Author: "Ordieres-Mere, J." - Searchworks@Jio Institute Digital Library Search Results

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24 results on '"Ordieres-Mere, J."'

1. Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs

Author: Mas-Buitrago, P., González-Marcos, A., Solano, E., Passegger, V. M., Cortés-Contreras, M., Ordieres-Meré, J., Bello-García, A., Caballero, J. A., Schweitzer, A., Tabernero, H. M., Montes, D., and Cifuentes, C.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract: Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthetic data). We propose a feature-based deep transfer learning (DTL) approach based on autoencoders to determine stellar parameters from high-resolution spectra. Using this methodology, we provide new estimations for the effective temperature, surface gravity, metallicity, and projected rotational velocity for 286 M dwarfs observed by the CARMENES survey. Using autoencoder architectures, we projected synthetic PHOENIX-ACES spectra and observed CARMENES spectra onto a new feature space of lower dimensionality in which the differences between the two domains are reduced. We used this low-dimensional new feature space as input for a convolutional neural network to obtain the stellar parameter determinations. We performed an extensive analysis of our estimated stellar parameters, ranging from 3050 to 4300 K, 4.7 to 5.1 dex, and -0.53 to 0.25 dex for Teff, logg, and [Fe/H], respectively. Our results are broadly consistent with those of recent studies using CARMENES data, with a systematic deviation in our Teff scale towards hotter values for estimations above 3750 K. Furthermore, our methodology mitigates the deviations in metallicity found in previous DL techniques due to the synthetic gap. We consolidated a DTL-based methodology to determine stellar parameters in M dwarfs from synthetic spectra, with no need for high-quality measurements involved in the knowledge transfer. These results suggest the great potential of DTL to mitigate the differences in feature distributions between the observations and the PHOENIX-ACES spectra., Comment: Accepted in A&A
Published: 2024
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2. The CARMENES search for exoplanets around M dwarfs -- A deep transfer learning method to determine Teff and [M/H] of target stars

Author: Bello-García, A., Passegger, V. M., Ordieres-Meré, J., Schweitzer, A., Caballero, J. A., González-Marcos, A., Ribas, I., Reiners, A., Quirrenbach, A., Amado, P. J., Béjar, V. J. S., Cifuentes, C., Henning, Th., Kaminski, A., Luque, R., Montes, D., Morales, J. C., Pedraz, S., Tabernero, H. M., and Zechmeister, M.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20 K for temperature and 0.2 dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars.
Published: 2023
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3. J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology

Author: Mas-Buitrago, P., Solano, E., González-Marcos, A., Rodrigo, C., Martín, E. L., Caballero, J. A., Jiménez-Esteban, F., Cruz, P., Ederoclite, A., Ordieres-Meré, J., Bello-García, A., Dupke, R. A., Cenarro, A. J., Cristóbal-Hornillos, D., Hernández-Monteagudo, C., López-Sanjuan, C., Marín-Franch, A., Moles, M., Varela, J., Ramió, H. Vázquez, Alcaniz, J., Sodré Jr., L., and Angulo, R. E.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar population and brown dwarfs, from M7 V to cooler objects with L, T, and Y spectral types. Most of them have been discovered using wide-field imaging surveys, for which the Virtual Observatory (VO) has proven to be of great utility. We aim to perform a search for UCDs in the entire Javalambre Photometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$) following a VO methodology. We also explore the ability to reproduce this search with a purely machine learning (ML)-based methodology that relies solely on J-PLUS photometry. We followed three different approaches based on parallaxes, proper motions, and colours, respectively, using the VOSA tool to estimate the effective temperatures. For the ML methodology, we built a two-step method based on principal component analysis and support vector machine algorithms. We identified a total of 7827 new candidate UCDs, which represents an increase of about 135% in the number of UCDs reported in the sky coverage of the J-PLUS second data release. Among the candidate UCDs, we found 122 possible unresolved binary systems, 78 wide multiple systems, and 48 objects with a high Bayesian probability of belonging to a young association. We also identified four objects with strong excess in the filter corresponding to the Ca II H and K emission lines and four other objects with excess emission in the H$\alpha$ filter. With the ML approach, we obtained a recall score of 92% and 91% in the test and blind test, respectively. We consolidated the proposed search methodology for UCDs, which will be used in deeper and larger upcoming surveys such as J-PAS and Euclid. We concluded that the ML methodology is more efficient in the sense that it allows for a larger number of true negatives to be discarded prior to analysis with VOSA, although it is more photometrically restrictive., Comment: Accepted in A&A
Published: 2022
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4. Metallicities in M dwarfs: Investigating different determination techniques

