The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Full list of authors: Rodrigues, Natalia V. N.; Raul Abramo, L.; Queiroz, Carolina; Martinez-Solaeche, Gines; Perez-Rafols, Ignasi; Bonoli, Silvia; Chaves-Montero, Jonas; Pieri, Matthew M.; Gonzalez Delgado, Rosa M.; Morrison, Sean S.; Marra, Valerio; Marquez, Isabel; Hernan-Caballero, A.; Diaz-Garcia, L. A.; Benitez, Narciso; Cenarro, A. Javier; Dupke, Renato A.; Ederoclite, Alessandro; Lopez-Sanjuan, Carlos; Marin-Franch, Antonio; de Oliveira, Claudia Mendes; Moles, Mariano; Sodre, Laerte, Jr.; Varela, Jesus; Ramio, Hector Vazquez; Taylor, Keith.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. © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.This paper has gone through internal review by the J-PAS collaboration. NR acknowledges financial support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – Finance Code 001. RA was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). CQ acknowledges financial support from FAPESP (grants 2015/11442-0 and 2019/06766-1) and CAPES – Finance Code 001. IPR was supported by funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowskja-Curie grant agreement number 754510. MPP and SSM were supported by the Programme National de Cosmologie et Galaxies (PNCG) of CNRS/INSU with INP and IN2P3, co-funded by CEA and CNES, the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the ‘Investissements d’Avenir’ French Government program, managed by the French National Research Agency (ANR), and by ANR under contract ANR-14-ACHN-0021. GMS, RMGD, and LADG acknowledge support from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and the project PID2019-109067-GB100. JCM and SB acknowledge financial support from Spanish Ministry of Science, Innovation, and Universities through the project PGC2018-097585-B-C22. AFS acknowledges support from the Spanish Ministerio de Ciencia e Innovación through project PID2019-109592GB-I00 and the Generalitat Valenciana project PROMETEO/2020/085. RAD acknowledges partial support from CNPq grant 308105/2018-4. AE acknowledges the financial support from the Spanish Ministry of Science and Innovation and the European Union – NextGenerationEU through the Recovery and Resilience Facility project ICTS-MRR-2021-03-CEFCA. LSJ acknowledges support from CNPq (304819/2017-4) and FAPESP (2019/10923-5). This study is based on observations made with the JST250 telescope and PathFinder camera for the miniJPAS project at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Física del Cosmos de Aragón (CEFCA). We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing and calibrating the OAJ data used in this work. Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel; the Aragonese Government through the Research Groups E96, E103, E16_17R, and E16_20R; the Spanish Ministry of Science, Innovation, and Universities (MCIU/AEI/FEDER, UE) with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867 and FCDD13-4E-2685). Funding for the J-PAS Project has also been provided by the Brazilian agencies FINEP, FAPESP, and FAPERJ and by the National Observatory of Brazil, with additional funding provided by the Tartu Observatory and by the J-PAS Chinese Astronomical Consortium. Funding for the SDSS-III/IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-III/IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001131-S).Peer reviewe

The miniJPAS survey: Identification and characterization of galaxy populations with the J-PAS photometric system

