Search Results - "Rodrigues, Natália V N"

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data by Rodrigues, Natália V N, Raul Abramo, L, Hirata, Nina S T

“…Abstract Errors in measurements are key to weighting the value of data, but are often neglected in machine learning (ML). We show how convolutional neural…”
Get full text

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties by Rodrigues, Natália V N, Raul Abramo, L, Queiroz, Carolina, Martínez-Solaeche, Ginés, Pérez-Ràfols, Ignasi, Bonoli, Silvia, Chaves-Montero, Jonás, Pieri, Matthew M, González Delgado, Rosa M, Morrison, Sean S, Marra, Valerio, Márquez, Isabel, Hernán-Caballero, A, Díaz-García, L A, Benítez, Narciso, Cenarro, A Javier, Dupke, Renato A, Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, Mendes de Oliveira, Claudia, Moles, Mariano, Sodré, Laerte, Varela, Jesús, Vázquez Ramió, Héctor, Taylor, Keith

“…ABSTRACT Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band…”
Get full text

Mimicking the halo–galaxy connection using machine learning by de Santi, Natalí S M, Rodrigues, Natália V N, Montero-Dorta, Antonio D, Abramo, L Raul, Tucci, Beatriz, Artale, M Celeste

“…ABSTRACT Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When…”
Get full text

High-fidelity reproduction of central galaxy joint distributions with neural networks by Rodrigues, Natália V N, de Santi, Natalí S M, Montero-Dorta, Antonio D, Abramo, L Raul

“…ABSTRACT The relationship between galaxies and haloes is central to the description of galaxy formation and a fundamental step towards extracting precise…”
Get full text

The miniJPAS survey quasar selection – I. Mock catalogues for classification by Queiroz, Carolina, Abramo, L Raul, Rodrigues, Natália V N, Pérez-Ràfols, Ignasi, Martínez-Solaeche, Ginés, Hernán-Caballero, Antonio, Hernández-Monteagudo, Carlos, Lumbreras-Calle, Alejandro, Pieri, Matthew M, Morrison, Sean S, Bonoli, Silvia, Chaves-Montero, Jonás, Chies-Santos, Ana L, Díaz-García, L A, Fernandez-Soto, Alberto, González Delgado, Rosa M, Alcaniz, Jailson, Benítez, Narciso, Javier Cenarro, A, Civera, Tamara, Dupke, Renato A, Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, Mendes de Oliveira, Claudia, Moles, Mariano, Muniesa, David, Sodré, Laerte, Taylor, Keith, Varela, Jesús, Vázquez Ramió, Héctor

“…ABSTRACT In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify…”
Get full text

High-fidelity reproduction of central galaxy joint distributions with Neural Networks by Rodrigues, Natália V. N, de Santi, Natalí S. M, Montero-Dorta, Antonio D, Abramo, L. Raul

“…The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological…”
Get full text

Mimicking the halo-galaxy connection using machine learning by de Santi, Natalí S. M, Rodrigues, Natália V. N, Montero-Dorta, Antonio D, Abramo, L. Raul, Tucci, Beatriz, Artale, M. Celeste

“…Volume 514, 2022, Pages 2463-2478 Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in…”
Get full text

The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE by Pérez-Ràfols, Ignasi, Abramo, L. Raul, Martínez-Solaeche, Ginés, Pieri, Matthew M, Queiroz, Carolina, Rodrigues, Natália V. N, Bonoli, Silvia, Chaves-Montero, Jonás, Morrison, Sean S, Alcaniz, Jailson, Benitez, Narciso, Carneiro, Saulo, Cenarro, Javier, Cristóbal-Hornillos, David, Dupke, Renato, Ederoclite, Alessandro, Delgado, Rosa M. González, Hernán-Caballero, Antonio, López-Sanjuan, Carlos, Marín-Franch, Antonio, Marra, Valerio, de Oliveira, Claudia Mendes, Moles, Mariano, SodréJr, Laerte, Taylor, Keith, Varela, Jesús, Ramió, Héctor Vázquez

“…A&A 678, A144 (2023) We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE…”
Get full text

