Daniel de Andres

Profesor Ayudante Doctor

Universidad Rey Juan Carlos

Spain

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About

Daniel de Andres is currently serving as Profesor Ayudante Doctor at Universidad Rey Juan Carlos, Spain. His research primarily focuses on machine learning applications in astrophysics, particularly related to galaxy clusters and their formation. His recent articles explore topics such as predicting halo formation time, identifying galaxy cluster mergers, and estimating observational properties of galaxy clusters using advanced computational techniques.

Articles (10)

Year:

2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

Year:

2022

Year:

2020

Collaborators (3)

Chris Power

University of Western Australia

AUSTRALIA

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Gustavo Yepes

Universidad Autónoma de Madrid

SPAIN

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Weiguang Cui

University of Edinburgh

UNITED KINGDOM

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Daniel de Andres

Research Interests

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Articles (10)

Predicting halo formation time using machine learning

Deep learning generated observations of galaxy clusters from dark-matter-only simulations

Identifying galaxy cluster mergers with deep neural networks using idealized Compton-y and X-ray maps

Galaxy cluster mass bias from projected mass maps

The three hundred project: mapping the matter distribution in galaxy clusters via deep learning from multiview simulated observations

the three hundred project: a machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel’dovich maps

The Three Hundred project: The gizmo-simba run

Machine learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations

A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps

Anisotropic deformations in a class of projectively-invariant metric-affine theories of gravity

Collaborators (3)

Chris Power

Gustavo Yepes

Weiguang Cui

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