Author: Passegger, V. M., Bello-García, A., Ordieres-Meré, J., Antoniadis-Karnavas, A., Marfil, E., Duque-Arribas, C., Amado, P. J., Delgado-Mena, E., Montes, D., Rojas-Ayala, B., Schweitzer, A., Tabernero, H. M., Béjar, V. J. S., Caballero, J. A., Hatzes, A. P., Henning, Th., Pedraz, S., Quirrenbach, A., Reiners, A., and Ribas, I.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: Deriving metallicities for solar-like stars follows well-established methods, but for cooler stars such as M dwarfs, the determination is much more complicated due to forests of molecular lines that are present. Several methods have been developed in recent years to determine accurate stellar parameters for these cool stars ($T_{\rm eff} \lesssim$ 4000 K). However, significant differences can be found at times when comparing metallicities for the same star derived using different methods. In this work, we determine the effective temperatures, surface gravities, and metallicities of 18 well-studied M dwarfs observed with the CARMENES high-resolution spectrograph following different approaches, including synthetic spectral fitting, analysis of pseudo-equivalent widths, and machine learning. We analyzed the discrepancies in the derived stellar parameters, including metallicity, in several analysis runs. Our goal is to minimize these discrepancies and find stellar parameters that are more consistent with the literature values. We attempted to achieve this consistency by standardizing the most commonly used components, such as wavelength ranges, synthetic model spectra, continuum normalization methods, and stellar parameters. We conclude that although such modifications work quite well for hotter main-sequence stars, they do not improve the consistency in stellar parameters for M dwarfs, leading to mean deviations of around 50-200 K in temperature and 0.1-0.3 dex in metallicity. In particular, M dwarfs are much more complex and a standardization of the aforementioned components cannot be considered as a straightforward recipe for bringing consistency to the derived parameters. Further in-depth investigations of the employed methods would be necessary in order to identify and correct for the discrepancies that remain., Comment: accepted for publication in A&A
Published: 2021
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5. The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars

Author: Passegger, V. M., Bello-García, A., Ordieres-Meré, J., Caballero, J. A., Schweitzer, A., González-Marcos, A., Ribas, I., Reiners, A., Quirrenbach, A., Amado, P. J., Azzaro, M., Bauer, F. F., Béjar, V. J. S., Cortés-Contreras, M., Dreizler, S., Hatzes, A. P., Henning, Th., Jeffers, S. V., Kaminski, A., Kürster, M., Lafarga, M., Marfil, E., Montes, D., Morales, J. C., Nagel, E., Sarro, L. M., Solano, E., Tabernero, H. M., and Zechmeister, M.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and predict stellar parameters such as effective temperature, surface gravity, metallicity, and rotational velocity. With this study, we firstly demonstrate the capability of deep neural networks to precisely recover stellar parameters from a synthetic training set. Secondly, we analyze the application of this method to observed spectra and the impact of the synthetic gap (i.e., the difference between observed and synthetic spectra) on the estimation of stellar parameters, their errors, and their precision. Our convolutional network is trained on synthetic PHOENIX-ACES spectra in different optical and near-infrared wavelength regions. For each of the four stellar parameters, $T_{\rm eff}$, $\log{g}$, [M/H], and $v \sin{i}$, we constructed a neural network model to estimate each parameter independently. We then applied this method to 50 M dwarfs with high-resolution spectra taken with CARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs), which operates in the visible (520-960 nm) and near-infrared wavelength range (960-1710 nm) simultaneously. Our results are compared with literature values for these stars. They show mostly good agreement within the errors, but also exhibit large deviations in some cases, especially for [M/H], pointing out the importance of a better understanding of the synthetic gap.
Published: 2020
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6. Gaia Data Release 1. Testing the parallaxes with local Cepheids and RR Lyrae stars

Author: Gaia Collaboration, Clementini, G., Eyer, L., Ripepi, V., Marconi, M., Muraveva, T., Garofalo, A., Sarro, L. M., Palmer, M., Luri, X., Molinaro, R., Rimoldini, L., Szabados, L., Musella, I., Anderson, R. I., Prusti, T., de Bruijne, J. H. J., Brown, A. G. A., Vallenari, A., Babusiaux, C., Bailer-Jones, C. A. L., Bastian, U., Biermann, M., Evans, D. W., Jansen, F., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., van Leeuwen, F., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Drimmel, R., Høg, E., Katz, D., Lattanzi, M. G., O'Mullane, W., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Perryman, M., Bramante, L., Cacciari, C., Castañeda, J., Chaoul, L., Cheek, N., De Angeli, F., Fabricius, C., Guerra, R., Hernández, J., Jean-Azntoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordóñez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thévenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., De Ridder, J., de Torres, A., Delchambre, L., Dell'Oro, A., Ducourant, C., Frémat, Y., García-Torres, M., Gosset, E., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, A. J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M., Recio-Blanco, A., Robin, A. C., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., van