activity
20202026
most citedThe miniJPAS survey: star-galaxy classification using machine learning

55 citations · 97 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.SR2026

Stellar characterization with photometric colors from J-PLUS and 2MASS surveys

J. F. Aguilar, P. Cruz, E. Solano +18

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared ph…

astro-ph.SR2025

J-VAR: the northern variable sky in 7 filters -- First Data Release

A. Ederoclite, H. Vázquez Ramió, A. Alvarez-Candal +37

Aims. The analysis of variability of astronomical sources is of extraordinary interest, as it allows the study of astrophysical phenomena in real time. This paper presents the Java…

astro-ph.SR2024★ 42 cited

Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools

Carlos Rodrigo, Patricia Cruz, John F. Aguilar +18

This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and as…

astro-ph.SR2021

J-PLUS: Searching for very metal-poor star candidates using the SPEEM pipeline

Carlos Andrés Galarza, Simone Daflon, Vinicius M. Placco +25

We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential to identify low-metallicity stars using the Stella…

astro-ph.IM2020★ 55 cited

The miniJPAS survey: star-galaxy classification using machine learning

P. O. Baqui, V. Marra, L. Casarini +31

Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work…