19 citations · 34 across the 3 of their papers we have counts for
8 papers
Robust marginalization of baryonic effects for cosmological inference at the field level
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +11
We train neural networks to perform likelihood-free inference from 2D maps containing the total mass surface density from thousands of hydrodynamic simula…
Multifield Cosmology with Artificial Intelligence
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +10
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly und…
Interpreting deep learning models for weak lensing
José Manuel Zorrilla Matilla, Manasi Sharma, Daniel Hsu +1
Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets. Understanding which featu…
Optimizing simulation parameters for weak lensing analyses involving non-Gaussian observables
José Manuel Zorrilla Matilla, Stefan Waterval, Zoltán Haiman
We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in raytracing DM-only simulations, to two hyper-p…
Probing gaseous galactic halos through the rotational kSZ effect
José Manuel Zorrilla Matilla, Zoltán Haiman
The rotational kinematic Sunyaev-Zeldovich (rkSZ) signal, imprinted on the cosmic microwave background (CMB) by the gaseous halos (spinning "atmospheres") of foreground galaxies, w…
Constraining neutrino mass with tomographic weak lensing peak counts
Zack Li, Jia Liu, José Manuel Zorrilla Matilla +1
Massive cosmic neutrinos change the structure formation history by suppressing perturbations on small scales. Weak lensing data from galaxy surveys probe the structure evolution an…