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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 ~1 deg2 in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift (z < 2.1), and quasars at high redshift (z ≥ 2.1). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN1) and colours for the other (ANN2). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS. In the mock test set, the f1-score for quasars at high redshift with the ANN1 (ANN2) are 0.99 (0.99), 0.93 (0.92), and 0.63 (0.57) for 17 < r ≤ 20, 20 < r ≤ 22.5, and 22.5 < r ≤ 23.6, respectively, where r is the J-PAS rSDSS band. In the case of low-redshift quasars, galaxies, and stars, we reached 0.97 (0.97), 0.82 (0.79), and 0.61 (0.58); 0.94 (0.94), 0.90 (0.89), and 0.81 (0.80); and 1.0 (1.0), 0.96 (0.94), and 0.70 (0.52) in the same r bins. In the SDSS DR12Q superset miniJPAS sample, the weighted f1-score reaches 0.87 (0.88) for objects that are mostly within 20 < r ≤ 22.5. We find that the most common confusion occurs between quasars at low redshift and galaxies in mocks and miniJPAS data. We discuss the origin of this confusion, and we show examples in which these objects present features that are shared by both classes. Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS. © The Authors 2023.

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.

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 candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

Context. In modern astronomy, machine learning has proved to be efficient and effective in mining big data from the newest telescopes. Aims. In this study, we construct a supervised machine-learning algorithm to classify the objects in the Javalambre Photometric Local Universe Survey first data release (J-PLUS DR1). Methods. The sample set is featured with 12-waveband photometry and labeled with spectrum-based catalogs, including Sloan Digital Sky Survey spectroscopic data, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and VERONCAT a the Veron Catalog of Quasars AGN. The performance of the classifier is presented with the applications of blind test validations based on RAdial Velocity Extension, the Kepler Input Catalog, the Two Micron All Sky Survey Redshift Survey, and the UV-bright Quasar Survey. A new algorithm was applied to constrain the potential extrapolation that could decrease the performance of the machine-learning classifier. Results. The accuracies of the classifier are 96.5% in the blind test and 97.0% in training cross-validation. The F1-scores for each class are presented to show the balance between the precision and the recall of the classifier. We also discuss different methods to constrain the potential extrapolation.

Aims. We analyze the white dwarf population in miniJPAS, the first square degree observed with 56 medium-band, 145 Å in width optical filters by the Javalambre Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a data-based forecast for the white dwarf science with low-resolution (R ∼ 50) photo-spectra. Methods. We define the sample of the bluest point-like sources in miniJPAS with r < 21.5 mag, a point-like probability larger than 0.5, (u − r)< 0.80 mag, and (g − i)< 0.25 mag. This sample comprises 33 sources with spectroscopic information: 11 white dwarfs and 22 quasi-stellar objects (QSOs). We estimate the effective temperature (Teff), the surface gravity, and the composition of the white dwarf population by a Bayesian fitting to the observed photo-spectra. Results. The miniJPAS data are sensitive to the Balmer series and the presence of polluting metals. Our results, combined with those from the Javalambre Photometric Local Universe Survey (J-PLUS) which has a lower spectral resolution but has already observed thousands of white dwarfs, suggest that J-PAS photometry would permit – down to r ∼ 21.5 mag and at least for sources with 7000 < Teff < 22 000 K – both the classification of the observed white dwarfs into H-dominated and He-dominated with 99% confidence and the detection of calcium absorption for equivalent widths larger than 15 Å. The effective temperature is estimated with a 2% uncertainty, which is close to the 1% from spectroscopy. A precise estimation of the surface gravity depends on the available parallax information. In addition, the white dwarf population at Teff > 7000 K can be segregated from the bluest extragalactic QSOs, providing a clean sample based on optical photometry alone. Conclusions. The J-PAS low-resolution photo-spectra would produce precise effective temperatures and atmospheric compositions for white dwarfs, complementing the data from Gaia. J-PAS will also detect and characterize new white dwarfs b

