The SiTian project, designed to utilize 60 telescopes distributed across multiple sites in China, is a next-generation time-domain survey initiative. As a...
The miniJPAS survey quasar selection - I. Mock catalogues for classification
In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar...
The miniJPAS survey. Identification and characterization of galaxy populations with the J-PAS photometric system
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will soon start imaging thousands of square degrees of the northern sky with...
The miniJPAS survey: A preview of the Universe in 56 colors
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will scan thousands of square degrees of the northern sky with a unique set of...
The miniJPAS survey: AGN and host galaxy coevolution of X-ray-selected sources
Studies indicate strong evidence of a scaling relation in the local Universe between the supermassive black hole mass (M BH) and the stellar mass of their host...
The miniJPAS survey: Exploring the spatially resolved capabilities of the J-PAS survey with Py2DJPAS
This work presents Py2DJPAS, a tool developed in Python to automate the analysis of the properties of spatially resolved galaxies in the miniJPAS survey, a 1...
The miniJPAS survey: Maximising the photo-z accuracy from multi-survey datasets with probability conflation
We present a new method for obtaining photometric redshifts (photo-z) for sources observed by multiple photometric surveys using a combination (conflation) of...
The miniJPAS survey: Photometric redshift catalogue
MiniJPAS is a ∼1 deg 2 imaging survey of the AEGIS field in 60 bands, performed to demonstrate the scientific potential of the upcoming Javalambre-Physics of...
The miniJPAS survey: star-galaxy classification using machine learning
Context. Future astrophysical surveys such as J-PAS will produce very large datasets, the so-called "big data", which will require the deployment of accurate...