Workshop: 2nd ICML Workshop on Machine Learning for Astrophysics

Cosmology with Galaxy Photometry Alone

ChangHoon Hahn · Peter Melchior · Francisco Villaescusa-Navarro · Romain Teyssier

Abstract: We present the first cosmological constraints from only the observed photometry of galaxies using neural density estimation (NDE). Villaescusa-Navarro et al. \yrcite{villaescusa-navarro2022} recently demonstrated that the internal physical properties of a single galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables. We use ensembled NDE and the CAMELS suite of hydrodynamical simulations to infer cosmological parameters from galaxy photometry. We find that the cosmological information in the photometry of a single galaxy is severely limited. However, since NDE dramatically reduces the cost of evaluating the posterior, we can feasibly combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of $\sim$15,000 NASA-Sloan Atlas galaxies, we constrain $\Omega_m = 0.310^{+0.080}_{-0.098}$ and $\sigma_8 = 0.792^{+0.099}_{-0.090}$.

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