From the 1 of 9 linked papers with an AI index.
3 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
Identifying backsplash galaxies using machine learning
Roan Haggar, Elizaveta Sazonova, Cameron R. Morgan +5
The paper presents a machine‑learning model trained on The Three Hundred cluster simulations that can identify backsplash galaxies in observations, achieving about 70% purity/compl…
The Three Hundred Project: deducing the stellar splashback structure of galaxy clusters from their orbiting profiles
Kris Walker, Aaron Ludlow, Chris Power +2
We examine the splashback structure of galaxy clusters using hydrodynamical simulations from the GIZMO run of The Three Hundred Project, focusing on the relationship between the st…
The life and times of dark matter haloes: what will I be when I grow up?
Julian Onions, Frazer Pearce, Alexander Knebe +6
Are the most massive objects in the Universe today the direct descendants of the most massive objects at higher redshift? We address this question by tracing the evolutionary histo…
PyMGal: A Python Package for Generating Optical Mock Observations from Hydrodynamical Simulations
Patrick Janulewicz, Weiguang Cui
We introduce PyMGal, a Python package for generating optical mock observations of galaxies from hydrodynamical simulations. PyMGal reads the properties of stellar particles from th…
Reconsidering the dynamical states of galaxy clusters using PCA and UMAP
Roan Haggar, Federico De Luca, Marco De Petris +10
Numerous metrics exist to quantify the dynamical state of galaxy clusters, both observationally and within simulations. Many of these correlate strongly with one another, but it is…