From the 1 of 7 linked papers with an AI index.
7 papers
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…
Probing the baryonic--dark matter connection in galaxy clusters using X-rays with gated recurrent unit neural networks
Asif Iqbal, Subhabrata Majumdar, Weiguang Cui +3
Accurate cluster mass measurements are crucial for cosmology, yet conventional hydrostatic equilibrium (HSE) methods can suffer from systematic biases, particularly in dynamically…
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…
Deriving accurate galaxy cluster masses using X-ray thermodynamic profiles and graph neural networks
Asif Iqbal, Subhabrata Majumdar, Elena Rasia +4
Precise determination of galaxy cluster masses is crucial for establishing reliable mass-observable scaling relations in cluster cosmology. We employ graph neural networks (GNNs) t…
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…
Constraining neutrino mass with the CSST galaxy clusters
Mingjing Chen, Yufei Zhang, Wenjuan Fang +2
With the advent of next-generation surveys, constraints on cosmological parameters are anticipated to become more stringent, particularly for the total neutrino mass. This study fo…