Author:
Bonoli S.,Marín-Franch A.,Varela J.,Vázquez Ramió H.,Abramo L. R.,Cenarro A. J.,Dupke R. A.,Vílchez J. M.,Cristóbal-Hornillos D.,González Delgado R. M.,Hernández-Monteagudo C.,López-Sanjuan C.,Muniesa D. J.,Civera T.,Ederoclite A.,Hernán-Caballero A.,Marra V.,Baqui P. O.,Cortesi A.,Cypriano E. S.,Daflon S.,de Amorim A. L.,Díaz-García L. A.,Diego J. M.,Martínez-Solaeche G.,Pérez E.,Placco V. M.,Prada F.,Queiroz C.,Alcaniz J.,Alvarez-Candal A.,Cepa J.,Maroto A. L.,Roig F.,Siffert B. B.,Taylor K.,Benitez N.,Moles M.,Sodré L.,Carneiro S.,Mendes de Oliveira C.,Abdalla E.,Angulo R. E.,Aparicio Resco M.,Balaguera-Antolínez A.,Ballesteros F. J.,Brito-Silva D.,Broadhurst T.,Carrasco E. R.,Castro T.,Cid Fernandes R.,Coelho P.,de Melo R. B.,Doubrawa L.,Fernandez-Soto A.,Ferrari F.,Finoguenov A.,García-Benito R.,Iglesias-Páramo J.,Jiménez-Teja Y.,Kitaura F. S.,Laur J.,Lopes P. A. A.,Lucatelli G.,Martínez V. J.,Maturi M.,Overzier R. A.,Pigozzo C.,Quartin M.,Rodríguez-Martín J. E.,Salzano V.,Tamm A.,Tempel E.,Umetsu K.,Valdivielso L.,von Marttens R.,Zitrin A.,Díaz-Martín M. C.,López-Alegre G.,López-Sainz A.,Yanes-Díaz A.,Rueda-Teruel F.,Rueda-Teruel S.,Abril Ibañez J.,L Antón Bravo J.,Bello Ferrer R.,Bielsa S.,Casino J. M.,Castillo J.,Chueca S.,Cuesta L.,Garzarán Calderaro J.,Iglesias-Marzoa R.,Íniguez C.,Lamadrid Gutierrez J. L.,Lopez-Martinez F.,Lozano-Pérez D.,Maícas Sacristán N.,Molina-Ibáñez E. L.,Moreno-Signes A.,Rodríguez Llano S.,Royo Navarro M.,Tilve Rua V.,Andrade U.,Alfaro E. J.,Akras S.,Arnalte-Mur P.,Ascaso B.,Barbosa C. E.,Beltrán Jiménez J.,Benetti M.,Bengaly C. A. P.,Bernui A.,Blanco-Pillado J. J.,Borges Fernandes M.,Bregman J. N.,Bruzual G.,Calderone G.,Carvano J. M.,Casarini L.,Chaves-Montero J.,Chies-Santos A. L.,Coutinho de Carvalho G.,Dimauro P.,Duarte Puertas S.,Figueruelo D.,González-Serrano J. I.,Guerrero M. A.,Gurung-López S.,Herranz D.,Huertas-Company M.,Irwin J. A.,Izquierdo-Villalba D.,Kanaan A.,Kehrig C.,Kirkpatrick C. C.,Lim J.,Lopes A. R.,Lopes de Oliveira R.,Marcos-Caballero A.,Martínez-Delgado D.,Martínez-González E.,Martínez-Somonte G.,Oliveira N.,Orsi A. A.,Penna-Lima M.,Reis R. R. R.,Spinoso D.,Tsujikawa S.,Vielva P.,Vitorelli A. Z.,Xia J. Q.,Yuan H. B.,Arroyo-Polonio A.,Dantas M. L. L.,Galarza C. A.,Gonçalves D. R.,Gonçalves R. S.,Gonzalez J. E.,Gonzalez A. H.,Greisel N.,Jiménez-Esteban F.,Landim R. G.,Lazzaro D.,Magris G.,Monteiro-Oliveira R.,Pereira C. B.,Rebouças M. J.,Rodriguez-Espinosa J. M.,Santos da Costa S.,Telles E.
Abstract
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 56 filters using the dedicated 2.55 m Javalambre Survey Telescope (JST) at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera (4.2 deg2 field-of-view with 1.2 Gpixels), the JST was equipped with the JPAS-Pathfinder, a one CCD camera with a 0.3 deg2 field-of-view and plate scale of 0.23 arcsec pixel−1. To demonstrate the scientific potential of J-PAS, the JPAS-Pathfinder camera was used to perform miniJPAS, a ∼1 deg2 survey of the AEGIS field (along the Extended Groth Strip). The field was observed with the 56 J-PAS filters, which include 54 narrow band (FWHM ∼ 145 Å) and two broader filters extending to the UV and the near-infrared, complemented by the u, g, r, i SDSS broad band filters. In this miniJPAS survey overview paper, we present the miniJPAS data set (images and catalogs), as we highlight key aspects and applications of these unique spectro-photometric data and describe how to access the public data products. The data parameters reach depths of magAB ≃ 22−23.5 in the 54 narrow band filters and up to 24 in the broader filters (5σ in a 3″ aperture). The miniJPAS primary catalog contains more than 64 000 sources detected in the r band and with matched photometry in all other bands. This catalog is 99% complete at r = 23.6 (r = 22.7) mag for point-like (extended) sources. We show that our photometric redshifts have an accuracy better than 1% for all sources up to r = 22.5, and a precision of ≤0.3% for a subset consisting of about half of the sample. On this basis, we outline several scientific applications of our data, including the study of spatially-resolved stellar populations of nearby galaxies, the analysis of the large scale structure up to z ∼ 0.9, and the detection of large numbers of clusters and groups. Sub-percent redshift precision can also be reached for quasars, allowing for the study of the large-scale structure to be pushed to z > 2. The miniJPAS survey demonstrates the capability of the J-PAS filter system to accurately characterize a broad variety of sources and paves the way for the upcoming arrival of J-PAS, which will multiply this data by three orders of magnitude.