Full list of authors: González Delgado, R. M.; Díaz-García, L. A.; de Amorim, A.; Bruzual, G.; Cid Fernandes, R.; Pérez, E.; Bonoli, S.; Cenarro, A. J.; Coelho, P. R. T.; Cortesi, A.; García-Benito, R.; López Fernández, R.; Martínez-Solaeche, G.; Rodríguez-Martín, J. E.; Magris, G.; Mejía-Narvaez, A.; Brito-Silva, D.; Abramo, L. R.; Diego, J. M. ; Dupke, R. A.; Hernán-Caballero, A.; Hernández-Monteagudo, C.; López-Sanjuan, C.; Marín-Franch, A.; Marra, V.; Moles, M.; Montero-Dorta, A.; Queiroz, C.; Sodré, L.; Varela, J.; Vázquez Ramió, H.; Vílchez, J. M.; Baqui, P. O.; Benítez, N.; Cristóbal-Hornillos, D.; Ederoclite, A.; Mendes de Oliveira, C.; Civera, T.; Muniesa, D.; Taylor, K.; Tempel, E.; J-PAS Collaboration.The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will soon start imaging thousands of square degrees of the northern sky with its unique set of 56 filters (spectral resolution of R - 60). Before the arrival of the final instrument, we observed 1 deg2 on the AEGIS field with an interim camera with all the J-PAS filters. Taking advantage of these data, dubbed miniJPAS, we aim at proving the scientific potential of the J-PAS to derive the stellar population properties of galaxies via fitting codes for spectral energy distributions (SEDs), with the ultimate goal of performing galaxy evolution studies across cosmic time. One parametric (BaySeAGal) and three non-parametric (MUFFIT, AlStar, and TGASPEX) SED-fitting codes are used to constrain the stellar mass, age, metallicity, extinction, and rest-frame and dust-corrected (u-r) colours of a complete flux-limited sample (rSDSS - 22.5 AB) of miniJPAS galaxies that extends up to z = 1. We generally find consistent results on the galaxy properties derived from the different codes, independently of the galaxy spectral type or redshift; this is remarkable considering that 25% of the J-spectra have signal-to-noise ratios (S/N) -3. For galaxies with S=N - 10, we estimate that the J-PAS photometric system will allow us to derive the stellar population properties of rest-frame (u - r) colour, stellar mass, extinction, and mass-weighted age with a precision of 0:04 - 0:02 mag, 0:07 - 0:03 dex, 0:2 - 0:09 mag, and 0:16 - 0:07 dex, respectively. This precision is equivalent to that obtained with spectroscopic surveys of similar S/N. By using the dust-corrected (u - r) colour mass diagram, a powerful proxy for characterizing galaxy populations, we find: (i) that the fraction of red and blue galaxies evolves with cosmic time, with red galaxies being -38% and -18% of the whole population at z = 0:1 and z = 0:5, respectively, and (ii) consistent results between codes for the average intrinsic (u-r) colour, stellar mass, age, and stellar metallicity of blue and red galaxies and their evolution up to z = 1. At all redshifts, the more massive galaxies belong to the red sequence, and these galaxies are typically older and more metal-rich than their counterparts in the blue cloud. Our results confirm that with J-PAS data we will be able to analyse large samples of galaxies up to z - 1, with galaxy stellar masses above log(M?=M-) - 8:9, 9.5, and 9.9 at z = 0:3, 0.5, and 0.7, respectively. The star formation history of a complete sub-sample of galaxies selected at z - 0:1 with log(M=M-) > 8:3 constrains the cosmic evolution of the star formation rate density up to z - 3, in good agreement with results from cosmological surveys. © ESO 2021.Acknowledgements. R.G.D., L.A.D.G., R.G.B., G.M.S., J.R.M., and E.P. acknowledge financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709), and to the AYA2016-77846-P and PID2019-109067-GB100. L.A.D.G. also acknowledges financial support by the Ministry of Science and Technology of Taiwan (grant MOST 106-2628-M-001-003-MY3) and by the Academia Sinica (grant AS-IA-107-M01). G.B. acknowledges financial support from the National Autonomous University of México (UNAM) through grant DGAPA/PAPIIT IG100319 and from CONACyT through grant CB2015-252364. SB acknowledges PGC2018-097585-B-C22, MINECO/FEDER, UE of the Spanish Ministerio de Econo-mia, Industria y Competitividad. L.S.J. acknowledges support from Brazilian agencies FAPESP (2019/10923-5) and CNPq (304819/201794). P.O.B. acknowledges support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. P.R.T.C. acknowledges financial support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) process number 2018/05392-8 and Conselho Nacional de Desenvolvi-mento Científico e Tecnológico (CNPq) process number 310041/2018-0. V.M. thanks CNPq (Brazil) for partial financial support. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 888258. E.T. acknowledges support by ETAg grant PRG1006 and by EU through the ERDF CoE grant TK133. Based on observations made with the JST/T250 telescope and PathFinder camera for the miniJPAS project at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Física del Cosmos de Aragón (CEFCA). We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing and calibrating the OAJ data used in this work. Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain and Aragón through the Fondo de Inver-siones de Teruel; the Aragón Government through the Research Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685).Peer reviewe

The miniJPAS survey. Evolution of the luminosity and stellar mass functions of galaxies up to z∼0.7z \sim 0.7

