Search Results for author:
Found 3 papers, 1 papers with code
On the dissection of degenerate cosmologies with machine learning
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos.
Distinguishing standard and modified gravity cosmologies with machine learning
We present a convolutional neural network to identify distinct cosmological scenarios based on the weak-lensing maps they produce.
Cosmology and Nongalactic Astrophysics
Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach
In this work we report on our initial attempt to reduce statistical errors in weak lensing shear estimation using a machine learning approach -- training a multi-layered convolutional neural network to directly estimate the shear given an observed background galaxy image.
Cosmology and Nongalactic Astrophysics
