Teaching neural networks to generate fast Sunyaev-Zel'dovich maps
The thermal Sunyaev-Zel’dovich (tSZ) and the kinematic Sunyaev-Zel’dovich (kSZ) effects trace the distribution of electron pressure and momentum in the hot universe. These observables depend on rich multiscale physics, thus, simulated maps should ideally be based on calculations that capture baryoni...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020 October 21
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| In: |
The astrophysical journal
Year: 2020, Jahrgang: 902, Heft: 2, Pages: 1-15 |
| ISSN: | 1538-4357 |
| DOI: | 10.3847/1538-4357/abb80f |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3847/1538-4357/abb80f
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| Verfasserangaben: | Leander Thiele, Francisco Villaescusa-Navarro, David N. Spergel, Dylan Nelson, and Annalisa Pillepich |
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| 520 | |a The thermal Sunyaev-Zel’dovich (tSZ) and the kinematic Sunyaev-Zel’dovich (kSZ) effects trace the distribution of electron pressure and momentum in the hot universe. These observables depend on rich multiscale physics, thus, simulated maps should ideally be based on calculations that capture baryonic feedback effects such as cooling, star formation, and other complex processes. In this paper, we train deep convolutional neural networks with a U-Net architecture to map from the three-dimensional distribution of dark matter to electron density, momentum, and pressure at ∼100 kpc resolution. These networks are trained on a combination of the TNG300 volume and a set of cluster zoom-in simulations from the IllustrisTNG project. The neural nets are able to reproduce the power spectrum, one-point probability distribution function, bispectrum, and cross-correlation coefficients of the simulations more accurately than the state-of-the-art semianalytical models. Our approach offers a route to capture the richness of a full cosmological hydrodynamical simulation of galaxy formation with the speed of an analytical calculation. | ||
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