We aim at developing a robust methodology for constraining the luminosity and stellar mass functions (LMFs) of galaxies by solely using data from multi-filter surveys and testing the potential of these techniques for determining the evolution of the miniJPAS LMFs up to $z\sim0.7$. Stellar mass and $B$-band luminosity for each of the miniJPAS galaxies are constrained using an updated version of the SED-fitting code MUFFIT, whose values are based on composite stellar population models and the probability distribution functions of the miniJPAS photometric redshifts. Galaxies are classified through the stellar mass versus rest-frame colour diagram corrected for extinction. Different stellar mass and luminosity completeness limits are set and parametrised as a function of redshift, for setting limits in our flux-limited sample ($r_\mathrm{SDSS}<22$). The miniJPAS LMFs are parametrised according to Schechter-like functions via a novel maximum likelihood method accounting for uncertainties, degeneracies, probabilities, completeness, and priors. Overall, our results point to a smooth evolution with redshift ($0.05<z<0.7$) of the miniJPAS LMFs in agreement with previous work. The LMF evolution of star-forming galaxies mainly involve the bright and massive ends of these functions, whereas the LMFs of quiescent galaxies also exhibit a non-negligible evolution on their faint and less massive ends. The cosmic evolution of the global $B$-band luminosity density decreases ~0.1 dex from $z=0.7$ to 0, whereas for quiescent galaxies this quantity roughly remains constant. In contrast, the stellar mass density increases ~0.3 dex at the same redshift range, where such evolution is mainly driven by quiescent galaxies owing to an overall increasing number of this kind of galaxies, which in turn includes the majority and most massive galaxies (60-100% fraction of galaxies at $\log_{10}(M_\star/M_\odot)>10.7$).Comment: 31 pages, 15 figures. Submitted to A&

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:Photometric redshift catalogue

MiniJPAS is a ∼1 deg2 imaging survey of the AEGIS field in 60 bands, performed to demonstrate the scientific potential of the upcoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS). Full coverage of the 3800-9100 Å range with 54 narrow-band filters, in combination with 6 optical broad-band filters, allows for extremely accurate photometric redshifts (photo-z), which, applied over areas of thousands of square degrees, will enable new applications of the photo-z technique, such as measurement of baryonic acoustic oscillations. In this paper we describe the method we used to obtain the photo-z that is included in the publicly available miniJPAS catalogue, and characterise the photo-z performance. We built photo-spectra with 100 Å resolution based on forced-aperture photometry corrected for point spread function. Systematic offsets in the photometry were corrected by applying magnitude shifts obtained through iterative fitting with stellar population synthesis models. We computed photo-z with a customised version of LEPHARE, using a set of templates that is optimised for the J-PAS filter-set. We analysed the accuracy of miniJPAS photo-z and their dependence on multiple quantities using a subsample of 5266 galaxies with spectroscopic redshifts from SDSS and DEEP, which we find to be representative of the whole r 0.03), regardless of the magnitude, redshift, or spectral type of the sources. We show that the two main summary statistics characterising the photo-z accuracy for a population of galaxies (σNMAD and η) can be predicted by the distribution of odds in this population, and we use this to estimate the statistics for the whole miniJPAS sample. At r 0.82 with η = 0.05, at the cost of decreasing the density of selected galaxies to n ∼5200 deg-2 (∼2600 of which have |Δz| <0.003). © ESO 2021

Identification and characterization of emission line objects in J-PAS using artificial neural network