Reeven, W., Viala, Y., Abbas, U., Aramburu, A. Abreu, Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Álvarez, M. A., Alves, J., Andrei, A. H., Varela, E. Anglada, Antiche, E., Antoja, T., Antón, S., Arcay, B., Bach, N., Baker, S. G., Balaguer-Núñez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Navascués, D. Barrado y, Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., García, A. Bello, Belokurov, V., Bendjoya, P., Berihuete, A., Bianchi, L., Bienaymé, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bouy, H., Bragaglia, A., Breddels, M. A., Brouillet, N., Brüsemeister, T., Bucciarelli, B., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Caffau, E., Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Costigan, G., Crifo, F., Cross, N. J. G., Crosta, M., Crowley, C., Dafonte, C., Damerdji, Y., Dapergolas, A., David, P., David, M., De Cat, P., de Felice, F., de Laverny, P., De Luise, F., De March, R., de Souza, R., Debosscher, J., del Pozo, E., Delbo, M., Delgado, A., Delgado, H. E., Di Matteo, P., Diakite, S., Distefano, E., Dolding, C., Anjos, S. Dos, Drazinos, P., Durán, J., Dzigan, Y., Edvardsson, B., Enke, H., Evans, N. W., Bontemps, G. Eynard, Fabre, C., Fabrizio, M., Faigler, S., Falcão, A. J., Casas, M. Farràs, Federici, L., Fedorets, G., Fernández-Hernánde, J., Fernique, P., Fienga, A., Figueras, F., Filippi, F., Findeisen, K., Fonti, A., Fouesneau, M., Fraile, E., Fraser, M., Fuchs, J., Gai, M., Galleti, S., Galluccio, L., Garabato, D., García-Sedano, F., Garralda, N., Gavras, P., Gerssen, J., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomes, M., González-Marcos, A., González-Núñez, J., González-Vidal, J. J., Granvik, M., Guerrier, A., Guillout, P., Guiraud, J., Gúrpide, A., Gutiérrez-Sánchez, R., Guy, L. P., Haigron, R., Hatzidimitriou, D., Haywood, M., Heiter, U., Helmi, A., Hobbs, D., Hofmann, W., Holl, B., Holland, G., Hunt, J. A. S., Hypki, A., Icardi, V., Irwin, M., de Fombelle, G. Jevardat, Jofré, P., Jonker, P. G., Jorissen, A., Julbe, F., Karampelas, A., Kochoska, A., Kohley, R., Kolenberg, K., Kontizas, E., Koposov, S. E., Kordopatis, G., Koubsky, P., Krone-Martins, A., Kudryashova, M., Kull, I., Bachchan, R. K., Lacoste-Seris, F., Lanza, A. F., Lavigne, J. -B., Poncin-Lafitte, C. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Lemaitre, V., Lenhardt, H., Leroux, F., Liao, S., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Löffler, W., López, M., Lorenz, D., MacDonald, I., Fernandes, T. Magalhães, Managau, S., Mann, R. G., Mantelet, G., Marchal, O., Marchant, J. M., Marinoni, S., Marrese, P. M., Marschalkó, G., Marshall, D. J., Martín-Fleitas, J. M., Martino, M., Mary, N., Matijevič, G., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Miranda, B. M. H., Molina, D., Molinaro, M., Molnár, L., Moniez, M., Montegriffo, P., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morgenthaler, S., Morris, D., Mulone, A. F., Narbonne, J., Nelemans, G., Nicastro, L., Noval, L., Ordénovic, C., Ordieres-Meré, J., Osborne, P., Pagani, C., Pagano, I., Pailler, F., Palacin, H., Palaversa, L., Parsons, P., Pecoraro, M., Pedrosa, R., Pentikäinen, H., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poujoulet, E., Prša, A., Pulone, L., Ragaini, S., Rago, S., Rambaux, N., Ramos-Lerate, M., Ranalli, P., Rauw, G., Read, A., Regibo, S., Reylé, C., Ribeiro, R. A., Riva, A., Rixon, G., Roelens, M., Romero-Gómez, M., Rowell, N., Royer, F., Ruiz-Dern, L., Sadowski, G., Sellés, T. Sagristà, Sahlmann, J., Salgado, J., Salguero, E., Sarasso, M., Savietto, H., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Segransan, D., Shih, I-C., Smareglia, R., Smart, R. L., Solano, E., Solitro, F., Sordo, R., Nieto, S. Soria, Souchay, J., Spagna, A., Spoto, F., Stampa, U., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Stoev, H., Suess, F. F., Süveges, M., Surdej, J., Szegedi-Elek, E., Tapiador, D., Taris, F., Tauran, G., Taylor, M. B., Teixeira, R., Terrett, D., Tingley, B., Trager, S. C., Turon, C., Ulla, A., Utrilla, E., Valentini, G., van Elteren, A., Van Hemelryck, E., van Leeuwen, M., Varadi, M., Vecchiato, A., Veljanoski, J., Via, T., Vicente, D., Vogt, S., Voss, H., Votruba, V., Voutsinas, S., Walmsley, G., Weiler, M., Weingrill, K., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Žerjal, M., Zucker, S., Zurbach, C., Zwitter, T., Alecu, A., Allen, M., Prieto, C. Allende, Amorim, A., Anglada-Escudé, G., Arsenijevic, V., Azaz, S., Balm, P., Beck, M., Bernstein, H. -H., Bigot, L., Bijaoui, A., Blasco, C., Bonfigli, M., Bono, G., Boudreault, S., Bressan, A., Brown, S., Brunet, P. -M., Bunclark, P., Buonanno, R., Butkevich, A. G., Carret, C., Carrion, C., Chemin, L., Chéreau, F., Corcione, L., Darmigny, E., de Boer, K. S., de Teodoro, P., de Zeeuw, P. T., Luche, C. Delle, Domingues, C. D., Dubath, P., Fodor, F., Frézouls, B., Fries, A., Fustes, D., Fyfe, D., Gallardo, E., Gallegos, J., Gardiol, D., Gebran, M., Gomboc, A., Gómez, A., Grux, E., Gueguen, A., Heyrovsky, A., Hoar, J., Iannicola, G., Parache, Y. Isasi, Janotto, A. -M., Joliet, E., Jonckheere, A., Keil, R., Kim, D. -W., Klagyivik, P., Klar, J., Knude, J., Kochukhov, O., Kolka, I., Kos, J., Kutka, A., Lainey, V., LeBouquin, D., Liu, C., Loreggia, D., Makarov, V. V., Marseille, M. G., Martayan, C., Martinez-Rubi, O., Massart, B., Meynadier, F., Mignot, S., Munari, U., Nguyen, A. -T., Nordlander, T., O'Flaherty, K. S., Ocvirk, P., Sanz, A. Olias, Ortiz, P., Osorio, J., Oszkiewicz, D., Ouzounis, A., Park, P., Pasquato, E., Peltzer, C., Peralta, J., Péturaud, F., Pieniluoma, T., Pigozzi, E., Poels, J., Prat, G., Prod'homme, T., Raison, F., Rebordao, J. M., Risquez, D., Rocca-Volmerange, B., Rosen, S., Ruiz-Fuertes, M. I., Russo, F., Sembay, S., Vizcaino, I. Serraller, Short, A., Siebert, A., Silva, H., Sinachopoulos, D., Slezak, E., Soffel, M., Sosnowska, D., Straižys, V., ter