Cosmology with one galaxy?

Abstract

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 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of Ω m , at fixed Ω b , with a ∼ 10% precision, while no constraint can be placed on σ 8 . Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, z ≤ 3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of Ω m . We believe that our results can be explained taking into account that changes in the value of Ω m , or potentially Ω b /Ω m , affect the dark matter content of galaxies. That effect leaves a distinct signature in galaxy properties to the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.

Learning cosmology and clustering with cosmic graphs

“…1 correlations among different galaxy features, color coded by Ω m , for galaxies in the z = 0 catalogues. As can be seen, and already noted in Villaescusa-Navarro et al (2022b), there are some noticeable correlations between galaxy properties and Ω m . Thus, our model may learn to extract cosmological information based on galaxy properties, on top of galaxy clustering.…”

“…Using V max as the only node feature leads to accurate results, with mean relative errors around 5 % and R 2 0.97. We note that Villaescusa-Navarro et al (2022b) found V max to be the most relevant feature when inferring the value of Ω m from individual galaxies. This could be due to the fact that V max measures the depth of the gravitational potential, that may correlate better with Ω m than the other considered galaxy properties.…”

“…• In Villaescusa-Navarro et al (2022b), Ω m was inferred from the properties of individual galaxies.…”

Machine Learning and Cosmology

“…Thus, simulations can be used as a laboratory to identify unrecognized patterns that can help our understanding of the underlying mechanisms behind the physical process being studied. For instance, it has been shown that unknown relations between galaxy properties and parameters describing the composition of the Universe can be easily identified by employing machine learning techniques on top of state-of-the-art hydrodynamic simulations [89]. We believe that machine learning can trigger a revolution in the large variety of areas of cosmology and galaxy formation that deal with high-dimensional data.…”

The scatter in the galaxy-halo connection: a machine learning analysis

“…Since the location and shape of the manifold depends on cosmological and astrophysical parameters (see e.g. Villaescusa- Navarro et al (2022b)), in this work we have compared two simulations with different cosmologies and subgrid physics models. One way to extend our framework is to consider a wider range of training sets which systematically vary such differences, which would improve understanding of the sensitivity of the results to the simulation inputs.…”