13 citations · 26 across the 5 of their papers we have counts for
5 papers
Towards Practical Field-Level Inference for Weak Lensing
Yuuki Omori, Justine Zeghal, Chihway Chang +2
Nonlinear structure growth generates higher-order correlations and morphological features in the cosmic density field that cannot be fully characterized by two-point statistics. Up…
LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators
Andreas Filipp, Yashar Hezaveh, Laurence Perreault-Levasseur +2
Strong gravitational lensing offers a unique probe of dark matter (DM) on sub-galactic scales, where the abundance and distribution of low-mass halos are highly sensitive to the un…
Using Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys
Patrick Janulewicz, Tracy M. A. Webb, Laurence Perreault-Levasseur
Brightest cluster galaxies (BCGs) lie deep within the largest gravitationally bound structures in existence. Though some cluster finding techniques identify the position of the BCG…
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Aritra Ghosh, C. Megan Urry, Aayush Mishra +10
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for million galaxies in the Hyper Supr…
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…