153 citations · 1.2k across the 50 of their papers we have counts for
51 papers
J-PAS & FLAMINGO: Cosmic voids and void galaxies in the gravitational landscape of photometric surveys
J. A. Mansour, B. McCarthy, L. J. Liivamägi +34
Photometric surveys offer a powerful way to map the large-scale structure of the Universe, but their redshift errors complicate the identification of cosmic voids, challenging stud…
J-PAS: unprecedented precision in stellar populations of diffuse tidal features
Sepideh Eskandarlou, Mohammad Akhlaghi, Francisco Arizo-Borillo +38
Galaxies frequently interact with nearby systems, a process that can significantly alter their morphology and star formation activity. However, spectroscopic studies of their faint…
J-PAS: Semi-Supervised Sim-to-Obs Transfer for Robust Star--Galaxy--Quasar Classification
Daniel López-Cano, L. Raul Abramo, L. Nakazono +28
Modern studies in astrophysics and cosmology increasingly rely on simulations and cross-survey analyses, yet differences in data generation, instrumentation, calibration, and unmod…
J-PAS: First Identification, Physical Properties and Ionization Efficiency of Extreme Emission Line Galaxies
A. Giménez-Alcázar, R. Amorín, J. M. Vílchez +32
Extreme emission line galaxies (EELGs) are key tracers of intense star formation and potential analogues of the sources that reionized the early Universe. Their low-redshift counte…
J-PAS: A value-added catalogue of optical line intensities for nebular emission galaxies (JOLINES)
J. A. Fernández-Ontiveros, C. López-Sanjuan, A. Hernán-Caballero +38
We present the value-added catalogue JOLINES (J-PAS optical line intensities for nebular emission galaxies), which provides emission-line fluxes in galaxies at from the spectrophot…
The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation
Ana Paula Jeakel, Gabriel Vieira dos Santos, Valerio Marra +27
We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJP…