Suchergebnisse

Aims. We study a sample of Hβ emission line sources at z ∼ 0.9 to identify the star-forming galaxies sample and characterise them in terms of line luminosity, stellar mass, star formation rate, and morphology. The final aim is to obtain the Hβ luminosity function of the star-forming galaxies at this redshift. Methods. We used the red tunable filter of the instrument Optical System for Imaging low Resolution Integrated Spectroscopy (OSIRIS) at Gran Telescopio de Canarias to obtain the pseudo spectra of emission line sources in the OTELO field. From these pseudo spectra, we identified the objects with Hβ emission. As the resolution of the pseudo spectra allowed us to separate Hβ from [Oâ » III], we were able to derive the Hβ flux without contamination from its adjacent line. Using data from the extended OTELO catalogue, we discriminated AGNs and studied the star formation rate, the stellar mass, and the morphology of the star-forming galaxies. Results. We find that our sample is located on the main sequence of star-forming galaxies. The sources are morphologically classified, mostly as disc-like galaxies (76%), and 90% of the sample are low-mass galaxies (M∗ < 1010â M⊠). The low-mass star-forming galaxies at z ∼ 0.9 that were detected by OTELO present similar properties as low-mass star-forming galaxies in the local universe, suggesting that these kinds of objects do not have a favorite epoch of formation and star formation enhancement from z ∼ 1 to now. Our sample of 40 Hβ star-forming galaxies include the faintest Hβ emitters detected so far. This allows us to constrain the faint end of the luminosity function for the Hβ line alone with a minimum luminosity of log L = 39â ergâ s-1, which is a hundred times fainter than previous surveys. The dust-corrected OSIRIS Tunable Emission Line Object survey (OTELO) Hβ luminosity function established the faint-end slope as α =-1.36 ± 0.15. We increased the scope of the analysis to the bright end by adding ancillary data from the literature, which was not dust-corrected in this case. The obtained slope for this extended luminosity function is α =-1.43 ± 0.12. © ESO 2021. ; This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the grants AYA2014 -58861 -C3 -1 -P, AYA2014 -58861 C3 -2 -P, AYA2014 -58861 -C3 -3 -P, AYA2013 -46724 -P, AYA2017 -88007 C3 -1 -P, AYA2017 -88007 -C3 -2, MDM-2017-0737 (Unidad de Excelencia Maria de Maeztu, CAB). APG acknowledges support from ESA through the Faculty of the European Space Astronomy Centre (ESAC) -Funding reference ESAC_549/2019. Based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias, in the island of La Palma. This study makes use of data from AEGIS, a multiwavelength sky survey conducted with the Chandra, GALEX, Hubble, Keck, CFHT, MMT, Subaru, Palomar, Spitzer, VLA, and other telescopes and supported in part by the NSF, NASA, and the STFC. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at Terapix available at the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. Based on observations obtained with WIRCam, a joint project of CFHT, Taiwan, Korea, Canada, France, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institute National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX, the WIRDS (WIRcam Deep Survey) consortium, and the Canadian Astronomy Data Centre. This research was supported by a grant from the Agence Nationale de la Recherche ANR-07-BLAN-0228. ; With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. ; Peer reviewed

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index. Methods. We used three classification methods for the OTELO database: (1) u? -? r color separation, (2) linear discriminant analysis using u? -? r and a shape parameter classification, and (3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data. © ESO 2020. ; This work was supported by the project Evolution of Galaxies, of reference AYA2014-58861-C3-1-P and AYA2017-88007-C3-1-P, within the "Programa estatal de fomento de la investigacion cientifica y tecnica de excelencia del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion (2013-2016)" of the "Agencia Estatal de Investigacion del Ministerio de Ciencia, Innovacion y Universidades", and co-financed by the FEDER "Fondo Europeo de Desarrollo Regional". JAD is grateful for the support from the UNAM-DGAPA-PASPA 2019 program, the UNAM-CIC, the Canary Islands CIE: Tricontinental Atlantic Campus 2017, and the kind hospitality of the IAC. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Innovation and Technology (MoIT), and from the Spanish Ministry of Economy and Competitiveness (MINECO) through projects AYA2013-42227-P and AYA2016-76682C3-1-P. APG, MSP and RPM were supported by the PNAYA project: AYA2017-88007-C3-2-P. MC and APG are also funded by Spanish State Research Agency grant MDM-2017-0737 (Unidad de Excelencia Maria de Maeztu CAB). EJA acknowledges support from the Spanish Government Ministerio de Ciencia, Innovacion y Universidades though grant PGC2018-095049-B-C21. M.P. and E.J.A. also acknowledge support from the State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709). JG receives support through the project AyA2018-RTI-096188-B-100. MALL acknowledges support from the Carlsberg Foundation via a Semper Ardens grant (CF15-0384). JIGS receives support through the Proyecto Puente 52.JU25.64661 (2018) funded by Sodercan S.A. and the Universidad de Cantabria, and PGC2018-099705-B-100 funded by the Ministerio de Ciencia, Innovacion y Universidades. Based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias, in the island of La Palma. This work is (partly) based on data obtained with the instrument OSIRIS, built by a Consortium led by the Instituto de Astrofisica de Canarias in collaboration with the Instituto de Astronomia of the Universidad Autonoma de Mexico. OSIRIS was funded by GRANTECAN and the National Plan of Astronomy and Astrophysics of the Spanish Government.