De Andrés Hernández, Daniel daniel.deandres@uam.es
Publications
- Articles 10
- Books 0
- Book chapters 0
- Conferences 0
- Working papers 0
- Technical reports 0
- Research projects 0
- Supervised theses 0
- Patent or software license 0
Identifying galaxy cluster mergers with deep neural networks using idealized Compton-y and X-ray maps
- Arendt, A.R.
- Perrott, Y.C.
- Contreras-Santos, A.
- de Andrés, D.
- Cui, W.
- Rennehan, D.
Monthly Notices Of The Royal Astronomical Society (p. 20-34) - 5/4/2024
10.1093/mnras/stae568 View at source
- ISSN 00358711
Generating galaxy clusters mass density maps from mock multiview images via deep learning
- de Andrés, D.
- Cui, W.
- Yepes, G.
- de Petris, M.
- Aversano, G.
- Ferragamo, A.
- de Luca, F.
- Jiménez Muñoz, A.
Epj Web Of Conferences - 28/3/2024
10.1051/epjconf/202429300013 View at source
- ISSN 2100014X
Galaxy cluster mass bias from projected mass maps
- Muñoz-Echeverría, M.
- Macías-Pérez, J.F.
- Artis, E.
- Cui, W.
- de Andres, D.
- De Luca, F.
- De Petris, M.
- Ferragamo, A.
- Giocoli, C.
- Hanser, C.
- Mayet, F.
- Meneghetti, M.
- Moyer-Anin, A.
- Paliwal, A.
- Perotto, L.
- Rasia, E.
- Yepes, G.
Astronomy & Astrophysics - 1/2/2024
10.1051/0004-6361/202346986 View at source
- ISSN 14320746
The three hundred project: mapping the matter distribution in galaxy clusters via deep learning from multiview simulated observations
- de Andres, Daniel
- Cui, Weiguang
- Yepes, Gustavo
- De Petris, Marco
- Ferragamo, Antonio
- De Luca, Federico
- Aversano, Gianmarco
- Rennehan, Douglas
Monthly Notices Of The Royal Astronomical Society (p. 1517-1530) - 23/1/2024
10.1093/mnras/stae071 View at source
- ISSN 00358711
THE THREE HUNDRED project: a machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel’dovich maps
- Ferragamo, A.
- de Andrés, D.
- Sbriglio, A.
- Cui, W.
- De Petris, M.
- Yepes, G.
- Dupuis, R.
- Jarraya, M.
- Lahouli, I.
- De Luca, F.
- Gianfagna, G.
- Rasia, E.
Monthly Notices Of The Royal Astronomical Society (p. 4000-4008) - 1/4/2023
10.1093/mnras/stad377 View at source
- ISSN 00358711
Machine learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
- de Andrés, D.
- Yepes, G.
- Sembolini, F.
- Martínez-Muñoz, G.
- Cui, W.G.
- Robledo, F.
- Chuang, C.H.
- Rasia, E.
Monthly Notices Of The Royal Astronomical Society (p. 111-129) - 1/1/2023
10.1093/mnras/stac3009 View at source
- ISSN 00358711
THE THREE HUNDRED project: The GIZMO-SIMBA run
- Cui, Weiguang
- Dave, Romeel
- Knebe, Alexander
- Rasia, Elena
- Gray, Meghan
- Pearce, Frazer
- Power, Chris
- Yepes, Gustavo
- Anbajagane, Dhayaa
- Ceverino, Daniel
- Contreras-Santos, Ana
- de Andres, Daniel
- De Petris, Marco
- Ettori, Stefano
- Haggar, Roan
- Li, Qingyang
- Wang, Yang
- Yang, Xiaohu
- Borgani, Stefano
- Dolag, Klaus
- Zu, Ying
- Kuchner, Ulrike
- Canas, Rodrigo
- Ferragamo, Antonio
- Gianfagna, Giulia
Monthly Notices Of The Royal Astronomical Society (p. 977-996) - 1/7/2022
10.1093/mnras/stac1402 View at source
- ISSN 00358711
A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps
- de Andres, Daniel
- Cui, Weiguang
- Ruppin, Florian
- De Petris, Marco
- Yepes, Gustavo
- Gianfagna, Giulia
- Lahouli, Ichraf
- Aversano, Gianmarco
- Dupuis, Romain
- Jarraya, Mahmoud
- Vega-Ferrero, Jesus;
Nature Astronomy (p. 1325-1331) - 1/11/2022
10.1038/s41550-022-01784-y View at source
- ISSN 23973366
Mass Estimation of Planck Galaxy Clusters using Deep Learning
- Daniel de Andrés
- Weiguang Cui
- F. Ruppin
- M. De Petris
- Gustavo Yepes
- Ichraf Lahouli
- Gianmarco Aversano
- Romain Dupuis
- Mahmoud Jarraya
Epj Web Of Conferences (p. 00013-00013) - 17/1/2022
10.1051/epjconf/202225700013 View at source
- ISSN 2100014X
Anisotropic deformations in a class of projectively-invariant metric-affine theories of gravity
- Jimenez, Jose Beltran
- de Andres, Daniel
- Delhom, Adria;
CLASSICAL AND QUANTUM GRAVITY - 1/11/2020
10.1088/1361-6382/abb923 View at source
- ISSN 02649381
This researcher has no books.
This researcher has no book chapters.
This researcher has no conferences.
This researcher has no working papers.
This researcher has no technical reports.
This researcher has no research projects.
This researcher has no supervised thesis.
This researcher has no patents or software licenses.
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