On the effects of parameters on galaxy properties in CAMELS and the predictability of ${\Omega }_{\mathrm{m}}$

Abstract

Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the universe, ${\Omega }_{\mathrm{m}}$, from the features of a single galaxy. We investigate the statistical properties of two of these simulation suites, IllustrisTNG and ASTRID, confirming that ${\Omega }_{\mathrm{m}}$ induces a strong displacement on the distribution of galaxy features. We also observe that most other parameters have little to no effect on the distribution, except for the stellar-feedback parameter $A_{SN1}$, which introduces some near-degeneracies that can be broken with specific features. These two properties explain the predictability of ${\Omega }_{\mathrm{m}}$. We use Optimal Transport to further measure the effect of parameters on the distribution of galaxy properties, which is found to be consistent with physical expectations. However, we observe discrepancies between the two simulation suites, both in the effect of ${\Omega }_{\mathrm{m}}$ on the galaxy properties and in the distributions themselves at identical parameter values. Thus, although ${\Omega }_{\mathrm{m}}$'s signature can be easily detected within a given simulation suite using just a single galaxy, applying this result to real observational data may prove significantly more challenging.

19 pages, 9 figures. Comments welcome!

References in corpus (7)

• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• The CAMELS project: public data release
• Cosmology with one galaxy?
• X-raying CAMELS: Constraining Baryonic Feedback in the Circum-Galactic Medium with the CAMELS simulations and eRASS X-ray Observations
• CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- I. Constraining physical processes in local star-forming galaxies
• Cosmology from one galaxy in a void?
• Cosmology with One Galaxy: Auto-Encoding the Galaxy Properties Manifold

Cited by in corpus (1)

• Dark Matter profiles of "in silico" galaxies: deep learning inference