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As the next generation of large galaxy surveys come online, it is becomingincreasingly important to develop and understand the machine learning toolsthat analyze big astronomical data. Neural networks are powerful and capable ofprobing deep patterns in data, but must be trained carefully on large andrepresentative data sets. We developed and generated a new `hump' of theCosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project:CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100$h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and$\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxyformation over a broad range of astrophysical parameters. As a proof-of-conceptfor the power of this vast suite of simulated galaxies in a large volume andbroad parameter space, we probe the power of simple clustering summarystatistics to marginalize over astrophysics and constrain cosmology usingneural networks. We use the two-point correlation function, count-in-cells, andthe Void Probability Function, and probe non-linear and linear scales across$0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effectof various galaxy selections, galaxy sampling, and choice of clusteringstatistics on these constraints. We additionally explore how these clusteringstatistics constrain and inform key stellar and galactic feedback parameters inthe Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest ofCAMELS, and offers great potential to many applications of machine learning inastrophysics: https://camels-sam.readthedocs.io.

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite