The GALANTE Photometric System

This paper describes the characterization of the GALANTE photometric system, a seven intermediate- and narrow-band filter system with a wavelength coverage from 3000 $\r{A}$ to 9000 $\r{A}$ . We describe the photometric system presenting the full sensitivity curve as a product of the filter sensitivity, CCD, telescope mirror, and atmospheric transmission curves, as well as some first- and second-order moments of this sensitivity function. The GALANTE photometric system is composed of four filters from the J-PLUS photometric system, a twelve broad-to-narrow filter system, and three exclusive filters, specifically designed to measure the physical parameters of stars such as effective temperature $T_{\rm eff}$, $\log(g)$, metallicity, colour excess $E(4405-5495)$, and extinction type $R_{5495}$. Two libraries, the Next Generation Spectral Library (NGSL) and the one presented in Ma\'iz Apell\'aniz & Weiler (2018), have been used to determine the transformation equations between the Sloan Digital Sky Survey ($\textit{SDSS}$) $\textit{ugriz}$ photometry and the GALANTE photometric system. We will use this transformation to calibrate the zero points of GALANTE images. To this end, a preliminary photometric calibration of GALANTE has been made based on two different $\textit{griz}$ libraries ($\textit{SDSS}$ DR12 and ATLAS All-Sky Stellar Reference Catalog, hereinafter $\textit{RefCat2}$). A comparison between both zero points is performed leading us to the choice of $\textit{RefCat2}$ as the base catalogue for this calibration, and applied to a field in the Cyg OB2 association.Comment: Accepted in MNRA

The GALANTE photometric survey of the northern Galactic plane: Project description and pipeline

The GALANTE optical photometric survey is observing the northern Galactic plane and some adjacent regions using seven narrow- and intermediate-filters, covering a total of 1618 square degrees. The survey has been designed with multiple exposure times and at least two different air masses per field to maximize its photometric dynamic range, comparable to that of Gaia, and ensure the accuracy of its photometric calibration. The goal is to reach at least 1% accuracy and precision in the seven bands for all stars brighter than AB magnitude 17 while detecting fainter stars with lower values of the signal-to-noise ratio.The main purposes of GALANTE are the identification and study of extinguished O+B+WR stars, the derivation of their extinction characteristics, and the cataloguing of F and G stars in the solar neighbourhood. Its data will be also used for a variety of other stellar studies and to generate a high-resolution continuum-free map of the H{\alpha} emission in the Galactic plane. We describe the techniques and the pipeline that are being used to process the data, including the basis of an innovative calibration system based on Gaia DR2 and 2MASS photometry.Comment: 18 pages, accepted for publication in MNRA

J-PLUS: analysis of the intracluster light in the Coma cluster

The intracluster light (ICL) is a luminous component of galaxy clusters composed of stars that are gravitationally bound to the cluster potential but do not belong to the individual galaxies. Previous studies of the ICL have shown that its formation and evolution are intimately linked to the evolutionary stage of the cluster. Thus, the analysis of the ICL in the Coma cluster will give insights into the main processes driving the dynamics in this highly complex system. Using a recently developed technique, we measure the ICL fraction in Coma at several wavelengths, using the J-PLUS unique filter system. The combination of narrow- and broadband filters provides valuable information on the dynamical state of the cluster, the ICL stellar types, and the morphology of the diffuse light. We use the Chebyshev-Fourier Intracluster Light Estimator (CICLE) to disentangle the ICL from the light of the galaxies, and to robustly measure the ICL fraction in seven J-PLUS filters. We obtain the ICL fraction distribution of the Coma cluster at different optical wavelengths, which varies from $\sim 7\%-21\%$, showing the highest values in the narrowband filters J0395, J0410, and J0430. This ICL fraction excess is distinctive pattern recently observed in dynamically active clusters (mergers), indicating a higher amount of bluer stars in the ICL compared to the cluster galaxies. Both the high ICL fractions and the excess in the bluer filters are indicative of a merging state. The presence of younger/lower-metallicity stars the ICL suggests that the main mechanism of ICL formation for the Coma cluster is the stripping of the stars in the outskirts of infalling galaxies and, possibly, the disruption of dwarf galaxies during past/ongoing mergers.Comment: 10 pages, 3 figures, 1 table. Accepted for publication in A&

Testing the models of CV evolution

The study of Cataclysmic Variables (CVs) is crucial to test our understanding of binary evolution and its application to many astrophysical phenomena, such as short gamma-ray bursts, X-ray transients and, more important, Supernovae Ia, our yardsticks for measuring distances. Yet, the predicted major component of the present-day CV population, the so-called "period bouncers" (CVs containing a white dwarf and a degenerate donor), has not been detected, highlighting a major discrepancy between theory and observations. We present here CHiCaS, the Compact binary HIgh CAdence Survey, which will perform three hours of uninterrupted time series photometry over 136 square degrees of the sky with JAST/T80Cam. By the end of next year, this program will deliver one minute cadence lightcurves for ¿2.5 million objects as faint as g¿21.5, along with full colour information. Via detection of their eclipses, CHiCaS will finally, and unambiguously identify the predicted large population of period bouncers. The identification of the missing population will provide an observational support for the current models for the mechanisms of angular momentum loss in compact binaries, which also describe the evolution of all kind of binaries. CHiCaS will also offer a complete and unbiased view into the short term variability of thousands of binaries, eclipsing systems, pulsating stars and CVs in the period gap, which will allow to improve our knowledge of these objects and to carry out additional tests on CV evolution.Peer ReviewedPostprint (published version

