The galaxy correlation function as a constraint on galaxy formation physics
Marcel P. van Daalen (1,2,3), Bruno M. B. Henriques (1), Raul E., Angulo (4), Simon D. M. White (1) ((1) Max Planck Institute for Astrophysics,, (2) Leiden Observatory, Leiden University, (3) Department of Astronomy, UC, Berkeley, Lawrence Berkeley National Laboratory

TL;DR
This paper presents a new method combining galaxy clustering and luminosity functions to better constrain galaxy formation physics and cosmological parameters using semi-analytic models and MCMC techniques.
Contribution
It introduces a novel approach to estimate galaxy correlation functions efficiently and use them with abundance data for joint constraints on galaxy formation and cosmology.
Findings
Achieved ~10% accuracy in correlation function estimation from small subsamples.
Constrained semi-analytic model parameters with improved precision.
Demonstrated potential for multi-epoch clustering and abundance data to inform galaxy formation and cosmology.
Abstract
We introduce methods which allow observed galaxy clustering to be used together with observed luminosity or stellar mass functions to constrain the physics of galaxy formation. We show how the projected two-point correlation function of galaxies in a large semi-analytic simulation can be estimated to better than ~10% using only a very small subsample of the subhalo merger trees. This allows measured correlations to be used as constraints in a Monte Carlo Markov Chain exploration of the astrophysical and cosmological parameter space. An important part of our scheme is an analytic profile which captures the simulated satellite distribution extremely well out to several halo virial radii. This is essential to reproduce the correlation properties of the full simulation at intermediate separations. As a first application, we use low-redshift clustering and abundance measurements to constrain…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
