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The miniJPAS and J-NEP Surveys: Machine Learning for Star-Galaxy Separation

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset...

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Hoofdauteurs: Ana Paula Jeakel, Gabriel Vieira dos Santos, Valerio Marra, Rodrigo von Marttens, Siddhartha Gurung-López, Raul Abramo, Jailson Alcaniz, Narciso Benitez, Silvia Bonoli, Javier Cenarro, David Cristóbal-Hornillos, Simone Daflon, Renato Dupke, Alessandro Ederoclite, Rosa M. González Delgado, Antonio Hernán-Caballero, Carlos Hernández-Monteagudo, Jifeng Liu, Carlos López-Sanjuan, Antonio Marín-Franch, Claudia Mendes de Oliveira, Mariano Moles, Fernando Roig, Laerte Sodré, Keith Taylor, Jesús Varela, Héctor Vázquez Ramió, José M. Vilchez, Christopher Willmer, Javier Zaragoza-Cardiel
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: MDPI AG 2026-01-01
Reeks:Galaxies
Onderwerpen:
astronomy
galaxies
stars
data analysis
machine learning
Online toegang:https://www.mdpi.com/2075-4434/14/1/6
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