J-PAS: forecasts on interacting vacuum energy models

The next generation of galaxy surveys will allow us to test some fundamental aspects of the standard cosmological model, including the assumption of a minimal coupling between the components of the dark sector. In this paper, we present the Javalambre Physics of the Accelerated Universe Astrophysical Survey (J-PAS) forecasts on a class of unified models where cold dark matter interacts with a vacuum energy, considering future observations of baryon acoustic oscillations, redshift-space distortions, and the matter power spectrum. After providing a general framework to study the background and linear perturbations, we focus on a concrete interacting model without momentum exchange by taking into account the contribution of baryons. We compare the J-PAS results with those expected for DESI and Euclid surveys and show that J-PAS is competitive to them, especially at low redshifts. Indeed, the predicted errors for the interaction parameter, which measures the departure from a $\Lambda$CDM model, can be comparable to the actual errors derived from the current data of cosmic microwave background temperature anisotropies.Comment: 34 pages, 9 figures, 14 table

J-PLUS: Detecting and studying extragalactic globular clusters -- the case of NGC 1023

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&

J-PLUS DR3: Galaxy-Star-Quasar classification

The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources (29.8 million with $r \le 21$) on 3192 deg$^2$ (2881 deg$^2$ after masking). J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the dataset, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called {\tt TPOT} to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR12, LAMOST DR7 and \textit{Gaia} DR3. We checked that the training set of about 570 thousand galaxies, one million stars and 220 thousand quasars is both representative and pure to a good degree. We considered 37 features: besides the twelve photometric bands with their errors, six colors, four morphological parameters, galactic extinction with its error and the PSF relative to the corresponding pointing. After exploring numerous pipeline possibilities through the TPOT genetic algorithm, we found that XGBoost provides the best performance: the AUC for galaxies, stars and quasars is above 0.99 and the average precision is above 0.99 for galaxies and stars and 0.94 for quasars. XGBoost outperforms the star-galaxy classifiers already provided in J-PLUS DR3 and also efficiently classifies quasars. We also found that photometry was very important in the classification of quasars, showing the relevance of narrow-band photometry.Comment: 14 pages, 17 figure

J-NEP: 60-band photometry and photometric redshifts for the James Webb Space Telescope North Ecliptic Pole Time-Domain Field

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 demonstrate the scientific potential of J-PAS, two small surveys were performed with the single-CCD Pathfinder camera: miniJPAS (~1 deg2 along the Extended Groth Strip), and J-NEP (~0.3 deg2 around the JWST North Ecliptic Pole Time Domain Field), including all 56 J-PAS filters as well as u, g, r, and i. J-NEP is ~0.5-1.0 magnitudes deeper than miniJPAS, providing photometry for 24,618 r-band detected sources and photometric redshifts (photo-z) for the 6,662 sources with r<23. In this paper we describe the photometry and photo-z of J-NEP and demonstrate a new method for the removal of systematic offsets in the photometry based on the median colours of galaxies, dubbed "galaxy locus recalibration". This method does not require spectroscopic observations except in a few reference pointings and, unlike previous methods, is applicable to the whole J-PAS survey. We use a spectroscopic sample of 787 galaxies to test the photo-z performance for J-NEP and in comparison to miniJPAS. We find that the deeper J-NEP observations result in a factor ~1.5-2 decrease in sigma_NMAD (a robust estimate of the standard deviation of the photo-z error) and the outlier rate relative to miniJPAS for r>21.5 sources, but no improvement in brighter ones. We find the same relation between sigma_NMAD and odds in J-NEP and miniJPAS, suggesting sigma_NMAD can be predicted for any set of J-PAS sources from their odds distribution alone, with no need for additional spectroscopy to calibrate the relation. We explore the causes for photo-z outliers and find that colour-space degeneracy at low S/N, photometry artifacts, source blending, and exotic spectra are the most important factors.Comment: 16 pages, 25 figures, accepted for publication in Astronomy and Astrophysic

The miniJPAS survey: Identification and characterization of the emission line galaxies down to z<0.35z < 0.35 in the AEGIS field

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$^2$, 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 $0.35$. Using a method based on artificial neural networks, we detect the ELG population and measure the equivalent width and flux of the $H\alpha$, $H\beta$, [OIII], and [NII] emission lines. We explore the ionization mechanism using the diagrams [OIII]/H$\beta$ versus [NII]/H$\alpha$ (BPT) and EW(H$\alpha$) versus [NII]/H$\alpha$ (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 $72.8 \pm 0.4$%, $17.7 \pm 0.4$% , and $9.4 \pm 0.2$% are star-forming (SF), active galactic nucleus (Seyfert), and quiescent galaxies, respectively. Based on the flux of $H\alpha$ we find that the star formation main sequence is described as $\log$ SFR $[M_\mathrm{\odot} \mathrm{yr}^{-1}] = 0.90^{+ 0.02}_{-0.02} \log M_{\star} [M_\mathrm{\odot}] -8.85^{+ 0.19}_{-0.20}$ and has an intrinsic scatter of $0.20^{+ 0.01}_{-0.01}$. The cosmic evolution of the SFR density ($\rho_{\text{SFR}}$) is derived at three redshift bins: $0 < z \leq 0.15$, $0.15 < z \leq 0.25$, and $0.25 < z \leq 0.35$, which agrees with previous results that were based on measurements of the $H\alpha$ emission line.Comment: 22 pages, 19 figure

The miniJPAS survey:star-galaxy classification using machine learning

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 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases