Search Results for author: Leander Thiele

Found 8 papers, 4 papers with code

Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks

1 code implementation28 Feb 2022 Leander Thiele, Miles Cranmer, William Coulton, Shirley Ho, David N. Spergel

We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations.

Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter

1 code implementation4 Jan 2022 Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, Miles Cranmer, David N. Spergel, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, Shirley Ho

Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$.

Symbolic Regression

Robust marginalization of baryonic effects for cosmological inference at the field level

no code implementations21 Sep 2021 Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, David N. Spergel, Yin Li, Benjamin Wandelt, Leander Thiele, Andrina Nicola, Jose Manuel Zorrilla Matilla, Helen Shao, Sultan Hassan, Desika Narayanan, Romeel Dave, Mark Vogelsberger

We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.

Multifield Cosmology with Artificial Intelligence

no code implementations20 Sep 2021 Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Yin Li, Benjamin Wandelt, Andrina Nicola, Leander Thiele, Sultan Hassan, Jose Manuel Zorrilla Matilla, Desika Narayanan, Romeel Dave, Mark Vogelsberger

Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.

Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps

no code implementations14 Jul 2020 Leander Thiele, Francisco Villaescusa-Navarro, David N. Spergel, Dylan Nelson, Annalisa Pillepich

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.

Cosmology and Nongalactic Astrophysics

Cannot find the paper you are looking for? You can Submit a new open access paper.