Rescaling merger trees with a halo-profile correction enables cheap generation of galaxy summary statistics across cosmologies using semi-analytic models, matching dedicated simulation accuracy with far fewer base runs.
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SHAMe-SF modeling of small-scale DESI ELG clustering delivers 6% precision on σ8 and Ωm h², matching full DR1 results with 1% volume.
First systematic validation shows Hybrid Bias Expansion model for galaxy bispectrum remains accurate up to k=0.25 h/Mpc in DESI-like mocks, outperforming tree-level EFT.
citing papers explorer
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Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
Rescaling merger trees with a halo-profile correction enables cheap generation of galaxy summary statistics across cosmologies using semi-analytic models, matching dedicated simulation accuracy with far fewer base runs.
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Cosmological constraints from the small scale clustering of Emission Line Galaxies
SHAMe-SF modeling of small-scale DESI ELG clustering delivers 6% precision on σ8 and Ωm h², matching full DR1 results with 1% volume.
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Validation of the Hybrid Bias Expansion model for the galaxy bispectrum
First systematic validation shows Hybrid Bias Expansion model for galaxy bispectrum remains accurate up to k=0.25 h/Mpc in DESI-like mocks, outperforming tree-level EFT.