35 citations · 50 across the 6 of their papers we have counts for
6 papers
Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning
Daniel de Andres, Weiguang Cui, Gustavo Yepes +5
Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80\% of the total mass, cannot be directly observed but traced by t…
The Three Hundred Project: Mapping The Matter Distribution in Galaxy Clusters Via Deep Learning from Multiview Simulated Observations
Daniel de Andres, Weiguang Cui, Gustavo Yepes +5
A galaxy cluster as the most massive gravitationally-bound object in the Universe, is dominated by Dark Matter, which unfortunately can only be investigated through its interaction…
SANGEA: Scalable and Attributed Network Generation
Valentin Lemaire, Youssef Achenchabe, Lucas Ody +4
The topic of synthetic graph generators (SGGs) has recently received much attention due to the wave of the latest breakthroughs in generative modelling. However, many state-of-the-…
A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton parameter maps
Daniel de Andres, Weiguang Cui, Florian Ruppin +8
Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological para…
Advancing Reacting Flow Simulations with Data-Driven Models
Kamila Zdybał, Giuseppe D'Alessio, Gianmarco Aversano +4
The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems,…
Mass Estimation of Planck Galaxy Clusters using Deep Learning
Daniel de Andres, Weiguang Cui, Florian Ruppin +6
Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some…