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
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
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.
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