SHAMe-SF: Predicting the clustering of star-forming galaxies with an enhanced abundance matching model

Abstract

With the advent of several galaxy surveys targeting star-forming galaxies, it is important to have models capable of interpreting their spatial distribution in terms of astrophysical and cosmological parameters. To address this need, we introduce SHAMe-SF, an extension of the subhalo abundance matching (SHAM) technique designed specifically for analyzing the redshift-space clustering of star-forming galaxies. Our model directly links a galaxy's star formation rate to the properties of its host dark-matter halo, with further modulations based on effective models of feedback and gas stripping. To quantify the accuracy of our model, we show that it simultaneously reproduces key clustering statistics such as the projected correlation function, monopole, and quadrupole of star-forming galaxy samples at various redshifts and number densities. Notably, these tests were conducted over a wide range of scales $[0.6, 30]\hMpc$, using samples from both the TNG300 magneto-hydrodynamic simulation and from a semi-analytical model. SHAMe-SF can also reproduce the clustering of simulated galaxies that fall within the colour selection criteria employed by DESI for emission line galaxies. Our model exhibits several potential applications, including the generation of covariance matrices, exploration of galaxy formation processes, and even placing constraints on the cosmological parameters of the Universe.

18 pages, 10 figures

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Cited by in corpus (7)

• Investigating the galaxy-halo connection of DESI Emission-Line Galaxies with SHAMe-SF
• Modeling spatially-resolved galactic H$\alpha$ emission for galaxy clustering
• ELG$\times$LRG distribution through dark matter halo dynamics
• Exploring the halo-galaxy connection with probabilistic approaches
• Probabilistic Lagrangian bias estimators and the cumulant bias expansion
• Gaussian Lagrangian Galaxy Bias
• STRAWBERRY: Finding haloes in the gravitational potential