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The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space
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abstract
We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an 8-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.
Forward citations
Cited by 5 Pith papers
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CHEFT recovers matter power to percent level and weighted tracers to ~3–5% by expressing the halo-halo spectrum as a sum of collapsed HEFT operators with probabilistic mass-dependent biases.
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Bridging Simulations and EFT: A Hybrid Model of the Lyman-Alpha Forest Field
A hybrid EFT forward model using N-body displacements reproduces the simulated Lyman-alpha forest to 5% at k <= 1 h/Mpc with a white-noise residual.
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CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation
A new emulator predicts cumulative dark matter halo mass functions for three mass definitions with claimed 2-10% accuracy from z=0 to 3, based on the Kun simulation suite.
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Low-redshift constraints on structure growth from CMB lensing tomography
Low-redshift galaxy clustering and CMB lensing tomography with hybrid effective field theory gives S8=0.79±0.06, consistent with Planck, while data alone prefer Ωm=0.245±0.024.
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