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...
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Autor*in:	Helen Shao [verfasserIn] Francisco Villaescusa-Navarro [verfasserIn] Pablo Villanueva-Domingo [verfasserIn] Romain Teyssier [verfasserIn] Lehman H. Garrison [verfasserIn] Marco Gatti [verfasserIn] Derek Inman [verfasserIn] Yueying Ni [verfasserIn] Ulrich P. Steinwandel [verfasserIn] Mihir Kulkarni [verfasserIn] Eli Visbal [verfasserIn] Greg L. Bryan [verfasserIn] Daniel Anglés-Alcázar [verfasserIn] Tiago Castro [verfasserIn] Elena Hernández-Martínez [verfasserIn] Klaus Dolag [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure

Übergeordnetes Werk:	In: The Astrophysical Journal - IOP Publishing, 2022, 944(2023), 1, p 27
Übergeordnetes Werk:	volume:944 ; year:2023 ; number:1, p 27

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4357/acac7a

Katalog-ID:	DOAJ089163990

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520			\|a 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters.
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allfields	10.3847/1538-4357/acac7a doi (DE-627)DOAJ089163990 (DE-599)DOAJa8c3ad9a6b294721a0554fb79a931eb1 DE-627 ger DE-627 rakwb eng QB460-466 Helen Shao verfasserin aut Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics Francisco Villaescusa-Navarro verfasserin aut Pablo Villanueva-Domingo verfasserin aut Romain Teyssier verfasserin aut Lehman H. Garrison verfasserin aut Marco Gatti verfasserin aut Derek Inman verfasserin aut Yueying Ni verfasserin aut Ulrich P. Steinwandel verfasserin aut Mihir Kulkarni verfasserin aut Eli Visbal verfasserin aut Greg L. Bryan verfasserin aut Daniel Anglés-Alcázar verfasserin aut Tiago Castro verfasserin aut Elena Hernández-Martínez verfasserin aut Klaus Dolag verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 944(2023), 1, p 27 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:944 year:2023 number:1, p 27 https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 kostenfrei https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 1, p 27
spelling	10.3847/1538-4357/acac7a doi (DE-627)DOAJ089163990 (DE-599)DOAJa8c3ad9a6b294721a0554fb79a931eb1 DE-627 ger DE-627 rakwb eng QB460-466 Helen Shao verfasserin aut Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics Francisco Villaescusa-Navarro verfasserin aut Pablo Villanueva-Domingo verfasserin aut Romain Teyssier verfasserin aut Lehman H. Garrison verfasserin aut Marco Gatti verfasserin aut Derek Inman verfasserin aut Yueying Ni verfasserin aut Ulrich P. Steinwandel verfasserin aut Mihir Kulkarni verfasserin aut Eli Visbal verfasserin aut Greg L. Bryan verfasserin aut Daniel Anglés-Alcázar verfasserin aut Tiago Castro verfasserin aut Elena Hernández-Martínez verfasserin aut Klaus Dolag verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 944(2023), 1, p 27 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:944 year:2023 number:1, p 27 https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 kostenfrei https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 1, p 27
allfields_unstemmed	10.3847/1538-4357/acac7a doi (DE-627)DOAJ089163990 (DE-599)DOAJa8c3ad9a6b294721a0554fb79a931eb1 DE-627 ger DE-627 rakwb eng QB460-466 Helen Shao verfasserin aut Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics Francisco Villaescusa-Navarro verfasserin aut Pablo Villanueva-Domingo verfasserin aut Romain Teyssier verfasserin aut Lehman H. Garrison verfasserin aut Marco Gatti verfasserin aut Derek Inman verfasserin aut Yueying Ni verfasserin aut Ulrich P. Steinwandel verfasserin aut Mihir Kulkarni verfasserin aut Eli Visbal verfasserin aut Greg L. Bryan verfasserin aut Daniel Anglés-Alcázar verfasserin aut Tiago Castro verfasserin aut Elena Hernández-Martínez verfasserin aut Klaus Dolag verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 944(2023), 1, p 27 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:944 year:2023 number:1, p 27 https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 kostenfrei https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 1, p 27
allfieldsGer	10.3847/1538-4357/acac7a doi (DE-627)DOAJ089163990 (DE-599)DOAJa8c3ad9a6b294721a0554fb79a931eb1 DE-627 ger DE-627 rakwb eng QB460-466 Helen Shao verfasserin aut Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics Francisco Villaescusa-Navarro verfasserin aut Pablo Villanueva-Domingo verfasserin aut Romain Teyssier verfasserin aut Lehman H. Garrison verfasserin aut Marco Gatti verfasserin aut Derek Inman verfasserin aut Yueying Ni verfasserin aut Ulrich P. Steinwandel verfasserin aut Mihir Kulkarni verfasserin aut Eli Visbal verfasserin aut Greg L. Bryan verfasserin aut Daniel Anglés-Alcázar verfasserin aut Tiago Castro verfasserin aut Elena Hernández-Martínez verfasserin aut Klaus Dolag verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 944(2023), 1, p 27 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:944 year:2023 number:1, p 27 https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 kostenfrei https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 1, p 27
