpc/h scale with respect to that found for random simulations. This one-sigma scatter obtained when comparing the simulated and the observation-reconstructed velocity fields is only 104 +/- 4 km/s i.e. the linear theory threshold. These two results demonstrate that these simulations are in agreement with each other and with the observations of our neighborhood. For the first time, simulations constrained with observational radial peculiar velocities resemble the Local Universe up to a distance of 150 Mpc/h on a scale of a few tens of megaparsecs. When focusing on the inner part of the box, the resemblance with our cosmic neighborhood extends to a few megaparsecs (< 5 Mpc/h). The simulations provide a proper Large Scale environment for studies of the formation of nearby objects.

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

    astro-ph.CO 2026-06 unverdicted novelty 6.0 of 10

    Manticore-Deep uses tiled Bayesian field-level inference on SDSS and BOSS data to produce posterior ensembles of 3D cosmic fields that are consistent with LCDM and validated by 7.4σ CMB lensing and 3.5σ kSZ detections.

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