Linden, M., Terrell, D., Theil, S., Tiede, C., Troisi, L., Tsalmantza, P., Tur, D., Vaccari, M., Vachier, F., Valles, P., Van Hamme, W., Veltz, L., Virtanen, J., Wallut, J. -M., Wichmann, R., Wilkinson, M. I., Ziaeepour, H., and Zschocke, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Parallaxes for 331 classical Cepheids, 31 Type II Cepheids and 364 RR Lyrae stars in common between Gaia and the Hipparcos and Tycho-2 catalogues are published in Gaia Data Release 1 (DR1) as part of the Tycho-Gaia Astrometric Solution (TGAS). In order to test these first parallax measurements of the primary standard candles of the cosmological distance ladder, that involve astrometry collected by Gaia during the initial 14 months of science operation, we compared them with literature estimates and derived new period-luminosity ($PL$), period-Wesenheit ($PW$) relations for classical and Type II Cepheids and infrared $PL$, $PL$-metallicity ($PLZ$) and optical luminosity-metallicity ($M_V$-[Fe/H]) relations for the RR Lyrae stars, with zero points based on TGAS. The new relations were computed using multi-band ($V,I,J,K_{\mathrm{s}},W_{1}$) photometry and spectroscopic metal abundances available in the literature, and applying three alternative approaches: (i) by linear least squares fitting the absolute magnitudes inferred from direct transformation of the TGAS parallaxes, (ii) by adopting astrometric-based luminosities, and (iii) using a Bayesian fitting approach. TGAS parallaxes bring a significant added value to the previous Hipparcos estimates. The relations presented in this paper represent first Gaia-calibrated relations and form a "work-in-progress" milestone report in the wait for Gaia-only parallaxes of which a first solution will become available with Gaia's Data Release 2 (DR2) in 2018., Comment: 29 pages, 25 figures. Accepted for publication by A&A
Published: 2017
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7. Gaia Data Release 1. Open cluster astrometry: performance, limitations, and future prospects

Author: Gaia Collaboration, van Leeuwen, F., Vallenari, A., Jordi, C., Lindegren, L., Bastian, U., Prusti, T., de Bruijne, J. H. J., Brown, A. G. A., Babusiaux, C., Bailer-Jones, C. A. L., Biermann, M., Evans, D. W., Eyer, L., Jansen, F., Klioner, S. A., Lammers, U., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Drimmel, R., Høg, E., Katz, D., Lattanzi, M. G., O'Mullane, W., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Perryman, M., Bramante, L., Cacciari, C., Castañeda, J., Chaoul, L., Cheek, N., De Angeli, F., Fabricius, C., Guerra, R., Hernández, J., Jean-Antoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordóñez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thévenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Clementini, G., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., De Ridder, J., de Torres, A., Delchambre, L., Dell'Oro, A., Ducourant, C., Frémat, Y., García-Torres, M., Gosset, E., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, A. J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M., Recio-Blanco, A., Robin, A. C., Sarro, L. M., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., van Reeven, W., Viala, Y., Abbas, U., Aramburu, A. Abreu, Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Álvarez, M. A., Alves, J., Anderson, R. I., Andrei, A. H., Varela, E. Anglada, Antiche, E., Antoja, T., Antón, S., Arcay, B., Bach, N., Baker, S. G., Balaguer-Núñez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Navascués, D. Barrado y, Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., García, A. Bello, Belokurov, V., Bendjoya, P., Berihuete, A., Bianchi, L., Bienaymé, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bouy, H., Bragaglia, A., Breddels, M. A., Brouillet, N., Brüsemeister, T., Bucciarelli, B., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Caffau, E., Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Costigan, G., Crifo, F., Cross, N. J. G., Crosta, M., Crowley, C., Dafonte, C., Damerdji, Y., Dapergolas, A., David, P., David, M., De Cat, P., de Felice, F., de Laverny, P., De Luise, F., De March, R., de Martino, D., de Souza, R., Debosscher, J., del Pozo, E., Delbo, M., Delgado, A., Delgado, H. E., Di Matteo, P., Diakite, S., Distefano, E., Dolding, C., Anjos, S. Dos, Drazinos, P., Durán, J., Dzigan, Y., Edvardsson, B., Enke, H., Evans, N. W., Bontemps, G. Eynard, Fabre, C., Fabrizio, M., Faigler, S., Falcão, A. J., Casas, M. Farràs, Federici, L., Fedorets, G., Fernández-Hernández, J., Fernique, P., Fienga, A., Figueras, F., Filippi, F., Findeisen, K., Fonti, A., Fouesneau, M., Fraile, E., Fraser, M., Fuchs, J., Gai, M., Galleti, S., Galluccio, L., Garabato, D., García-Sedano, F., Garofalo, A., Garralda, N., Gavras, P., Gerssen, J., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomes, M., González-Marcos, A., González-Núñez, J., González-Vidal, J. J., Granvik, M., Guerrier, A., Guillout, P., Guiraud, J., Gúrpide, A., Gutiérrez-Sánchez, R., Guy, L. P., Haigron, R., Hatzidimitriou, D., Haywood, M., Heiter, U., Helmi, A., Hobbs, D., Hofmann, W., Holl, B., Holland, G., Hunt, J. A. S., Hypki, A., Icardi, V., Irwin, M., de Fombelle, G. Jevardat, Jofré, P., Jonker, P. G., Jorissen, A., Julbe, F., Karampelas, A., Kochoska, A., Kohley, R., Kolenberg, K., Kontizas, E., Koposov, S. E., Kordopatis, G., Koubsky, P., Krone-Martins, A., Kudryashova, M., Kull, I., Bachchan, R. K., Lacoste-Seris, F., Lanza, A. F., Lavigne, J. -B., Poncin-Lafitte, C. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Lemaitre, V., Lenhardt, H., Leroux, F., Liao, S., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Löffer, W., López, M., Lorenz, D., MacDonald, I., Fernandes, T. Magalhães, Managau, S., Mann, R. G., Mantelet, G., Marchal, O., Marchant, J. M., Marconi, M., Marinoni, S., Marrese, P. M., Marschalkó, G., Marshall, D. J., Martín-Fleitas, J. M., Martino, M., Mary, N., Matijevič, G., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Miranda, B. M. H., Molina, D., Molinaro, R., Molinaro, M., Molnár, L., Moniez, M., Montegrio, P., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morgenthaler, S., Morris, D., Mulone, A. F., Muraveva, T., Musella, I., Narbonne, J., Nelemans, G., Nicastro, L., Noval, L., Ordénovic, C., Ordieres-Meré, J., Osborne, P., Pagani, C., Pagano, I., Pailler, F., Palacin, H., Palaversa, L., Parsons, P., Pecoraro, M., Pedrosa, R., Pentikäinen, H., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poujoulet, E., Prša, A., Pulone, L., Ragaini, S., Rago, S., Rambaux, N., Ramos-Lerate, M., Ranalli, P., Rauw, G., Read, A., Regibo, S., Reylé, C., Ribeiro, R. A., Rimoldini, L., Ripepi, V., Riva, A., Rixon, G., Roelens, M., Romero-Gómez, M., Rowell, N., Royer, F., Ruiz-Dern, L., Sadowski, G., Sellés, T. Sagristà, Sahlmann, J., Salgado, J., Salguero, E., Sarasso, M., Savietto, H., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Segransan, D., Shih, I-C., Smareglia, R., Smart, R. L., Solano, E., Solitro, F., Sordo, R., Nieto, S. Soria, Souchay, J., Spagna, A., Spoto, F., Stampa, U., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Stoev, H., Suess, F. F., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Tapiador, D., Taris, F., Tauran, G., Taylor, M. B., Teixeira, R., Terrett, D., Tingley, B., Trager, S. C., Turon, C., Ulla, A., Utrilla, E., Valentini, G., van Elteren, A., Van Hemelryck, E., van Leeuwen, M., Varadi, M., Vecchiato, A., Veljanoski, J., Via, T., Vicente, D., Vogt, S., Voss, H., Votruba, V., Voutsinas, S., Walmsley, G., Weiler, M., Weingril, K., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Žerjal, M., Zucker, S., Zurbach, C., Zwitter, T., Alecu, A., Allen, M., Prieto, C. Allende, Amorim, A., Anglada-Escudé, G., Arsenijevic, V., Azaz, S., Balm, P., Beck, M., Bernsteiny, H. -H., Bigot, L., Bijaoui, A., Blasco, C., Bonfigli, M., Bono, G., Boudreault, S., Bressan, A., Brown, S., Brunet, P. -M., Bunclarky, P., Buonanno, R., Butkevich, A. G., Carret, C., Carrion, C., Chemin, L., Chéreau, F., Corcione, L., Darmigny, E., de Boer, K. S., de Teodoro, P., de Zeeuw, P. T., Luche, C. Delle, Domingues, C. D., Dubath, P., Fodor, F., Frézouls, B., Fries, A., Fustes, D., Fyfe, D., Gallardo, E., Gallegos, J., Gardio, D., Gebran, M., Gomboc, A., Gómez, A., Grux, E., Gueguen, A., Heyrovsky, A., Hoar, J., Iannicola, G., Parache, Y. Isasi, Janotto, A. -M., Joliet, E., Jonckheere, A., Keil, R., Kim, D. -W., Klagyivik, P., Klar, J., Knude, J., Kochukhov, O., Kolka, I., Kos, J., Kutka, A., Lainey, V., LeBouquin, D., Liu, C., Loreggia, D., Makarov, V. V., Marseille, M. G., Martayan, C., Martinez-Rubi, O., Massart, B., Meynadier, F., Mignot, S., Munari, U., Nguyen, A. -T., Nordlander, T., O'Flaherty, K. S., Ocvirk, P., Sanz, A. Olias, Ortiz, P., Osorio, J., Oszkiewicz, D., Ouzounis, A., Palmer, M., Park, P., Pasquato, E., Peltzer, C., Peralta, J., Péturaud, F., Pieniluoma, T., Pigozzi, E., Poelsy, J., Prat, G., Prod'homme, T., Raison, F., Rebordao, J. M., Risquez, D., Rocca-Volmerange, B., Rosen, S., Ruiz-Fuertes, M. I., Russo, F., Sembay, S., Vizcaino, I. Serraller, Short, A., Siebert, A., Silva, H., Sinachopoulos, D., Slezak, E., Soffel, M., Sosnowska, D., Straižys, V., ter Linden, M., Terrell, D., Theil, S., Tiede, C., Troisi, L., Tsalmantza, P., Tur, D., Vaccari, M., Vachier, F., Valles, P., Van Hamme, W., Veltz, L., Virtanen, J., Wallut, J. -M., Wichmann, R., Wilkinson, M. I., Ziaeepour, H., and Zschocke, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information. Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters. Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed. Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier Hipparcos-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters. Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the Hipparcos data, with clearly increased luminosities for older A and F dwarfs., Comment: Accepted for publication by A&A. 21 pages main text plus 46 pages appendices. 34 figures main text, 38 figures appendices. 8 table in main text, 19 tables in appendices
Published: 2017
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8. Program and Project Management Articulation: Evidences from the Infrastructure Sector

Author: González, V., Hetemi, E., Bosch-Rekveldt, M., Ordieres-Meré, J., López-Paredes, Adolfo, Series Editor, Ayuso Muñoz, José Luis, editor, Yagüe Blanco, José Luis, editor, and Capuz-Rizo, Salvador F., editor
Published: 2021
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9. Finite element analysis of the hyper-elastic contact problem in automotive door sealing

Author: Ordieres-Meré, J., Bello-García, A., Muñoz-Munilla, V., and Del-Coz-Díaz, J.J.
