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[Context] Extragalactic surveys are a key tool for better understanding the evolution of galaxies. Both deep and wide-field surveys serve to provide a clearer emerging picture of the physical processes that take place in and around galaxies, and to identify which of these processes are the most important in shaping the properties of galaxies., [Aims] The Lockman Spectroscopic Redshift Survey using Osiris (Lockman-SpReSO) aims to provide one of the most complete optical spectroscopic follow-ups of the far-infrared (FIR) sources detected by the Herschel Space Observatory in the Lockman Hole (LH) field. The optical spectroscopic study of the FIR-selected galaxies supplies valuable information about the relation between fundamental FIR and optical parameters, including extinction, star formation rate, and gas metallicity. In this article, we introduce and provide an in-depth description of the Lockman-SpReSO project and of its early results., [Methods] We selected FIR sources from Herschel observations of the central 24 arcmin ×24 arcmin of the LH field with an optical counterpart up to 24.5 RC(AB). The sample comprises 956 Herschel FIR sources, plus 188 additional interesting objects in the field. These are point X-ray sources, cataclysmic variable star candidates, high-velocity halo star candidates, radio sources, very red quasi-stellar objects, and optical counterparts of sub-millimetre galaxies. The faint component of the catalogue (RC(AB) ≥ 20) was observed using the OSIRIS instrument on the 10.4 m Gran Telescopio Canarias in multi-object spectroscopy (MOS) mode. The bright component was observed using two multi-fibre spectrographs: the AF2-WYFFOS at the William Herschel Telescope and the HYDRA instrument at the WYIN telescope., [Results] From an input catalogue of 1144 sources, we measured a secure spectroscopic redshift in the range 0.03 ≲ z ≲ 4.96 for 357 sources with at least two identified spectral lines. In addition, for 99 sources that show only one emission or absorption line, a spectroscopic redshift was postulated based on the line and object properties, and photometric redshift. In both cases, properties of emission and absorption lines were measured. Furthermore, to characterize the sample in more depth with determined spectroscopic redshifts, spectral energy distribution (SED) fits were performed using the CIGALE software. The IR luminosity and the stellar mass estimations for the sample are also presented as a preliminary description.

[Context]: The morphology of galaxies provide us with a unique tool for relating and understanding other physical properties and their changes over the course of cosmic time. It is only recently that we have been afforded access to a wealth of data for an unprecedented number galaxies thanks to large and deep surveys. [Aims]: We present the morphological catalogue of the OTELO survey galaxies detected with the Hubble Space Telescope (HST)-ACS F814W images. We explore various methods applied in previous works to separate early-type (ET) and late-type (LT) galaxies classified via spectral energy distribution (SED) fittings using galaxy templates. Together with this article, we are releasing a catalogue containing the main morphological parameters in the F606W and F814W bands derived for more than 8000 sources. [Methods]: The morphological analysis is based on the single-Sérsic profile fit. We used the GALAPAGOS2 software to provide multi-wavelength morphological parameters fitted simultaneously in two HST-ACS bands. The GALAPAGOS2 software detects, prepares guess values for GALFIT-M, and provides the best-fitting single-Sérsic model in both bands for each source. Stellar masses were estimated using synthetic rest-frame magnitudes recovered from SED fittings of galaxy templates. The morphological catalogue is complemented with concentration indexes from a separate SExtractor dual, high dynamical range mode. [Results]: A total of 8812 sources were successfully fitted with single-Sérsic profiles. The analysis of a carefully selected sample of ∼3000 sources up to zphot = 2 is presented in this work, of which 873 sources were not detected in previous studies. We found no statistical evidence for the evolution of the low-mass end of mass-size relation for ET and LT since z = 2. Furthermore, we found a good agreement for the median size evolution for ET and LT galaxies, for a given stellar mass, with the data from the literature. Compared to previous works on faint field gal

[Context]: Computational techniques are essential for mining large databases produced in modern surveys with value-Added products. [Aims]: This paper presents a machine learning procedure to carry out a galaxy morphological classification and photometric redshift estimates simultaneously. Currently, only a spectral energy distribution (SED) fitting has been used to obtain these results all at once. [Methods]: We used the ancillary data gathered in the OTELO catalog and designed a nonsequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results of the morphological classification task to the redshift fitting process to ensure consistency between both procedures. [Results]: The results successfully recover the morphological classification and the redshifts of the test sample, reducing catastrophic redshift outliers produced by an SED fitting and avoiding possible discrepancies between independent classification and redshift estimates. Our technique may be adapted to include galaxy images to improve the classification.

© ESO 2020. Artículo firmado por 22 autores. The authors are grateful to the referee for careful reading of the paper and valuable suggestions and comments. 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 María de Maeztu CAB). EJA acknowledges support from the Spanish Government Ministerio de Ciencia, Innovación 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 Astrofísica de Andalucía (SEV2017-0709). JG receives support through the project AyA2018-RTI-096188-B100. 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 Cienc, 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., Ministerio de Ciencia e Innovación (MICINN)/FEDER, Ministerio de Economía y Competitividad (MINECO), Unidad de Excelencia María de Maeztu, Centro de Excelencia Severo Ochoa, the project Evolution of Galaxies 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 d, the UNAM-DGAPA-PASPA 2019 program, the UNAM-CIC, the Canary Islands CIE: Tricontinental Atlantic Campus 2017, the kind hospitality of the IAC, the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Innovation and Technology (MoIT), the PNAYA project, Carlsberg Foundation via a Semper Ardens, Sodercan S.A. through the Proyecto Puente, the Universidad de Cantabria, Spain, through the Proyecto Puente, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

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.

[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.

Context. The evolution of galaxies through cosmic time is studied observationally by means of extragalactic surveys. The usefulness of these surveys is greatly improved by increasing the cosmological volume, in either depth or area, and by observing the same targets in different wavelength ranges. A multi-wavelength approach using different observational techniques can compensate for observational biases. Aims. The OTELO survey aims to provide the deepest narrow-band survey to date in terms of minimum detectable flux and emission line equivalent width in order to detect the faintest extragalactic emission line systems. In this way, OTELO data will complements other broad-band, narrow-band, and spectroscopic surveys. Methods. The red tunable filter of the OSIRIS instrument on the 10.4m Gran Telescopio Canarias (GTC) is used to scan a spectral window centred at 9175 angstrom, which is free from strong sky emission lines, with a sampling interval of 6 angstrom and a bandwidth of 12 angstrom in the most deeply explored EGS region. Careful data reduction using improved techniques for sky ring subtraction, accurate astrometry, photometric calibration, and source extraction enables us to compile the OTELO catalogue. This catalogue is complemented with ancillary data ranging from deep X-ray to far-infrared, including high resolution HST images, which allow us to segregate the different types of targets, derive precise photometric redshifts, and obtain the morphological classification of the extragalactic objects detected. Results. The OTELO multi-wavelength catalogue contains 11 237 entries and is 50% complete at AB magnitude 26.38. Of these sources, 6600 have photometric redshifts with an uncertainty delta z(phot) better than 0.2 (1+z(phot)). A total of 4336 of these sources correspond to preliminary emission line candidates, which are complemented by 81 candidate stars and 483 sources that qualify as absorption line systems. The OTELO survey results will be released to th

Accurate calibration of data is essential for the current generation of cosmic microwave background (CMB) experiments. Using data from the Very Small Array (VSA), we describe procedures which will lead to an accuracy of 1 per cent or better for experiments such as the VSA and CBI. Particular attention is paid to the stability of the receiver systems, the quality of the site and frequent observations of reference sources. At 30 GHz the careful correction for atmospheric emission and absorption is shown to be essential for achieving 1 per cent precision. The sources for which a 1 per cent relative flux density calibration was achieved included Cas A, Cyg A, Tau A and NGC 7027 and the planets Venus, Jupiter and Saturn. A flux density, or brightness temperature in the case of the planets, was derived at 33 GHz relative to Jupiter which was adopted as the fundamental calibrator. A spectral index at similar to 30 GHz is given for each. Cas A, Tau A, NGC 7027 and Venus were examined for variability. Cas A was found to be decreasing at 0.394 ± 0.019 per cent yr^(-1) over the period 2001 March to 2004 August. In the same period Tau A was decreasing at 0.22 ± 0.07 per cent yr^(-1). A survey of the published data showed that the planetary nebula NGC 7027 decreased at 0.16 ± 0.04 per cent yr^(-1) over the period 1967-2003. Venus showed an insignificant (1.5 ±1.3 per cent) variation with Venusian illumination. The integrated polarization of Tau A at 33 GHz was found to be 7.8 ± 0.6 per cent at position angle =148° ± 3°.