Tesis del Departamento de Astronomía Extragaláctica. Instituto de Astrofísica de Andalucía; Universidad de Granada. Programa de Doctorado en Física y Ciencias del Espacio. Leída el 19 de octubre del 2022, a las 12:00 h en el Salón de Actos del IAA.[EN] In the years to come the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will map ∼ 8000 deg2 of the northern sky in 56 colours (J-spectrum), providing an unprecedented amount of images of astronomical objects. Before arrival of JPCam to the Observatorio Astrof´ısico de Javalambre (OAJ), the J-PAS collaboration observed 1 deg2 of the AEGIS field with the JPAS- Pathfinder camera, using the same photometric system of J-PAS. More than 60 000 objects were detected in what is known as the miniJPAS survey. The main goal of this thesis is to identify and characterize emission line objects with J-PAS. In particular, we study emission line galaxies (ELG) and the properties that can be derived from the analysis of both the emission lines and the stellar populations. Furthermore, we dedicate one chapter to the detection of quasars. Unlike others photometric surveys that use few narrow band filters, the unique characteristics of J-PAS allows us to study these objects in a continuous range of redsfhit. For instance, we will be able to detect the emission lines of Hα or [O ii] in galaxies from 0 to z ∼ 0.35, and z ∼ 1, respectively. Similarly, the Lyα emission line of quasars will be detected from redshift 2.1 up to redshift 4. Traditional methods that measure the equivalent width (EW) of an emission line are generally based on the photometry contrast. Although, this methods gives a very good first approximation, it is limited in many ways. Firstly, there are emission lines such as Hα and [N ii] which are very close to each other in the spectrum. Therefore, they both contribute to the total observed flux in the filter, making difficult to disentangle the individual contribution of each emission line. This is particularly relevant in order to estimate the [N ii]/Hα ratio and determine the main ionization mechanisms of galaxies. What is more, in at least half of the observed galaxies by J-PAS the emission lines will fall in the middle of two adjacent filters. Consequently, measuring the EW is no longer feasible with the photometry contrast approach. In this thesis we developed new techniques based on machine learning (ML) in order to overcome these limitations. Unlike traditional methods, ML algorithms are able to find patterns in the data without making any empirical or theoretical assumptions. Nevertheless, large data sets are needed to train them efficiently. For this purpose, we generated mock J-PAS data, which are based on a collection of spectra from CALIFA, MaNGA, and SDSS. In chapter 3 we trained artificial neural networks (ANN) in order to predict from the generated syntethic J-PAS colors the EW of Hα, Hβ, [O iii], and [N ii] emission lines. Direct measurements of these lines were available in the catalogues for each spectrum. We showed that the minimum S/N that we need in the photometry to measure a line with an EW of 10 Å in Hα, Hβ, [N ii], and [O iii] is 5, 1.5, 3.5, and, 10 respectively. Instead, methods based on the photometry contrast need for the same EW a S/N in the photometry of at least 15.5. With a training set composed of CALIFA and MaNGA galaxies, we reached a precision of 0.092 and 0.078 dex in the [N ii]/Hα and [O iii]/Hβ ratios. Nevertheless, we found that there ratios are more difficult to constrain in galaxies hosting an active galactic nuclei (AGN). We also trained an ANN to distinguish between strong and week emission line galaxies (ELG). We proved that the regime of low emission (∼ 3 Å) can be explored in J-PAS. This is because ML algorithms are able to find much more complex relations between features, so even though we do not have enough sensitivity in the J-spectrum to distinguish galaxies with very low emission lines, the algorithms are able to find other patterns in the data to make this possible. As a proof of concept we applied our techniques to a sample of galaxies observed by miniJPAS in the redshift range 0 < z < 0.35. This is done in chapter 4. We showed that we are able to make a selection of emission line galaxies (ELG), distinguish AGNs from star forming galaxies based on the [N ii]/Hα and [O iii]/Hβ ratios, estimate the star formation rate (SFR) in galaxies throughout the flux of Hα, recover the star formation main sequence of galaxies or constrain the evolution of the cosmic star formation density up to redshift 0.35. Furthermore, our results derived from the properties of the emission lines are in agreement with the products obtained through the analysis of the stellar populations. For instance, we showed that blue (red) galaxies in miniJPAS are composed of a younger (older) stellar population and present stronger (weaker) emission lines. Finally, in chapter 5 we addressed the problem of source classification in order to distinguish between low redshift quasars, high redshift quasars, galaxies, and stars. We found that the main source of confusion appears between low redshift quasars and galaxies. This is because the host galaxy of low redshift quasars is sometimes bright enough to contribute to the observed spectrum. Thus, these objects present mixing features and consequently they are more difficult to classify. We paid special attention to the reliability of the ‘probabilities’ yield by the algorithms, something that is very often neglected in the community. In particular we investigated the effect of data augmentation via hybridisation. This technique consists in mixing the spectra from galaxies, quasars, and stars so as to generate hybrid objects with mixing probabilities. Unfortunately, we do not observe a global improvement in the performance of the algorithms. As a matter of fact, we observed that the ANN becomes under-confidence in their prediction. We believe this is likely due to the intrinsic nature of astronomical observations where errors are attached to observations, thus ‘hybridisation’ turns out to be a natural outcome as the S/N of the sources decreases. Although, the methods and techniques developed in this thesis are limited in some aspects, this work lays the foundations on which to study better the properties of emission line objects in J-PAS. As as soon as J-PAS begins to observe the sky, our methods will be tested in large sample of galaxies, thus it will be possible to improve them even further.Peer reviewe

The miniJPAS survey quasar selection III. Classification with artificial neural networks and hybridisation

