Multifield Cosmology with Artificial Intelligence

Villaescusa-Navarro, Francisco; Anglés-Alcázar, Daniel; Genel, Shy; Spergel, David N.; Li, Yin; Wandelt, Benjamin; Nicola, Andrina; Thiele, Leander; Hassan, Sultan; Matilla, Jose Manuel Zorrilla; Narayanan, Desika; Dave, Romeel; Vogelsberger, Mark

doi:10.48550/arxiv.2109.09747

Cited by 30 publications

(67 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3, indicating that their inclusion may benefit semi-empirical models such as SHAM, especially in satellites. That the features required to predicted masses are similar for both simulations is consistent with other work (Villaescusa-Navarro et al 2021a;Chen et al 2021), although we find that the relevant features for other properties (e.g. 𝑅 1/2 ) are simulation-dependent.…”

Section: The Galaxy-halo Connections In the Horizon-agn And Illustris...supporting

confidence: 91%

See 1 more Smart Citation

The scatter in the galaxy-halo connection: a machine learning analysis

Stiskalek,

Bartlett,

Desmond

et al. 2022

Preprint

View full text Add to dashboard Cite

We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxyhalo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy-halo connection as well as its best-fit trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy-halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-to-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.

show abstract

Section: The Galaxy-halo Connections In the Horizon-agn And Illustris...supporting

confidence: 91%

“…The disadvantage is that the results are tied to a particular simulation with no guarantee of accuracy should that simulation fail to describe the real Universe. Indeed, recent results have indicated that ML algorithms trained on one simulation will not in general be successful at reproducing others (Villaescusa- Navarro et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

The scatter in the galaxy-halo connection: a machine learning analysis

Stiskalek,

Bartlett,

Desmond

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Comparing our results to the constraints reported in Ref. [47], we find the uncertainties reported in this work to be significantly larger. We mainly attribute this to the fact that the analysis in Ref.…”

Section: Statistical Uncertaintiessupporting

confidence: 71%

Breaking baryon-cosmology degeneracy with the electron density power spectrum

Nicola,

Villaescusa-Navarro,

Spergel

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback. We use a suite of (magneto-)hydrodynamic simulations from the CAMELS project and perform an idealized analysis to forecast statistical uncertainties on a limited set of cosmological and physically-motivated astrophysical parameters. We find that the electron number density auto-correlation, measurable through either kinematic Sunyaev-Zel'dovich observations or through Fast Radio Burst dispersion measures, provides tight constraints on Ω m and the mean baryon fraction in intermediate-mass halos, fbar . By obtaining an empirical measure for the associated systematic uncertainties, we find these constraints to be largely robust to differences in baryonic feedback models implemented in hydrodynamic simulations. We further discuss the main caveats associated with our analysis, and point out possible directions for future work.8 Here, M denotes the mass of the Sun. 9 https://arepo-code.org. 10 http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.html. 11 The remaining cosmological parameters are fixed to Ω b = 0.049, h = 0.6711, ns = 0.9624 within ΛCDM.

show abstract

“…On the other hand, results are not completely off; for instance see Fig. 4 of Villaescusa-Navarro et al (2021b) for a similar exercise with 2D maps. We find that the true value of Ω m lies within the model standard deviation in a large fraction of galaxies, although there is obviously a large underlying bias.…”

Section: Appendixmentioning

confidence: 95%

“…neural networks, to find an approximation to the optimal estimator (Ravanbakhsh et al 2017;Schmelzle et al 2017;Gupta et al 2018;Ribli et al 2019;Fluri et al 2019;Ntampaka et al 2019;Hassan et al 2020;Zorrilla Matilla et al 2020;Villaescusa-Navarro et al 2021a;Lu et al 2021). Recent works have shown that even for fields that are very contaminated by astrophysical effects, it is possible to extract cosmological information from small scales (Villaescusa-Navarro et al 2021b).…”

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

Multifield Cosmology with Artificial Intelligence

Cited by 30 publications

References 39 publications

The scatter in the galaxy-halo connection: a machine learning analysis

The scatter in the galaxy-halo connection: a machine learning analysis

Breaking baryon-cosmology degeneracy with the electron density power spectrum

Cosmology with one galaxy?

Contact Info

Product

Resources

About