  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.