Full list of authors: Martinez-Solaeche, G.; Queiroz, C.; Delgado, R. M. Gonzalez; Rodrigues, N. V. N.; Garcia-Benito, R.; Perez-Rafols, I.; Abramo, L. Raul; Diaz-Garcia, L.; Pieri, M. M.; Chaves-Montero, J.; Hernan-Caballero, A.; Rodriguez-Martin, J. E.; Bonoli, S.; Morrison, S. S.; Marquez, I.; Vilchez, J. M.; Fernandez-Ontiveros, J. A.; Marra, V.; Alcaniz, J.; Benitez, N.; Cenarro, A. J.; Cristobal-Hornillos, D.; Dupke, R. A.; Ederoclite, A.; Lopez-Sanjuan, C.; Marin-Franch, A.; de Oliveira, C. Mendes; Moles, M.; Sodre, L.; Taylor, K.; Varela, J.; Ramio, H. Vazquez.--This is an Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.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.This paper has gone through internal review by the J-PAS collaboration. G.M.S., R.G.D., R.G.B., L.A.D.G., and J.R.M. acknowledge financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709), and to the AYA2016-77846-P and PID2019-109067-GB100. C.Q. acknowledges financial support from the Brazilian funding agencies FAPESP (grants 2015/11442-0 and 2019/06766-1) and Coordenação de Aperfeiçoamento de Pessoal de Nível Su- perior (Capes) – Finance Code 001. I.P.R. was suported by funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowskja-Curie grant agreement no. 754510. M.P.P. and S.S.M. were supported by the Programme National de Cosmologie et Galaxies (PNCG) of CNRS/INSU with INP and IN2P3, co-funded by CEA and CNES, the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the “Investissements d’Avenir” French Government program, managed by the French National Research Agency (ANR), and by ANR under contract ANR-14-ACHN-0021. N.R. acknowledges financial support from CAPES. R.A. was supported by CNPq and FAPESP. J.C.M. and S.B. acknowledge financial support from Spanish Ministry of Science, Innovation, and Universities through the project PGC2018-097585-B-C22. J.A.F.O. acknowledges the financial support from the Spanish Ministry of Science and Innovation and the European Union – NextGenerationEU through the Recovery and Resilience Facility project ICTS-MRR-2021-03-CEFCA. Based on observations made with the JST250 telescope and PathFinder camera for the miniJPAS project at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Física del Cosmos de Aragón (CEFCA). We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing and calibrating the OAJ data used in this work. Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel; the Aragonese Government through the Research Groups E96, E103, E16_17R, and E16_20R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). Funding for the J-PAS Project has also been provided by the Brazilian agencies FINEP, FAPESP, FAPERJ and by the National Observatory of Brazil with additional funding provided by the Tartu Observatory and by the J-PAS Chinese Astronomical Consortium.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001131-S).Peer reviewe