The miniJPAS survey has observed ∼1 deg2 of the AEGIS field with 60 bands (spectral resolution of R ∼ 60) in order to demonstrate the scientific potential of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS), which will map ∼8000 deg2 of the northern sky over the coming years. In particular, this paper demonstrates the potential of J-PAS in detecting groups with mass of up to 1013 M⊙ and in characterising their galaxy populations up to z ∼ 1. The parametric code BaySeAGal is used to derive the stellar population properties by fitting the J-PAS spectral energy distribution (SED) of the galaxy members in 80 groups at z ≤ 0.8 previously detected by the AMICO code, and of a galaxy field sample retrieved from the whole miniJPAS down to r < 22.75 (AB). Blue, red, quiescent, and transition (blue quiescent or green valley) galaxy populations are identified through their rest-frame (extinction-corrected) (u − r)int colour, galaxy stellar mass (M⋆), and specific star formation rate (sSFR). We measure the abundance of these galaxies as a function of M⋆ and environment in order to investigate the role that groups play in quenching star formation. Our findings are as follows. (i) The fraction of red and quiescent galaxies in groups increases with M⋆ and is always higher in groups (28% on average) than in the field (5%). (ii) The quenched fraction excess (QFE) in groups shows a strong dependence on M⋆, and increases from a few percent for galaxies with M⋆ < 1010 M⊙ to higher than 60% for galaxies with M⋆ > 3 × 1011 M⊙. (iii) The abundance excess of transition galaxies in groups shows a modest dependence on M⋆, being 5%–10% for galaxies with M⋆ < 1011 M⊙. (iv) The fading timescale, defined as the time that galaxies in groups spend in the transition phase, is very short (< 1.5 Gyr), indicating that the star formation of galaxies in groups declines very rapidly. (v) The evolution of the galaxy quenching rate in groups shows a modest but significant

Context. Precise measurements of black hole masses are essential to understanding the coevolution of these sources and their host galaxies. Aims. We develop a novel approach for computing black hole virial masses using measurements of continuum luminosities and emission line widths from partially overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. Methods. This novel method relies on forward-modelling quasar observations for estimating emission line widths, which enables unbiased measurements even for lines coarsely resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by 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 J-PAS narrow-band filters. Results. We find remarkable agreement between black hole masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from log(MBH)≃8 to 9.75, respectively. Reverberation mapping studies show that single-epoch masses present approximately 0.4 dex precision, letting us conclude that our novel technique delivers black hole masses with only mildly lower precision than single-epoch spectroscopy. Conclusions. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations that do not satisfy the preselection criteria of previous spectroscopic surveys. © ESO 2022.

Extragalactic globular clusters (GCs) are key objects for studying the formation and evolution of galaxies. The arrival of wide-field surveys such as the Javalambre Photometric Local Universe Survey (J-PLUS) offers new possibilities for the study of GCs. Nevertheless, GCs are not detected a priori by the data reduction pipeline of J-PLUS and, due to its pixel scale, the standard techniques of GCs detection are challenged. To fill this gap, we develop a semi-automatic pipeline to detect GCs in J-PLUS that can also be adapted to similar surveys. As a case study, we use data from the S0 galaxy NGC 1023 and we also study the stellar population content of GC candidates in the galaxy. To detect GCs, our methodology is based on Source Extractor and does not require a previous filtering or modelling of the host galaxy. We study colors and perform spectral energy distribution (SED) analysis on our final GC candidate catalog to obtain stellar population parameters. In NGC 1023, GCFinder identifies 523 GC candidates. We observe evidence of color bimodality in a few broad-band colors but not on narrow-band colors. The SED analysis reveals a clear metallicity bimodality and we observe that narrow-band filters are very useful to constrain metallicities. We also identified a broad age-metallicity relation as well as a wide metallicity distribution that are evidence that NGC 1023 experienced accretion events in the past. It is the first time this kind of study is performed with J-PLUS data. By detecting GC candidates in wide-field images without modeling the light of the galaxy, GCFinder becomes considerably faster, at a marginal loss of centrally-located GC candidates of about 7 percent. As GCFinder is entirely based on Source Extractor, it could be easily incorporated into automated software handling wide-field surveys., Comment: 21 pages, 19 figures, submitted to A&A

