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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.

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., The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is a photometric survey that is poised to scan several thousands of square degrees of the sky. It will use 54 narrow-band filters, combining the benefits of low-resolution spectra and photometry. Its offshoot, miniJPAS, is a 1 deg2 survey that uses J-PAS filter system with the Pathfinder camera. In this work, we study mJPC2470-1771, the most massive cluster detected in miniJPAS. We survey the stellar population properties of the members, their star formation rates (SFR), star formation histories (SFH), the emission line galaxy (ELG) population, spatial distribution of these properties, and the ensuing effects of the environment. This work shows the power of J-PAS to study the role of environment in galaxy evolution. We used a spectral energy distribution (SED) fitting code to derive the stellar population properties of the galaxy members: stellar mass, extinction, metallicity, (u − r)res and (u − r)int colours, mass-weighted age, the SFH that is parametrised by a delayed-τ model (τ, t0), and SFRs. We used artificial neural networks for the identification of the ELG population via the detection of the Hα, [NII], Hβ, and [OIII] nebular emission. We used the Ew(Hα)-[NII] (WHAN) and [OIII]/Hα-[NII]/Hα (BPT) diagrams to separate them into individual star-forming galaxies and AGNs. We find that the fraction of red galaxies increases with the cluster-centric radius; and at 0.5R200 the red and blue fractions are both equal. The redder, more metallic, and more massive galaxies tend to be inside the central part of the cluster, whereas blue, less metallic, and less massive galaxies are mainly located outside of the inner 0.5R200. We selected 49 ELG, with 65.3% of them likely to be star-forming galaxies, dominated by blue galaxies, and 24% likely to have an AGN (Seyfert or LINER galaxies). The rest are difficult to classify and are most likely composite galaxies. These latter galaxies are red, and their abundance decreases with the cluster-centric radius; in contrast, the fraction of star-forming galaxies increases outwards up to R200. Our results are compatible with an scenario in which galaxy members were formed roughly at the same epoch, but blue galaxies have had more recent star formation episodes, and they are quenching out from within the cluster centre. The spatial distribution of red galaxies and their properties suggest that they were quenched prior to the cluster accretion or an earlier cluster accretion epoch. AGN feedback or mass might also stand as an obstacle in the quenching of these galaxies. © J. E. Rodríguez-Martín et al. 2022., R.G.D., L.A.D.G., R.G.B., G.M.S., J.R.M., 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. 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 Economia, 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 Desenvolvimento 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 Skłodowska-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 Inversiones 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). Partially based on observations obtained at the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). R.A.D. acknowledges support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq through BP grant 308105/2018-4. Funding for the J-PAS Project has been provided by the Governments of Spain and Aragón through he Fondo de Inversión de Teruel, European FEDER funding and the Spanish Ministry of Science, Innovation and Universities, and 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 J-PAS Chinese Astronomical Consortium.

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. © ESO 2022., G.M.S., R.G.D., R.G.B., L.A.D.G., 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 Andalucí a (SEV-2017-0709), and to the AYA2016-77846-P and PID2019-109067-GB100. S.D.P. is grateful to the Fonds de Recherche du Québec – Nature et Technologies. 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.V.M. and S.D.P. acknowledge financial support from the Spanish Ministerio de Economía y Competitividad under grants AYA2016-79724-C4-4-P and PID2019-107408GB-C44, from Junta de Andalucía Excellence Project P18-FR-2664, and the funding of the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). A.E. and 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. L.S.J. acknowledges support from CNPq (304819/2017-4) and FAPESP (2019/10923-5). R.A.D. acknowledges partial support support from CNPq grant 308105/2018-4. 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 Skłodowska-Curie grant agreement No 888258. 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 Inversiones 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; European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685); 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 J-PAS Chinese Astronomical Consortium.

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.