Modelling the galaxy–halo connection with machine learning

Delgado, Ana Maria ; Wadekar, Digvijay ; Hadzhiyska, Boryana ; Bose, Sownak ; Hernquist, Lars ; Ho, Shirley

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Mimicking the halo–galaxy connection using machine learning

Natali Soler Matubaro de Santi Natália V. N Rodrigues; Antonio D. Montero Dorta; L. Raul Abramo; Beatriz Tucci; María Celeste Artale

Oxford 2022

Item não circula. Consulte sua biblioteca.(Acessar)

Mimicking the halo–galaxy connection using machine learning

de Santi, Natalí S M ; Rodrigues, Natália V N ; Montero-Dorta, Antonio D ; Abramo, L Raul ; Tucci, Beatriz ; Artale, M Celeste

Oxford University Press

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Finding quadruply imaged quasars with machine learning – I. Methods

A. Akhazhanov Marcos Lima

Oxford 2022

Item não circula. Consulte sua biblioteca.(Acessar)

Cosmological constraints from low redshift 21 cm intensity mapping with machine learning

Camila P Novaes Filipe B Abdalla; Elcio Abdalla; Alessandro Marins

Oxford Blackwell Scientific 2024

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Finding quadruply imaged quasars with machine learning – I. Methods

Akhazhanov, A ; More, A ; Amini, A ; Hazlett, C ; Treu, T ; Birrer, S ; Shajib, A ; Liao, K ; Lemon, C ; Agnello, A ; Nord, B ; Aguena, M ; Allam, S ; Andrade-Oliveira, F ; Annis, J ; Brooks, D ; Buckley-Geer, E ; Burke, D L ; Carnero Rosell, A ; Carrasco Kind, M ; Carretero, J ; Choi, A ; Conselice, C ; Costanzi, M ; da Costa, L N ; Pereira, M E S ; De Vicente, J ; Desai, S ; Dietrich, J P ; Doel, P ; Everett, S ; Ferrero, I ; Finley, D A ; Flaugher, B ; Frieman, J ; García-Bellido, J ; Gerdes, D W ; Gruen, D ; Gruendl, R A ; Gschwend, J ; Gutierrez, G ; Hinton, S R ; Hollowood, D L ; Honscheid, K ; James, D J ; Kim, A G ; Kuehn, K ; Kuropatkin, N ; Lahav, O ; Lima, M ; Lin, H ; Maia, M A G ; March, M ; Menanteau, F ; Miquel, R ; Morgan, R ; Palmese, A ; Paz-Chinchón, F ; Pieres, A ; Plazas Malagón, A A ; Sanchez, E ; Scarpine, V ; Serrano, S ; Sevilla-Noarbe, I ; Smith, M ; Soares-Santos, M ; Suchyta, E ; Swanson, M E C ; Tarle, G ; To, C ; Varga, T N ; Weller, J

United Kingdom: Oxford University Press

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Machine learning and cosmological simulations – II. Hydrodynamical simulations

Kamdar, Harshil M ; Turk, Matthew J ; Brunner, Robert J

London: Oxford University Press

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Star cluster classification in the PHANGS–HST survey: Comparison between human and machine learning approaches

Whitmore, Bradley C ; Lee, Janice C ; Chandar, Rupali ; Thilker, David A ; Hannon, Stephen ; Wei, Wei ; Huerta, E A ; Bigiel, Frank ; Boquien, Médéric ; Chevance, Mélanie ; Dale, Daniel A ; Deger, Sinan ; Grasha, Kathryn ; Klessen, Ralf S ; Kruijssen, J M Diederik ; Larson, Kirsten L ; Mok, Angus ; Rosolowsky, Erik ; Schinnerer, Eva ; Schruba, Andreas ; Ubeda, Leonardo ; Van Dyk, Schuyler D ; Watkins, Elizabeth ; Williams, Thomas

Oxford University Press

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VDES J2325−5229 a z = 2.7 gravitationally lensed quasar discovered using morphology-independent supervised machine learning

Ostrovski, Fernanda ; McMahon, Richard G ; Connolly, Andrew J ; Lemon, Cameron A ; Auger, Matthew W ; Banerji, Manda ; Hung, Johnathan M ; Koposov, Sergey E ; Lidman, Christopher E ; Reed, Sophie L ; Allam, Sahar ; Benoit-Levy, Aurelien ; Bertin, Emmanuel ; Brooks, David ; Buckley-Geer, Elizabeth ; Rosell, Aurelio Carnero ; Kind, Matias Carrasco ; Carretero, Jorge ; Cunha, Carlos E ; da Costa, Luiz N ; Desai, Shantanu ; Diehl, H Thomas ; Dietrich, Jorg P ; Evrard, August E ; Finley, David A ; Flaugher, Brenna ; Fosalba, Pablo ; Frieman, Josh ; Gerdes, David W ; Goldstein, Daniel A ; Gruen, Daniel ; Gruendl, Robert A ; Gutierrez, Gaston ; Honscheid, Klaus ; James, David J ; Kuehn, Kyler ; Kuropatkin, Nikolay ; Lima, Marcos ; Lin, Huan ; Maia, Marcio A G ; Marshall, Jennifer L ; Martini, Paul ; Melchior, Peter ; Miquel, Ramon ; Ogando, Ricardo ; Malagon, Andres Plazas ; Reil, Kevin ; Romer, Kathy ; Sanchez, Eusebio ; Santiago, Basilio ; et. al

London: Oxford University Press

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Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

Cavuoti, S ; Humphrey, A ; Hunt, L K ; Tortora, C ; Auricchio, N ; Bender, R ; Branchini, E ; Brinchmann, J ; Camera, S ; Capobianco, V ; Castellano, M ; Conversi, L ; Corcione, L ; Degaudenzi, H ; Douspis, M ; Dubath, F ; Duncan, C A J ; Dupac, X ; Dusini, S ; Farrens, S ; Frailis, M ; Fumana, M ; Garilli, B ; Gillard, W ; Gillis, B ; Grupp, F ; Hornstrup, A ; Jahnke, K ; Kümmel, M ; Kohley, R ; Kunz, M ; Kurki-Suonio, H ; Ligori, S ; Maiorano, E ; Mansutti, O ; Marggraf, O ; Markovic, K ; Marulli, F ; Maurogordato, S ; Medinaceli, E ; Meneghetti, M ; Merlin, E ; Meylan, G ; Moscardini, L ; Munari, E ; Niemi, S M ; Padilla, C ; Paltani, S ; Pasian, F ; Polenta, G ; Poncet, M ; Renzi, A ; Rhodes, J ; Riccio, G ; Sartoris, B ; Schneider, P ; Scodeggio, M ; Secroun, A ; Seidel, G ; Sirignano, C ; Stanco, L ; Tavagnacco, D ; Taylor, A N ; Tereno, I ; Toledo-Moreo, R ; Tutusaus, I ; Valentijn, E A ; Valenziano, L ; Zacchei, A ; Zamorani, G ; Andreon, S ; Boucaud, A ; Graciá-Carpio, J ; Maino, D ; Mei, S ; Scottez, V ; Tenti, M ; Ballardini, M ; Biviano, A ; Cappi, A ; Casas, S ; Cuby, J ; Escoffier, S ; Ganga, K ; Gozaliasl, G ; Hildebrandt, H ; Kansal, V ; Loureiro, A ; Macías-Pérez, J F ; Mainetti, G ; Marcin, S ; Martinelli, M ; Metcalf, R B ; Patrizii, L ; Peel, A ; Potter, D ; Sakr, Z ; Schirmer, M ; Sereno, M ; Valiviita, J

Oxford University Press

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