Cosmology with One Galaxy?

  title={Cosmology with One Galaxy?},
  author={Francisco Villaescusa-Navarro and J. Ding and Shy Genel and Stephanie Tonnesen and Valentina La Torre and David N. Spergel and R. Teyssier and Yin Li and Caroline Heneka and Pablo Lemos and Daniel Angl'es-Alc'azar and Daisuke Nagai and Mark Vogelsberger},
  journal={The Astrophysical Journal},
Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood… 

Improving cosmological covariance matrices with machine learning

By using the denoised covariance matrices, the cosmological parameters can be recovered with nearly the same accuracy as when using covarianceMatrices built with a sample of 30,000 spectra in the case of the cheap simulations, and with 15,000 Spectra with the N-body simulations.

Machine Learning and Cosmology

Current and ongoing developments relating to the application of machine learning within cosmology and a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities are summarized.



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