Interpreting deep learning models for weak lensing

no code implementations • 13 Jul 2020 • José Manuel Zorrilla Matilla, Manasi Sharma, Daniel Hsu, Zoltán Haiman

Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets.

Cosmology and Nongalactic Astrophysics

Weak lensing cosmology with convolutional neural networks on noisy data

1 code implementation • 10 Feb 2019 • Dezső Ribli, Bálint Ármin Pataki, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman, István Csabai

Previous studies attempted to extract non-Gaussian information from weak lensing observations through several higher-order statistics such as the three-point correlation function, peak counts or Minkowski-functionals.

Cosmology and Nongalactic Astrophysics

Non-Gaussian information from weak lensing data via deep learning

no code implementations • 4 Feb 2018 • Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman

Weak lensing maps contain information beyond two-point statistics on small scales.

Geometry and growth contributions to cosmic shear observables

no code implementations • 16 Jun 2017 • José Manuel Zorrilla Matilla, Zoltán Haiman, Andrea Petri, Toshiya Namikawa

We explore the sensitivity of weak lensing observables to the expansion history of the universe and to the growth of cosmic structures, as well as the relative contribution of both effects to constraining cosmological parameters.

Cosmology and Nongalactic Astrophysics