Understanding the origins of small-scale flats of CCDs and their wavelength-dependent variations plays an important role in high-precision photometric, astrometric, and shape measurements of astronomical objects. Based on the unique flat data of 47 narrowband filters provided by JPAS-Pathfinder, we analyze the variations of small-scale flats as a function of wavelength. We find moderate variations (from about 1.0% at 390 nm to 0.3% at 890 nm) of small-scale flats among different filters, increasing toward shorter wavelengths. Small-scale flats of two filters close in central wavelengths are strongly correlated. We then use a simple physical model to reproduce the observed variations to a precision of about ±0.14% by considering the variations of charge collection efficiencies, effective areas, and thicknesses between CCD pixels. We find that the wavelength-dependent variations of the small-scale flats of the JPAS-Pathfinder camera originate from inhomogeneities of the quantum efficiency (particularly charge collection efficiency), as well as the effective area and thickness of CCD pixels. The former dominates the variations in short wavelengths, while the latter two dominate at longer wavelengths. The effects on proper flat-fielding, as well as on photometric/flux calibrations for photometric/slitless spectroscopic surveys, are discussed, particularly in blue filters/wavelengths. We also find that different model parameters are sensitive to flats of different wavelengths, depending on the relations between the electron absorption depth, photon absorption length, and CCD thickness. In order to model the wavelength-dependent variations of small-scale flats, a small number (around 10) of small-scale flats with well-selected wavelengths are sufficient to reconstruct small-scale flats in other wavelengths. © 2021. The American Astronomical Society. All rights reserved.

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 and efficient machine-learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about similar to 1 deg(2) of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. The miniJPAS primary catalog contains approximately 64 000 objects in the r detection band (mag(AB)less than or similar to 24), with forced-photometry in all other filters.Aims. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g., stars) objects, which is a step required for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools that are based on explicit modeling. In particular, our goal is to release a value-added catalog with our best classification.Methods. In order to train and test our classifiers, we cross-matched the miniJPAS dataset with SDSS and HSC-SSP data, whose classification is trustworthy within the intervals 15 <= r <= 20 and 18.5 <= r <= 23.5, respectively. We trained and tested six different ML algorithms on the two cross-matched catalogs: K-nearest neighbors, decision trees, random forest (RF), artificial neural networks, extremely randomized trees (ERT), and an ensemble classifier. This last is a hybrid algorithm that combines artificial neural networks and RF with the J-PAS stellar and galactic loci classifier. As input for the ML algorithms we used the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also used the mean point spread function in the r detection band for each pointing.Results. We find that the RF and ERT algorithms perform best in all scenarios. When the full magnitude range of 15 <= r <= 23.5 is analyzed, we find an area under the curve AUC=0.957 with RF when photometric information alone is used, and AUC

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 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 ) 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 (5_ in a 300 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 qu

Aims. Our goal is to estimate the star formation main sequence (SFMS) and the star formation rate density (SFRD) at z = 0.017 (d . 75 Mpc) using the Javalambre Photometric Local Universe Survey (J-PLUS) first data release, that probes 897.4 deg2 with twelve optical bands. Methods. We extract the Ha emission flux of 805 local galaxies from the J-PLUS filter J0660, being the continuum level estimated with the other eleven J-PLUS bands, and the dust attenuation and nitrogen contamination corrected with empirical relations. Stellar masses (M), Ha luminosities (LHa), and star formation rates (SFRs) were estimated by accounting for parameters covariances. Our sample comprises 689 blue galaxies and 67 red galaxies, classified in the (u - g) vs. (g - z) color–color diagram, plus 49 AGN. Results. The SFMS is explored at log M 8 and it is clearly defined by the blue galaxies, with the red galaxies located below them. The SFMS is described as log SFR = 0.83 log M -8.44. We find a good agreement with previous estimations of the SFMS, especially those based on integral field spectroscopy. The Ha luminosity function of the AGN-free sample is well described by a Schechter function with log L * Ha = 41.34, log f * = -2.43, and a = -1.25. Our measurements provide a lower characteristic luminosity than several previous studies in the literature. Conclusions. The derived star formation rate density at d . 75 Mpc is log SFR = -2.10 ± 0.11, with red galaxies accounting for 15% of the SFRD. Our value is lower than previous estimations at similar redshift, and provides a local reference for evolutionary studies regarding the star formation history of the Universe © 2021 EDP Sciences. All rights reserved.