Published: 2008
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10. Testing parallaxes with local Cepheids and RR Lyrae stars

Author: Clementini, G., Eyer, L., Ripepi, V., Marconi, M., Muraveva, T., Garofalo, A., Sarro, L. M., Palmer, M., Luri, X., Molinaro, R., Rimoldini, L., Szabados, L., Musella, I., Anderson, R. I., Prusti, T., De Bruijne, J. H. J., Brown, A. G. A., Vallenari, A., Babusiaux, C., Bailer-Jones, C. A. L., Bastian, U., Biermann, M., Evans, D. W., Jansen, F., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., Van Leeuwen, F., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Drimmel, R., Hog, E., Katz, D., Lattanzi, M. G., O'Mullane, W., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Perryman, M., Bramante, L., Cacciari, C., Castaneda, J., Chaoul, L., Cheek, N., De Angeli, F., Fabricius, C., Guerra, R., Hernandez, J., Jean-Antoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordonez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thevenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., De Ridder, J., De Torres, A., Delchambre, L., Dell'Oro, A., Ducourant, C., Fremat, Y., Garcia-Torres, M., Gosset, E., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, A. J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M., Recio-Blanco, A., Robin, A. C., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., Van Reeven, W., Viala, Y., Abbas, U., Abreu Aramburu, A., Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Alvarez, M. A., Alves, J., Andrei, A. H., Anglada Varela, E., Antiche, E., Antojal, T., Anton, S., Arcay, B., Bach, N., Baker, S. G., Balaguer-Nunez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Barrado Y Navascues, D., Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., Bello Garcia, A., Belokurov, V., Bendjoya, P., Berihuete, A., Bianchi, L., Bienayme, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bragaglia, A., Breddels, M. A., Brouillet, N., Bruesemeister, T., Bucciarelli, B., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Caffau, E., Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Costigan, G., Crifo, F., Cross, N. J. G., Crosta, M., Crowley, C., Dafonte, C., Damerdji, Y., Dapergolas, A., David, P., David, M., De Cat, P., De Felice, F., De Laverny, P., De Luise, F., De March, R., De Souza, R., Debosscher, J., Del Pozo, E., Delbo, M., Delgado, A., Delgado, H. E., Di Matteo, P., Diakite, S., Distefano, E., Dolding, C., Dos Anjos, S., Drazinos, P., Duran, J., Dzigan, Y., Edvardsson, B., Enke, H., Evans, N. W., Bontemps, G. Eynard, Fabre, C., Fabrizio, M., Falcao, A. J., Farras Casas, M., Federici, L., Fedorets, G., Fernandez-Hernandez, J., Fernique, P., Fienga, A., Figueras, F., Filippi, F., Findeisen, K., Fonti, A., Fouesneau, M., Fraile, E., Fraser, M., Fuchs, J., Gai, M., Galleti, S., Galluccio, L., Garabato, D., Garcia-Sedano, F., Garralda, N., Gavras, P., Gerssen, J., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomes, M., Gonzalez-Marcos, A., Gonzalez-Nunez, J., Gonzalez-Vidal, J. J., Granvik, M., Guerrier, A., Guillout, P., Guiraud, J., Gurpide, A., Gutierrez-Sanchez, R., Guy, L. P., Haigron, R., Hatzidimitriou, D., Haywood, M., Heiter, U., Helmi, A., Hobbs, D., Hofmann, W., Holl, B., Holland, G., Hunt, J. A. S., Hypki, A., Icardi, V., Irwin, M., De Fombelle, G. Jevardat, Jofre, P., Jonker, P. G., Jorissen, A., Julbe, F., Karampelas, A., Kochoska, A., Kohley, R., Kolenberg, K., Kontizas, E., Koposov, S. E., Kordopatis, G., Koubsky, P., Krone-Martins, A., Kudryashova, M., Bachchan, R. K., Lacoste-Seris, F., Lanza, A. F., Lavigne, J. -B., Le Poncin-Lafitte, C., Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Lemaitre, V., Lenhardt, H., Leroux, F., Liao, S., Licata, E., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loeffler, W., Lopez, M., Lorenz, D., Macdonald, I., Magalhaes Fernandes, T., Managau, S., Mann, R. G., Mantelet, G., Marchal, O., Marchant, J. M., Marinoni, S., Marrese, P. M., Marschalko, G., Marsha, D. J., Martin-Fleitas, J. M., Martino, M., Mary, N., Matijevic, G., Mcmillan, P. J., Messina, S., Michalik, D., Millar, N. R., Miranda, B. M. H., Molina, D., Molinaro, M., Molnar, L., Monicz, M., Montegriffo, P., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morgenthaler, S., Morris, D., Mulone, A. F., Narbonne, J., Nelemans, G., Nicastro, L., Noval, L., Ordenovic, C., Ordieres-Mere, J., Osborne, P., Pagani, C., Pagano, I., Pailler, F., Palacin, H., Palaversa, L., Parsons, P., Pecoraro, M., Pedrosa, R., Pentikainen, H., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poujoulet, E., Prsa, A., Pulone, L., Ragaini, S., Rago, S., Rambaux, N., Ramos-Lerate, M., Ranalli, P., Rauw, G., Read, A., Regibo, S., Reyle, C., Ribeiro, R. A., Riva, A., Rixon, G., Roelens, M., Romero-Gomez, M., Rowell, N., Royer, F., Ruiz-Dern, L., Sadowski, G., Selles, T. Sagrista, Sahlmann, J., Salgado, J., Salguero, E., Sarasso, M., Savietto, H., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Segransan, D., Shih, I-C., Smareglia, R., Smart, R. L., Solano, E., Solitro, F., Sordo, R., Soria Nieto, S., Souchay, J., Spagna, A., Spoto, F., Stampa, U., Steele, I. A., Steidelmueller, H., Stephenson, C. A., Stoev, H., Suess, F. F., Suveges, M., Surdej, J., Szegedi-Elek, E., Tapiador, D., Taris, F., Tauran, G., Taylor, M. B., Teixeira, R., Terrett, D., Tingley, B., Trager, S. C., Turon, C., Ulla, A., Utrilla, E., Valentini, G., Van Elteren, A., Van Hemelryck, E., Van Leeuwen, M., Varadi, M., Vecchiato, A., Veljanoski, J., Via, T., Vicente, D., Vogt, S., Voss, H., Votruba, V., Voutsinas, S., Walmsley, G., Weiler, M., Weingrill, K., Wevers, T., Wyrzykowski, L., Yoldas, A., Zerjal, M., Zucker, S., Zurbach, C., Zwitter, T., Alecu, A., Allen, M., Allende