The miniJPAS survey: A preview of the Universe in 56 colors

Full list of authors: 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.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 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. © 2021 EDP Sciences. All rights reserved.Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain and Aragon through the Fondo de Inversiones de Teruel; the Aragon Government through the Research Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). This work has made used of CEFCA's Scientific High Performance Computing system which has been funded by the Governments of Spain and Aragon through the Fondo de Inversiones de Teruel, and the Spanish Ministry of Economy and Competitiveness (MINECO-FEDER, grant AYA2012-30789). Funding for the J-PAS project has been provided also by the Brazilian agencies FINEP, FAPESP, FAPERJ and by the National Observatory of Brazil. Additional funding was also provided by the Tartu Observatory and by the Chinese Consortium from the Academy of Sciences SB acknowledges partial support from the project PGC2018-097585-B-C22. R.A.D. acknowledges support from the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico -CNPq through BP grant 308105/2018-4, and the Financiadora de Estudos e Projetos -FINEP grants REF. 1217/13 -01.13.0279.00 and REF 0859/10 -01.10.0663.00 and also FAPERJ PRONEX grant E-26/110.566/2010 for hardware funding support for the J-PAS project through the National Observatory of Brazil and Centro Brasileiro de Pesquisas Fisicas. LRA acknowledges financial support from CNPq (306696/2018-5) and FAPESP (2015/17199-0). VMthanks CNPq (Brazil) and FAPES (Brazil) for partial financial support and the H2020 project No 888258. L.A.D.G. and K.U. acknowledge support from the Ministry of Science and Technology of Taiwan (grant MOST 106-2628-M-001-003-MY3) and from the Academia Sinica (grant AS-IA-107-M01). J.M.D. and D.H acknowledge the support of project PGC2018-101814-B-100. MQ thanks CNPq (Brazil) and FAPERJ (Brazil) for financial support. PC acknowledges financial support from FAPESP (2018/05392-8) and CNPq (310041/2018-0). AAC acknowledges support from FAPERJ (E26/203.186/2016), CNPq (304971/2016-2 and 401669/2016-5), and the Universidad de Alicante (contract UATALENTO1802). C.Q. acknowledges support from FAPESP (2015/11442-0 and 2019/067661). V.M.P. is supported by NOIRLab, which is managed by AURA under a cooperative agreement with the NSF. P.B acknowledges support from CAPES -Finance Code 001. IAA researchers acknowledge financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709). Authors acknowledge support from the Generalitat Valenciana project of excellence Prometeo/2020/085. RGD, GMS, JRM, RGB, EP acknowledge financial support from the project AyA2016-77846-P. TC is supported by the INFN INDARK PD51 and PRIN-MIUR 2015W7KAWC. MAR and ALM acknowledge support from the MINECO project FIS2016-78859P(AEI/FEDER, UE). ET, AT and JL acknowledge the support by ETAg grants IUT40-2 and by EU through the ERDF CoE grant TK133 and MOBTP86. CK, JMV, JIP acknowledge financial support from project AYA2016-79724C4-4P. PAAL thanks the support of CNPq (309398/2018-5). LC thanks CNPq for partial support. Y.J-T acknowledges financial support from the FAPERJ (E26/202.835/2016), and from the Horizon 2020 Marie Sklodowska-Curie grant agreement No 898633. DMD acknowledges financial support from the SFB 881 of the DFG and from the MINECO grant AYA2016-81065-C2-2. FP acknowledges support of the project PGC2018-101931-B-I00. JC acknowledges support of the project E AYA2017-88007-C3-1-P, and co-financed by the FEDER. JIGs acknowledges support of projects of reference AYA2017-88007-C3-3-P, and PGC2018-099705-B-I00 and co-financed by the FEDER. EMG and PV would like to acknowledge financial support from the project ESP2017-83921C2-1-R. GMS acknowleges financial support from a predoctoral contract, ref. PRE2018-085523 (MCIU/AEI/FSE, UE). S.C. is partially supported by CNPq. R.G.L. acknowledges CAPES (process 88881.162206/2017-01) and Alexander von Humboldt Foundation for the financial support. JSA acknowledges support from FAPERJ (E26/203.024/2017), CNP (310790/2014-0 and 400471/2014-0) and FINEP (1217/13 -01.13.0279.00 and Ref. 0859/10 -01.10.0663.00). RvM acknowledges support from CNPq. AFS, PAM, VJM and FJB acknowledge support from project AYA2016-81065-C2-2. PAM acknowledges support from the "Subprograma Atraccio de Talent -Contractes Postdoctorals de la Universitat de Valencia". ESC acknowledges support from CNPq (308539/20184) and FAPESP (2019/19687-2). CMdO acknowledges support from CNPq (grant 312333/2014-5) and FAPESP (grant 2009/54202-8). LSJ acknowledges support from CNPq (grant 304819/2017-4) and FAPESP (grant 2012/008004). JMC acknowledges support from CNPq (grant 310727/2016-2). C.H.-M. and N. Greisel also acknowledge the support of the European Union via the Career Integration Grant CIG-PCIG9-GA-2011-294183. JJBP and AMC would like to acknowledge the support from the grant PGC2018-094626-B-C21 and the Basque Government grant IT-979-16. AMC acknowledges the postdoctoral contract from the University of the Basque Country UPV/EHU "Especializacioon de personal investigador doctor" program. MLLD acknowledges CAPES -Finance Code 001; and CNPq (142294/2018-7). GB acknowledges financial support from the UNAM through grant DGAPA/PAPIIT IG100319, from CONACyT through grant CB2015-252364, and from FAPESP projects 2017/02375-2 and 2018/05392-8. M.J. Reboucas acknowledges the support of FAPERJ under a CNE E-26/202.864/2017 grant, and CNPq. Support by CNPq (305409/2016-6) and FAPERJ (E-26/202.841/2017) is acknowledged by DL. AB acknowledges a CNPq fellowship. C.A.G.acknowledges support from CAPES. EA acknowledges support from FAPESP (2011/18729-1). AC acknowledges support from PNPD/CAPES. ABA and FSK acknowledge the Severo Ochoa program SEV-2015-0548. FSK also thanks the AYA2017-89891-P and the RYC2015-18693 grants. DF acknowledges support from the Atraccion del Talento Cientifico en Salamanca programme and the project PGC2018-096038B-I00.Peer reviewe