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

Journal article

Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
https://arxiv.org/abs/2204.02408

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APA Click to copy
Perez, L. A., Genel, S., Villaescusa-Navarro, F., Somerville, R. S., Gabrielpillai, A., Anglés-Alcázar, D., … Yung, L. Y. A. Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite. Https://Arxiv.org/Abs/2204.02408.

Chicago/Turabian Click to copy
Perez, Lucia A., Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, and L. Y. Aaron Yung. “Constraining Cosmology with Machine Learning and Galaxy Clustering: the CAMELS-SAM Suite.” https://arxiv.org/abs/2204.02408 (n.d.).

MLA Click to copy
Perez, Lucia A., et al. “Constraining Cosmology with Machine Learning and Galaxy Clustering: the CAMELS-SAM Suite.” Https://Arxiv.org/Abs/2204.02408.

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@article{lucia-a,
  title = {Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite},
  journal = {https://arxiv.org/abs/2204.02408},
  author = {Perez, Lucia A. and Genel, Shy and Villaescusa-Navarro, Francisco and Somerville, Rachel S. and Gabrielpillai, Austen and Anglés-Alcázar, Daniel and Wandelt, Benjamin D. and Yung, L. Y. Aaron},
  howpublished = {}
}

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology 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 (Ωm and σ8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across 0.68< R <27 h−1 cMpc. Our cosmological constraints cluster around 3-8% error on ΩM and σ8, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: camels-sam.readthedocs.io