allfieldsSound	10.3847/1538-4357/acac7a doi (DE-627)DOAJ089163990 (DE-599)DOAJa8c3ad9a6b294721a0554fb79a931eb1 DE-627 ger DE-627 rakwb eng QB460-466 Helen Shao verfasserin aut Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics Francisco Villaescusa-Navarro verfasserin aut Pablo Villanueva-Domingo verfasserin aut Romain Teyssier verfasserin aut Lehman H. Garrison verfasserin aut Marco Gatti verfasserin aut Derek Inman verfasserin aut Yueying Ni verfasserin aut Ulrich P. Steinwandel verfasserin aut Mihir Kulkarni verfasserin aut Eli Visbal verfasserin aut Greg L. Bryan verfasserin aut Daniel Anglés-Alcázar verfasserin aut Tiago Castro verfasserin aut Elena Hernández-Martínez verfasserin aut Klaus Dolag verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 944(2023), 1, p 27 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:944 year:2023 number:1, p 27 https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 kostenfrei https://doi.org/10.3847/1538-4357/acac7a kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 944 2023 1, p 27
language	English
source	In The Astrophysical Journal 944(2023), 1, p 27 volume:944 year:2023 number:1, p 27
sourceStr	In The Astrophysical Journal 944(2023), 1, p 27 volume:944 year:2023 number:1, p 27
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure Astrophysics
isfreeaccess_bool	true
container_title	The Astrophysical Journal
authorswithroles_txt_mv	Helen Shao @@aut@@ Francisco Villaescusa-Navarro @@aut@@ Pablo Villanueva-Domingo @@aut@@ Romain Teyssier @@aut@@ Lehman H. Garrison @@aut@@ Marco Gatti @@aut@@ Derek Inman @@aut@@ Yueying Ni @@aut@@ Ulrich P. Steinwandel @@aut@@ Mihir Kulkarni @@aut@@ Eli Visbal @@aut@@ Greg L. Bryan @@aut@@ Daniel Anglés-Alcázar @@aut@@ Tiago Castro @@aut@@ Elena Hernández-Martínez @@aut@@ Klaus Dolag @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	269019219
id	DOAJ089163990
language_de	englisch
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callnumber-first	Q - Science
author	Helen Shao
spellingShingle	Helen Shao misc QB460-466 misc Cosmology misc Sigma8 misc Density parameters misc Cosmological parameters from large-scale structure misc Astrophysics Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
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topic_title	QB460-466 Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos Cosmology Sigma8 Density parameters Cosmological parameters from large-scale structure
topic	misc QB460-466 misc Cosmology misc Sigma8 misc Density parameters misc Cosmological parameters from large-scale structure misc Astrophysics
topic_unstemmed	misc QB460-466 misc Cosmology misc Sigma8 misc Density parameters misc Cosmological parameters from large-scale structure misc Astrophysics
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title	Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
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title_full	Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
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author_browse	Helen Shao Francisco Villaescusa-Navarro Pablo Villanueva-Domingo Romain Teyssier Lehman H. Garrison Marco Gatti Derek Inman Yueying Ni Ulrich P. Steinwandel Mihir Kulkarni Eli Visbal Greg L. Bryan Daniel Anglés-Alcázar Tiago Castro Elena Hernández-Martínez Klaus Dolag
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title_sort	robust field-level inference of cosmological parameters with dark matter halos
callnumber	QB460-466
title_auth	Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
abstract	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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters.
abstractGer	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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters.
abstract_unstemmed	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 the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω _m and σ _8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω _m and σ _8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP ^3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω _m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters.
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title_short	Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
url	https://doi.org/10.3847/1538-4357/acac7a https://doaj.org/article/a8c3ad9a6b294721a0554fb79a931eb1 https://doaj.org/toc/1538-4357
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author2Str	Francisco Villaescusa-Navarro Pablo Villanueva-Domingo Romain Teyssier Lehman H. Garrison Marco Gatti Derek Inman Yueying Ni Ulrich P. Steinwandel Mihir Kulkarni Eli Visbal Greg L. Bryan Daniel Anglés-Alcázar Tiago Castro Elena Hernández-Martínez Klaus Dolag
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Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos

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