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

Abstract

Context. 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. Aims. We introduce SHAMe-SF, an extension of the subhalo abundance matching (SHAM) technique designed specifically for analysing the redshift-space clustering of star-forming galaxies. Methods. Our model directly links a galaxy’s star-formation rate to the properties of its host dark matter subhalo, 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. These tests were conducted over a wide range of scales [0.6, 30] h−1 Mpc using samples from both the TNG300 magneto-hydrodynamic simulation and a semi-analytical model. Results. SHAMe-SF can reproduce the clustering of simulated galaxies selected by star-formation rate as well as galaxies that fall within the colour selection criteria employed by DESI for emission line galaxies. Conclusions. 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.