Cataclysmic Variables (CVs) are binaries in which a white dwarf accretes from a low-mass companion star. CVs are the best-suited laboratories to test our understanding of the evolution of compact, interacting binaries as they are numerous, relatively bright, and both stellar components are structurally simple. Nonetheless, while a large fraction ( ) of the present-day Galactic CV population is expected to be old, highly evolved and to host brown dwarf companions, yet very few of these so-called “period bouncers” have been identified so far. The lack of these systems in the observed Galactic CV population possibly suggests that the physical mechanisms driving CV evolution (such as the common envelope phase, the mechanisms of angular momentum loss and/or the response of the companions to the mass loss) are still not completely understood. The Compact binary HIgh CAdence Survey (CHiCaS) is a high cadence photometric survey performing three hours of uninterrupted time series photometry at one minute cadence over 136 square degrees of sky with JAST/T80Cam. CHiCaS aims to unambiguously identify the predicted large population of period-bounce CVs via detection of binary eclipses, thus providing an observational support for the current evolutionary models of all kind of compact binaries, such as black hole binaries, X-ray transients, double degenerates or SN Ia progenitors. Moreover, CHiCaS will deliver high cadence light curves along with full-colour information for about 2.5 million sources, thus identifying several hundred thousand variable stars, including eclipsing and contact binaries, pulsating and flaring stars, which will provide a significant legacy value., Peer Reviewed, Postprint (published version)

The next generation of galaxy surveys will allow us to test one of the most fundamental assumptions of the standard cosmology, i.e. that gravity is governed by the general theory of relativity (GR). In this paper, we investigate the ability of the Javalambre Physics of the AcceleratingUniverseAstrophysical Survey (J-PAS) to constrainGR and its extensions. Based on the J-PAS information on clustering and gravitational lensing, we perform a Fisher matrix forecast on the effective Newton constant, mu, and the gravitational slip parameter, eta, whose deviations from unity would indicate a breakdown of GR. Similar analysis is also performed for the DESI and Euclid surveys and compared to J-PAS with two configurations providing different areas, namely an initial expectation with 4000 deg(2) and the future best case scenario with 8500 deg(2). We show that J-PAS will be able to measure the parameters mu and eta at a sensitivity of 2-7 per cent, and will provide the best constraints in the interval z = 0.3-0.6, thanks to the large number of ELGs detectable in that redshift range. We also discuss the constraining power of J-PAS for dark energy models with a time-dependent equation-of-state parameter of the type w(a) = w(0) + w(a)(1 - a), obtaining Delta w(0) = 0.058 and Delta w(a) = 0.24 for the absolute errors of the dark energy parameters.© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Context. From the approximately 3500 planetary nebulae (PNe) discovered in our Galaxy, only 14 are known to be members of the Galactic halo. Nevertheless, a systematic search for halo PNe has never been performed. Aims. In this study, we present new photometric diagnostic tools to identify compact PNe in the Galactic halo by making use of the novel 12-filter system projects, Javalambre Photometric Local Universe Survey (J-PLUS) and Southern-Photometric Local Universe Survey (S-PLUS). Methods. We reconstructed the Isaac Newton Telescope Photometric H alpha Survey of the Northern Galactic Plane diagnostic diagram and propose four new ones using (i) the J-PLUS and S-PLUS synthetic photometry for a grid of photo-ionisation models of halo PNe, (ii) several observed halo PNe, as well as (iii) a number of other emission-line objects that resemble PNe. All colour-colour diagnostic diagrams are validated using two known halo PNe observed by J-PLUS during the scientific verification phase and the first data release (DR1) of S-PLUS and the DR1 of J-PLUS. Results. By applying our criteria to the DR1s (similar to 1190 deg(2)), we identified one PN candidate. However, optical follow-up spectroscopy proved it to be a HII region belonging to the UGC 5272 galaxy. Here, we also discuss the PN and two HII galaxies recovered by these selection criteria. Finally, the cross-matching with the most updated PNe catalogue (HASH) helped us to highlight the potential of these surveys, since we recover all the known PNe in the observed area. Conclusions. The tools here proposed to identify PNe and separate them from their emission-line contaminants proved to be very efficient thanks to the combination of many colours, even when applied - like in the present work - to an automatic photometric search that is limited to compact PNe. © ESO 2020