Related papers: J-PAS: Measuring emission lines with artificial ne…
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…
J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg$^2$ of the visible sky from Javalambre, capturing data in 56 narrow band filters. This survey…
J-PAS will soon start imaging 8000 deg2 of the northern sky with its unique set of 56 filters (R $\sim$ 60). Before, we observed 1 deg2 on the AEGIS field with an interim camera with all the J-PAS filters. With this data (miniJPAS), we aim…
Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and…
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, we analyze the miniJPAS survey, which observed about 1…
The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict…
This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on…
A new approach to estimating photometric redshifts - using Artificial Neural Networks (ANNs) - is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is…
Upcoming large-area narrow band photometric surveys, such as J-PAS, will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially-resolved stellar populations…
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 counterparts offer a unique opportunity to study the physical…
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is an ongoing survey mapping thousands of square degrees in the Northern Hemisphere using 56 narrow-band filters, delivering IFU-like photometric data well…
The J-PAS survey will observe ~1/3 of the northern sky with a set of 56 narrow-band filters using the dedicated 2.55 m JST telescope at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera, in order to…
The Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) is a narrow band, very wide field Cosmological Survey to be carried out from the Javalambre Observatory in Spain with a purpose-built, dedicated 2.5m telescope…
MiniJPAS is a ~1 deg^2 imaging survey of the AEGIS field in 60 bands, performed to demonstrate the scientific potential of the upcoming JPAS survey. Full coverage of the 3800-9100 \AA range with 54 narrow and 6 broad optical filters allow…
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 spectrophotometric catalogues of miniJPAS, J-NEP and the J-PAS early…
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will soon start to scan thousands of square degrees of the northern extragalactic sky with a unique set of $56$ optical filters from a dedicated $2.55$m…
In the present paper we aim to validate a methodology designed to extract the Halpha emission line flux from J-PLUS photometric data. J-PLUS is a multi narrow-band filter survey carried out with the 2 deg2 field of view T80Cam camera,…
We present The Machine, an artificial neural network (ANN) capable of differentiating between the numbers of Gaussian components needed to describe the emission lines of Integral Field Spectroscopic (IFS) observations. Here we show the…
The application of artificial neural networks (ANNs) for the estimation of HI gas mass fraction (\fgas) is investigated, based on a sample of 13,674 galaxies in the Sloan Digital Sky Survey (SDSS) with HI detections or upper limits from the…
In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan…