activity
20182026
most citedThe CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

69 citations · 491 across the 27 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

astro-ph.CO2022★ 28 cited

The SZ flux-mass (-) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback

Digvijay Wadekar, Leander Thiele, J. Colin Hill +8

Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos () from CMB surveys, and cause its relation with the…

astro-ph.CO2022★ 9 cited

An exploration of the properties of cluster profiles for the thermal and kinetic Sunyaev-Zel'dovich effects

Billy K. K. Lee, William R. Coulton, Leander Thiele +1

With the advent of high-resolution, low-noise CMB measurements, the ability to extract cosmological information from thermal Sunyaev-Zel'dovich effect and kinetic Sunyaev-Zel'dovic…

astro-ph.CO2022

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

Leander Thiele, Miles Cranmer, William Coulton +2

Theoretical uncertainty limits our ability to extract cosmological information from baryonic fields such as the thermal Sunyaev-Zel'dovich (tSZ) effect. Being sourced by the electr…

astro-ph.CO2022★ 67 cited

The CAMELS project: public data release

Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…

astro-ph.CO2022★ 15 cited

Percent-level constraints on baryonic feedback with spectral distortion measurements

Leander Thiele, Digvijay Wadekar, J. Colin Hill +7

High-significance measurements of the monopole thermal Sunyaev-Zel'dovich CMB spectral distortions have the potential to tightly constrain poorly understood baryonic feedback proce…

astro-ph.CO2022★ 28 cited

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

Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro +7

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations…