Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
We train graph neural networks on halo catalogs from Gadget N -body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogs contain ≲5000 halos with masses ≳10 ^10 h ^−1 M _⊙ in a periodic volume of ${(25\,{h}^{-1}\,\mathrm{Mpc})}^{3}$ ; every halo in th...
| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Langue: | English |
| Publié: |
IOP Publishing
2023-01-01
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| Collection: | The Astrophysical Journal |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.3847/1538-4357/acac7a |