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ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) predicts the dynamical properties of galaxies using the projected phase-space information. Written in R, it offers a choice of machine learning methods to classify the dynamical properties of galaxies. An interface for online use of the software is available at https://mdelosrios.shinyapps.io/roger_shiny/.
Cosmic-kite performs a fast estimation of the TT Cosmic Microwave Background (CMB) power spectra corresponding to a set of cosmological parameters; it can also estimate the maximum-likelihood cosmological parameters from a power spectra. This software is an auto-encoder that was trained and calibrated using power spectra from random cosmologies computed with the CAMB code (ascl:1102.026).
MeSsI performs an automatic classification between merging and relaxed clusters. This method was calibrated using mock catalogues constructed from the millennium simulation, and performs the classification using some machine learning techniques, namely random forest for classification and mixture of gaussians for the substructure identification.