The miniJPAS survey: Identification and characterization of the emission line galaxies down to $z < 0.35$ in the AEGIS field
G. Mart\'inez-Solaeche, R. M. Gonz\'alez Delgado, R. Garc\'ia-Benito,, L. A. D\'iaz-Garc\'ia, J. E. Rodr\'iguez-Mart\'in, E. P\'erez, A. de Amorim,, S. Duarte Puertas, Laerte Sodr\'e Jr., David Sobral, Jon\'as Chaves-Montero,, J. M. V\'ilchez, A. Hern\'an-Caballero

TL;DR
This study uses miniJPAS data to identify and analyze emission line galaxies below redshift 0.35, employing neural networks to measure emission lines and classify galaxy types, providing insights into star formation and galaxy evolution.
Contribution
It introduces a neural network-based method for detecting and characterizing emission line galaxies in miniJPAS, expanding the understanding of galaxy properties at low redshift.
Findings
Identified 1787 emission line galaxies in the AEGIS field.
Determined galaxy classifications: ~73% star-forming, ~18% Seyfert, ~9% quiescent.
Derived the star formation main sequence and cosmic SFR density evolution.
Abstract
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is expected to map thousands of square degrees of the northern sky with 56 narrowband filters in the upcoming years. This will make J-PAS a very competitive and unbiased emission line survey compared to spectroscopic or narrowband surveys with fewer filters. The miniJPAS survey covered 1 deg, and it used the same photometric system as J-PAS, but the observations were carried out with the pathfinder J-PAS camera. In this work, we identify and characterize the sample of emission line galaxies (ELGs) from miniJPAS with a redshift lower than . Using a method based on artificial neural networks, we detect the ELG population and measure the equivalent width and flux of the , , [OIII], and [NII] emission lines. We explore the ionization mechanism using the diagrams [OIII]/H versus…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
