25 citations · 68 across the 11 of their papers we have counts for
11 papers
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: Validating inference from mock X-ray and millimetre analyses of galaxy clusters
F. De Luca, H. Bourdin, P. Mazzotta +6
Measurements of thermodynamical quantities in galaxy clusters are differently affected by simplified modelling of radially averaged observables in the X-ray and millimetre bands. T…
Machine Learning applications to Galaxy Clusters
Gustavo Yepes, Daniel de Andrés
This chapter reviews the application of Artificial Intelligence (AI) techniques to the study of galaxy clusters, covering both theoretical developments and their use as tools to in…
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…
3D scaling laws and projection effects in The300-NIKA2 Sunyaev-Zeldovich Large Program Twin Samples
A. Paliwal, W. Cui, D. de Andrés +10
The abundance of galaxy clusters with mass and redshift is a well-known cosmological probe. The cluster mass is a key parameter for studies that aim to constrain cosmological param…
Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
Ashleigh R. Arendt, Yvette C. Perrott, Ana Contreras-Santos +3
We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq…
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…
Galaxy cluster mass bias from projected mass maps: The Three Hundred-NIKA2 LPSZ twin samples
M. Muñoz-Echeverría, J. F. Macías-Pérez, E. Artis +14
The determination of the mass of galaxy clusters from observations is subject to systematic uncertainties. Beyond the errors due to instrumental and observational systematic effect…
The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps
A. Ferragamo, D. de Andres, A. Sbriglio +9
We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate…
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
Daniel de Andres, Gustavo Yepes, Federico Sembolini +5
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (st…
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…