1k citations · 2.9k across the 81 of their papers we have counts for
5 papers · 1 filter
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…
J-PLUS: Support Vector Regression to Measure Stellar Parameters
Cunshi Wang, Yu Bai, Haibo Yuan +17
Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and br…
J-PLUS: Support Vector Machine Applied to STAR-GALAXY-QSOClassification
Cunshi Wang, Yu Bai, C. López-Sanjuan +19
Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize m…
J-PLUS: photometric calibration of large area multi-filter surveys with stellar and white dwarf loci
C. López-Sanjuan, J. Varela, D. Cristóbal-Hornillos +22
We present the photometric calibration of the twelve optical passbands observed by the Javalambre Photometric Local Universe Survey (J-PLUS). The proposed calibration method has fo…