Void halo mass function: A promising probe of neutrino mass

Zhang, Gemma; Li, Zack; Liu, Jia; Spergel, David N.; Kreisch, Christina D.; Pisani, Alice; Wandelt, B. D.

doi:10.1103/physrevd.102.083537

Cited by 24 publications

(19 citation statements)

References 49 publications

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“…Due the low number density and sparsity of voids, very-large-volume simulations are required thus gravity-only simulations are commonly used (note no strong baryonic effects are expected (Paillas et al 2017)), however, usually the dark matter field is used, which might show some dependence with the resolution of the simulation. The use of simulations has also helped in designing new probes, e.g., massive halos inside voids for constraining neutrino masses (Zhang et al 2020) .…”

Section: Voidsmentioning

confidence: 99%

Large-scale dark matter simulations

Angulo

Hahn

2022

Living Rev Comput Astrophys

111

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We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then recap the relevant numerical approaches: discretization of the phase-space distribution (focusing on N-body but including alternatives, e.g., Lagrangian submanifold and Schrödinger–Poisson) and the respective techniques for their time evolution and force calculation (direct summation, mesh techniques, and hierarchical tree methods). We pay attention to the creation of initial conditions and the connection with Lagrangian Perturbation Theory. We then discuss the possible alternatives in terms of the micro-physical properties of dark matter (e.g., neutralinos, warm dark matter, QCD axions, Bose–Einstein condensates, and primordial black holes), and extensions to account for multiple fluids (baryons and neutrinos), primordial non-Gaussianity and modified gravity. We continue by discussing challenges involved in achieving highly accurate predictions. A key aspect of cosmological simulations is the connection to cosmological observables, we discuss various techniques in this regard: structure finding, galaxy formation and baryonic modelling, the creation of emulators and light-cones, and the role of machine learning. We finalise with a recount of state-of-the-art large-scale simulations and conclude with an outlook for the next decade.

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Section: Voidsmentioning

confidence: 99%

Large-scale dark matter simulations

Angulo

Hahn

2022

Living Rev Comput Astrophys

111

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“…However, we do not know what estimator would allow us to extract the maximum amount of information for non-Gaussian density fields, such as the matter and galaxy distribution on nonlinear scales. Quantifying the information content from different estimators is currently a very active area of research (Coulton et al 2019;Li et al 2019;Marques et al 2019;Ajani et al 2020;Allys et al 2020;Banerjee et al 2020;Dai et al 2020;de la Bella et al 2021;Friedrich et al 2020;Giri & Smith 2020;Hahn et al 2020;Villaescusa-Navarro et al 2020;Uhlemann et al 2020;Zhang et al 2020;Banerjee & Abel 2021aBayer et al 2021aBayer et al , 2021bCheng and Ménard 2021;Gualdi et al 2021aGualdi et al , 2021bHahn & Villaescusa-Navarro 2021;Harnois-Déraps et al 2021;Kuruvilla 2021;Kuruvilla & Aghanim 2021;Massara et al 2021;Naidoo et al 2021;Porth et al 2021;Samushia et al 2021;Valogiannis & Dvorkin 2021;Harnois-Déraps et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Cosmology with One Galaxy?

Villaescusa-Navarro

Ding

Genel

et al. 2022

ApJ

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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 2000 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 allows our models to infer the value of Ωm, at fixed Ωb, 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 Ωm. We believe that our results can be explained by considering that changes in the value of Ωm, or potentially Ωb/Ωm, affect the dark matter content of galaxies, which leaves a signature in galaxy properties distinct from 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.

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“…However, we do not know what estimator would allow us to extract the maximum amount of information for non-Gaussian density fields, like the matter and galaxy distribution resembles on non-linear scales. Quantifying the information content from different estimators is currently a very active area of research (Villaescusa-Navarro et al 2020;Samushia et al 2021;Gualdi et al 2021a;Kuruvilla & Aghanim 2021;Banerjee et al 2020;Hahn et al 2020;Uhlemann et al 2020;Friedrich et al 2020;Massara et al 2021;Dai et al 2020;Allys et al 2020;Banerjee & Abel 2021a,b;Gualdi et al 2021b,a;Giri & Smith 2020;de la Bella et al 2020;Hahn & Villaescusa-Navarro 2021;Valogiannis & Dvorkin 2021;Kuruvilla 2021;Naidoo et al 2021;Porth et al 2021;Harnois-Déraps et al 2022;Li et al 2019;Coulton et al 2019;Marques et al 2019;Zhang et al 2020;Ajani et al 2020;Harnois-Déraps et al 2021;Cheng & Ménard 2021;Harnois-Déraps et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Cosmology with one galaxy?

Villaescusa-Navarro,

Ding,

Genel

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Void halo mass function: A promising probe of neutrino mass

Cited by 24 publications

References 49 publications

Large-scale dark matter simulations

Large-scale dark matter simulations

Cosmology with One Galaxy?

Cosmology with one galaxy?

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