1 code implementation • 7 Sep 2020 • Doogesh Kodi Ramanah, Radosław Wojtak, Nikki Arendse
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
no code implementations • 12 Mar 2020 • Doogesh Kodi Ramanah, Radosław Wojtak, Zoe Ansari, Christa Gall, Jens Hjorth
We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i. e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