The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties by Rodrigues, Natália V. N, Abramo, L. Raul, Queiroz, Carolina, Martínez-Solaeche, Ginés, Pérez-Ràfols, Ignasi, Bonoli, Silvia, Chaves-Montero, Jonás, Pieri, Matthew M, Delgado, Rosa M. González, Morrison, Sean S, Marra, Valerio, Márquez, Isabel, Hernán-Caballero, A, Díaz-García, L. A, Benítez, Narciso, Cenarro, A. Javier, Dupke, Renato A, Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, de Oliveira, Claudia Mendes, Moles, Mariano, SodréJr, Laerte, Varela, Jesús, Ramió, Héctor Vázquez, Taylor, Keith

“…Monthly Notices of the Royal Astronomical Society, 2023, 520, 3494-3509 Astrophysical surveys rely heavily on the classification of sources as stars, galaxies…”
Get full text

The miniJPAS survey quasar selection III: Classification with artificial neural networks and hybridisation by Martínez-Solaeche, G, Queiroz, Carolina, Delgado, R. M. González, Rodrigues, Natália V. N, García-Benito, R, Pérez-Ràfols, Ignasi, Abramo, L. Raul, Díaz-García, Luis, Pieri, Matthew M, Chaves-Montero, Jonás, Hernán-Caballero, A, Rodríguez-Martín, J. E, Bonoli, Silvia, Morrison, Sean S, Márquez, Isabel, Vílchez, J. M, López-Sanjuan, C, Cenarro, A. J, Dupke, R. A, Martín-Franch, A, Varel, J, Ramió, H. Vázquez, Cristóbal-Hornillos, D, Moles, M, Alcaniz, J, Benitez, N, Fernández-Ontiveros, J. A, Ederoclite, A, Marra, V, de Oliveira, C. Mendes, Taylor, K

“…A&A 673, A103 (2023) This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were…”
Get full text

The miniJPAS survey quasar selection I: Mock catalogues for classification by Queiroz, Carolina, Abramo, L. Raul, Rodrigues, Natália V. N, Pérez-Ràfols, Ignasi, Martínez-Solaeche, Ginés, Hernán-Caballero, Antonio, Hernández-Monteagudo, Carlos, Lumbreras-Calle, Alejandro, Pieri, Matthew M, Morrison, Sean S, Bonoli, Silvia, Chaves-Montero, Jonás, Chies-Santos, Ana L, Díaz-García, L. A, Fernandez-Soto, Alberto, Delgado, Rosa M. González, Alcaniz, Jailson, Benítez, Narciso, Cenarro, A. Javier, Civera, Tamara, Dupke, Renato A, Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, de Oliveira, Claudia Mendes, Moles, Mariano, SodréJr, Laerte, Taylor, Keith, Varela, Jesús, Ramió, Héctor Vázquez

“…In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar…”
Get full text

The miniJPAS survey quasar selection – I. Mock catalogues for classification by Queiroz, Carolina, Abramo, L. Raul, Rodrigues, Natália V. N., Pérez-Ràfols, Ignasi, Martínez-Solaeche, Ginés, Hernán-Caballero, Antonio, Hernández-Monteagudo, Carlos, Lumbreras-Calle, Alejandro, Pieri, Matthew M., Morrison, Sean S., Bonoli, Silvia, Chaves-Montero, Jonás, Chies-Santos, Ana L., Díaz-García, L. A., Fernandez-Soto, Alberto, González Delgado, Rosa M., Alcaniz, Jailson, Benítez, Narciso, Javier Cenarro, A., Civera, Tamara, Dupke, Renato A., Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, Mendes de Oliveira, Claudia, Moles, Mariano, Muniesa, David, Sodré, Laerte, Taylor, Keith, Varela, Jesús, Vázquez Ramió, Héctor

“…ABSTRACT In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify…”
Get full text

Exploring the halo-galaxy connection with probabilistic approaches by Rodrigues, Natália V. N, de Santi, Natalí S. M, Abramo, L. Raul, Montero-Dorta, Antonio D

“…The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and…”
Get full text

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data by Rodrigues, Natália V. N, Abramo, L. Raul, Hirata, Nina S

“…Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks…”
Get full text