153 citations · 1.2k across the 50 of their papers we have counts for
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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…
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…
J-PLUS: Turning Off the Bright Stars
Sepideh Eskandarlou, Mohammad Akhlaghi, Johan H. Knapen +23
Photometric surveys require precise point spread function (PSF) characterization, as it varies across filters and is crucial for accurate photometry and low surface brightness (LSB…
The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE
Ignasi Pérez-Ràfols, L. Raul Abramo, Ginés Martínez-Solaeche +24
We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning clas…
The miniJPAS Survey: A Study on Wavelength Dependence of the Photon Response Non-uniformity of the JPAS-{\it Pathfinder} Camera
Kai Xiao, Haibo Yuan, J. Varela +20
Understanding the origins of small-scale flats of CCDs and their wavelength-dependent variations plays an important role in high-precision photometric, astrometric, and shape measu…
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…