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.
A new gas cooling model for semi-analytical galaxy formation models
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abstract
Semi-analytic galaxy formation models are widely used to gain insight into the astrophysics of galaxy formation and in model testing, parameter space searching and mock catalogue building. In this work we present a new model for gas cooling in halos in semi-analytic models, which improves over previous cooling models in several ways. Our new treatment explicitly includes the evolution of the density profile of the hot gas driven by the growth of the dark matter halo and by the dynamical adjustment of the gaseous corona as gas cools down. The effect of the past cooling history on the current mass cooling rate is calculated more accurately, by doing an integral over the past history. The evolution of the hot gas angular momentum profile is explicitly followed, leading to a self-consistent and more detailed calculation of the angular momentum of the cooled down gas. This model predicts higher cooled down masses than the cooling models previously used in GALFORM, closer to the predictions of the cooling models in L-GALAXIES and MORGANA, even though those models are formulated differently. It also predicts cooled down angular momenta that are higher than in previous GALFORM cooling models, but generally lower than the predictions of L-GALAXIES and MORGANA. When used in a full galaxy formation model, this cooling model improves the predictions for early-type galaxy sizes in GALFORM.
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The Manticore Project II: Bayesian digital twins of cosmic structure across the SDSS and BOSS volumes
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.