Prieto, C., Amorim, A., Anglada-Escude, G., Arsenijevic, V., Azaz, S., Balm, P., Beck, M., Bernsteint, H. -H., Bigot, L., Bijaoui, A., Blasco, C., Bonfigli, M., Bono, G., Boudreault, S., Bressan, A., Brown, S., Brunet, P. -M., Bunclark, P., Buonanno, R., Butkevich, A. G., Carret, C., Carrion, C., Chemin, L., Chereau, F., Corcione, L., Darmigny, E., De Boer, K. S., De Teodoro, P., De Zeeuw, P. T., Delle Luche, C., Domingues, C. D., Dubath, P., Fodor, F., Frezouls, B., Fries, A., Fustes, D., Fyfe, D., Gallardo, E., Gallegos, J., Gardiol, D., Gebran, M., Gomboc, A., Gomez, A., Grux, E., Gueguen, A., Heyrovsky, A., Hoar, J., Iannicola, G., Parache, Y. Isasi, Janotto, A. -M., Joliet, E., Jonckheere, A., Keil, R., Kim, D. -W., Klagyivik, P., Klar, J., Knude, J., Kochukhov, O., Kolka, I., Kos, J., Kutka, A., Lainey, V., Lebouquin, D., Liu, C., Loreggia, D., Makarov, V. V., Marseille, M. G., Martayan, C., Martinez-Rubi, O., Massart, B., Meynadier, F., Mignot, S., Munari, U., Nguyen, A. -T., Nordlander, T., O'Flaherty, K. S., Ocvirk, P., Olias Sanz, A., Ortiz, P., Osorio, J., Oszkiewicz, D., Ouzounis, A., Park, P., Pasquato, E., Peltzer, C., Peralta, J., Peturaud, F., Pieniluoma, T., Pigozzi, E., Poels, J., Prat, G., Prod'Homme, T., Raison, F., Rebordao, J. M., Risquez, D., Rocca-Volmerange, B., Rosen, S., Ruiz-Fuertes, M. I., Russo, F., Serraller Vizcaino, I., Short, A., Siebert, A., Silva, H., Sinachopoulos, D., Slezak, E., Soffel, M., Sosnowska, D., Straizys, V., Ter Linden, M., Terrell, D., Theil, S., Tiede, C., Troisi, L., Tsalmantza, P., Tur, D., Vaccari, M., Vachier, F., Valles, P., Van Hamme, W., Veltz, L., Virtanen, J., Wallut, J. -M., Wichmann, R., Wilkinson, M. I., Ziaeepour, H., Zschocke, S., Gaia Collaboration, and Gaia Collaboration
Subjects: stars: variables: RR Lyrae, variables: Cepheids [stars], Astronomi, astrofysik och kosmologi, Physics, parallaxes, distances [stars], data analysis [methods], stars: distances, Astronomy, Astrophysics and Cosmology, astrometry, methods: data analysis, variables: RR Lyrae [stars], stars: variables: Cepheids
Abstract: Context. Parallaxes for 331 classical Cepheids, 31 Type II Cepheids, and 364 RR Lyrae stars in common between Gaia and the HIPPARCOS and Tycho-2 catalogues are published in Gaia Data Release 1 (DR1) as part of the Tycho-Gaia Astrometric Solution (TGAS). Aims. In order to test these first parallax measurements of the primary standard candles of the cosmological distance ladder, which involve astrometry collected by Gaia during the initial 14 months of science operation, we compared them with literature estimates and derived new period-luminosity (PL), period-Wesenheit (PW) relations for classical and Type II Cepheids and infrared PL, PL-metallicity (PLZ), and optical luminosity-metallicity (MV-[Fe/H]) relations for the RR Lyrae stars, with zero points based on TGAS. Methods. Classical Cepheids were carefully selected in order to discard known or suspected binary systems. The final sample comprises 102 fundamental mode pulsators with periods ranging from 1.68 to 51.66 days (of which 33 with sigma(omega)/omega < 0 : 5). The Type II Cepheids include a total of 26 W Virginis and BL Herculis stars spanning the period range from 1.16 to 30.00 days (of which only 7 with sigma(omega)/omega 0 : 5). The RR Lyrae stars include 200 sources with pulsation period ranging from 0.27 to 0.80 days (of which 112 with sigma(omega)/omega < 0 : 5). The new relations were computed using multi- band (V; I; J; K-s) photometry and spectroscopic metal abundances available in the literature, and by applying three alternative approaches: (i) linear least-squares fitting of the absolute magnitudes inferred from direct transformation of the TGAS parallaxes; (ii) adopting astrometry-based luminosities; and (iii) using a Bayesian fitting approach. The last two methods work in parallax space where parallaxes are used directly, thus maintaining symmetrical errors and allowing negative parallaxes to be used. The TGAS-based PL; PW; PLZ, and MV [Fe/H] relations are discussed by comparing the distance to the Large Magellanic Cloud provided by different types of pulsating stars and alternative fitting methods. Results. Good agreement is found from direct comparison of the parallaxes of RR Lyrae stars for which both TGAS and HST measurements are available. Similarly, very good agreement is found between the TGAS values and the parallaxes inferred from the absolute magnitudes of Cepheids and RR Lyrae stars analysed with the Baade-Wesselink method. TGAS values also compare favourably with the parallaxes inferred by theoretical model fitting of the multi-band light curves for two of the three classical Cepheids and one RR Lyrae star, which were analysed with this technique in our samples. The K-band PL relations show the significant improvement of the TGAS parallaxes for Cepheids and RR Lyrae stars with respect to the HIPPARCOS measurements. This is particularly true for the RR Lyrae stars for which improvement in quality and statistics is impressive. Conclusions. TGAS parallaxes bring a significant added value to the previous HIPPARCOS estimates. The relations presented in this paper represent the first Gaia-calibrated relations and form a work-in-progress milestone report in the wait for Gaia-only parallaxes of which a first solution will become available with Gaia Data Release 2 (DR2) in 2018. Acknowledgements. This work has made use of results from the European Space Agency (ESA) space mission Gaia, the data from which were processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Gaia mission website is http://www.cosmos.esa.int/gaia. The authors are current or past members of the ESA and Airbus DS Gaia mission teams and of the Gaia DPAC. