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 2.55m JST at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera (4.2 deg 2 field-of-view with 1.2 Gpixels), the JST was equipped with the JPAS-Pathfinder, a one CCD camera with a 0.3 deg 2 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 deg 2 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 (NB, 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 (BB) 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 mag AB 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. For reference, the miniJPAS data and associated value added catalogs are publicly available http://archive.cefca.es/catalogues/minijpas-pdr201912.

We present a three-dimensional model of polarised galactic dust emission that takes into account the variation of the dust density, spectral index and temperature along the line of sight, and contains randomly generated small scale polarisation fluctuations. The model is constrained to match observed dust emission on large scales, and match on smaller scales extrapolations of observed intensity and polarisation power spectra. This model can be used to investigate the impact of plausible complexity of the polarised dust foreground emission on the analysis and interpretation of future CMB polarisation observations.

In the years to come, the Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) will observe 8000 deg2 of the northern sky with 56 photometric bands. J-PAS is ideal for the detection of nebular emission objects. This paper presents a new method based on artificial neural networks (ANNs) that is aimed at measuring and detecting emission lines in galaxies up to z = 0.35. These lines are essential diagnostics for understanding the evolution of galaxies through cosmic time. We trained and tested ANNs with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. To this aim, we carried out two tasks. First, we clustered galaxies in two groups according to the values of the equivalent width (EW) of Hα, Hβ, [N II], and [O III] lines measured in the spectra. Then we trained an ANN to assign a group to each galaxy. We were able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Second, we utilized another ANN to determine the values of those EWs. Subsequently, we obtained the [N II]/Hα, [O III]/Hβ, and O 3N 2 ratios, recovering the BPT diagram ([O III]/Hβ versus [N II]/Hα). We studied the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We were able to fully reproduce the main sequence of star-forming galaxies from the determination of the EWs. With the CALMa training set, we reached a precision of 0.092 and 0.078 dex for the [N II]/Hα and [O III]/Hβ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an active galactic nuclei. We also show the importance of the dataset used for both training and testing the model. Such ANNs are extremely useful for overcoming the limitations previously expected concerning the detection and measurements of the emission lines in such surveys as J-PAS. Furthermore, we show the capability of the method to measure a EW of 10 Å in Hα, Hβ, [N II] and [O III] lines with a signal-to-noise ratio (S/N) of 5, 1.5, 3.5, and 10, respectively, in the photometry. Finally, we compare the properties of emission lines in galaxies observed with miniJPAS and SDSS. Despite the limitation of such a comparison, we find a remarkable correlation in their EWs.

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 (spectral resolution of R ∼ 60) in the upcoming years. This resolution allows us to study emission line galaxies (ELGs) with a minimum equivalent width of 10 Å in the Hα emission line for a median signal-to-noise ratio (S/N) of 5. 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 deg2, 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 ELGs from miniJPAS with a redshift lower than 0.35, which is the limit to which the Hα line can be observed with the J-PAS filter system. Using a method based on artificial neural networks, we detect the ELG population and measure the equivalent width and flux of the Hα, Hβ, [O III], and [N II] emission lines. We explore the ionization mechanism using the diagrams [OIII]/Hβ versus [NII]/Hα (BPT) and EW(Hα) versus [NII]/Hα (WHAN). We identify 1787 ELGs (83%) from the parent sample (2154 galaxies) in the AEGIS field. For the galaxies with reliable EW values that can be placed in the WHAN diagram (2000 galaxies in total), we obtained that 72.8 ± 0.4%, 17.7 ± 0.4%, and 9.4 ± 0.2% are star-forming (SF), active galactic nucleus (Seyfert), and quiescent galaxies, respectively. The distribution of EW(Hα) is well correlated with the bimodal color distribution of galaxies. Based on the rest-frame (u − r)–stellar mass diagram, 94% of the blue galaxies are SF galaxies, and 97% of the red galaxies are LINERs or passive galaxies. The nebular extinction and star formation rate (SFR) were computed from the Hα and Hβ fluxes. We find that the star formation main sequence is described as log SFR [M⊙ yr−1] = 0.90−0.02+0.02 log M⋆[M⊙]−8.85−0.20+0.19 and has an intrinsic scatter of 0.20−0.01+0.01. The cosmic evolution of the SFR density (ρSFR) is derived at three redshift bins: 0 <  z ≤ 0.15, 0.15 <  z ≤ 0.25, and 0.25 <  z ≤ 0.35, which agrees with previous results that were based on measurements of the Hα emission line. However, we find an offset with respect to other estimates that were based on the star formation history obtained from fitting the spectral energy distribution of the stellar continuum. We discuss the origin of this discrepancy, which is probably a combination of several factors: the escape of ionizing photons, the SFR tracers, and dust attenuation, among others.

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.