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. We thank the referee, Pierre Kervella, for his detailed comments and suggestions that have helped to improve the paper analysis and presentation. This work has financially been supported by: the Agenzia Spaziale Italiana (ASI) through grants I/037/08/0, I/058/10/0, 2014-025-R.0, and 2014-025-R.1.2015 to INAF and contracts I/008/10/0 and 2013/030/I.0 to ALTEC S.p.A.; the Algerian Centre de Recherche en Astronomic, Astrophysique et Geophysique of Bouzareah Observatory; the Austrian FWF Hertha Firnberg Programme through grants T359, P20046, and P23737; the BELgian federal Science Policy Office (BELSPO) through various PROgramme de Developpement d'Experiences scientifiques (PRODEX) grants; the Brazil-France exchange programmes FAPESP-COFECUB and CAPES-COFECUB; the Chinese National Science Foundation through grant NSFC 11573054; the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010; the Danish Ministry of Science; the Estonian Ministry of Education and Research through grant IUT40-1; the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; the European Research Council (ERC) through grant 320360 and through the European Union's Horizon 2020 research and innovation programme through grant agreement 670519 (Mixing and Angular Momentum tranSport of massIvE stars - MAMSIE); the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF); the European Space Agency in the framework of the Gaia project; the European Space Agency Plan for European Cooperating States (PECS) programme through grants for Slovenia; the Czech Space Office through ESA PECS contract 98058; the Academy of Finland; the Magnus Ehrnrooth Foundation; the French Centre National de la Recherche Scientifique (CNRS) through action "Defi MASTODONS"; the French Centre National d'Etudes Spatiales (CNES); the French L'Agence Nationale de la Recherche (ANR) "investissements d'avenir" Initiatives D'EXcellence (IDEX) programme PSL* through grant ANR-10-IDEX-0001-02; the Region Aquitaine; the Universite de Bordeaux; the French Utinam Institute of the Universite de Franche-Comte, supported by the Region de Franche-Comte and the Institut des Sciences de l'Univers (INSU); the German Aerospace Agency (Deutsches Zentrum fur Luft- and Raumfahrt e.V. , DLR) through grants 50QG0501, 50QG0601, 50QG0602, 50QG0701, 50QG0901, 50QG1001, 50QG1101, 50QG140, 50QG1401, 50QG1402, and 50QG1404; the Hungarian Academy of Sciences through Lendulet Programme LP2014-17; the Hungarian National Research, Development, and Innovation Office through grants NKFIH K-115709, K-119517 and PD-116175; the Israel Ministry of Science and Technology through grant 3-9082; the Italian Istituto Nazionale di Astrofisica (INAF); the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.061.414 and through a VICI grant to A. Helmi; the Netherlands Research School for Astronomy (NOVA); the Polish National Science Centre through HARMONIA grant 2015/18/M/ST9/00544; the Portugese Fundacao para a Ciencia e a Tecnologia (FCT) through grants PTDC/CTE-SPA/118692/2010, PDCTE/CTE-AST/81711/2003, and SFRH/BPD/74697/2010; the Strategic Programmes PEst-OE/AMB/UI4006/2011 for SIM, UID/FIS/00099/2013 for CENTRA, and UID/EEA/00066/2013 for UNINOVA; the Slovenian Research Agency; the Spanish Ministry of Economy MINECO-FEDER through grants AyA2014-55216, AyA2011-24052, E5P2013-48318-C2-R, and E5P2014-55996-C2-R and MDM-2014-0369 of ICCUB (Unidad de Excelencia Maria de Maeztu); the Swedish National Space Board (SNSB/Rymdstyrelsen); the Swiss State Secretariat for Education, Research, and Innovation through the ESA PRODEX programme; the Swiss Mesures d'Accompagnement; the Swiss Activites Nationales Complementaires; the Swiss National Science Foundation, including an Early Postdoc.Mobility fellowship; the United Kingdom Rutherford Appleton Laboratory; the United Kingdom Science and Technology Facilities Council (STFC) through grants PP/C506756/1 and ST/100047X/1; and the United Kingdom Space Agency (UKSA) through grants ST/K000578/1 and ST/N000978/1.
Published: 2017

11. Gaia Data Release 1 Summary of the astrometric, photometric, and survey properties

Author: Brown, A. G. A., Vallenari, A., Prusti, T., De Bruijne, J. H. J., Mignard, F., Drimmel, R., Babusiaux, C., Bailer-Jones, C. A. L., Bastian, U., Biermann, M., Evans, D. W., Eyer, L., Jansen, F., Jordi, C., Katz, D., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., O'Mullane, W., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., Van Leeuwen, F., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Hog, E., Lattanzi, M. G., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Perryman, M., Bramante, L., Cacciari, C., Castaneda, J., Chaoul, L., Cheek, N., De Angeli, F., Fabricius, C., Guerra, R., Hernandez, J., Jean-Antoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordonez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thevenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Clementini, G., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., De Ridder, J., De Torres, A., Delchambre, L., Dell'Oro, A., Ducourant, C., Fremat, Y., Garcia-Torres, M., Gosset, E., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, A. J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M., Recio-Blanco, A., Robin, A. C., Sarro, L. M., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., Van Reeven, W., Viala, Y., Abbas, U., Aramburu, A. Abreu, Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Alvarez, M. A., Alves, J., Anderson, R. I., Andrei, A. H., Varela, E. Anglada, Antiche, E., Antoja, T., Anton, S., Arcay, B., Bach, N., Baker, S. G., Balaguer-Nunez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Barrado Y Navascues, D., Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., Garcia, A. Bello, Belokurov, V., Bendjoya, P., Berihuete, A., Bianchi, L., Bienayme, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bouy, H., Bragaglia, A., Breddels, M. A., Brouillet, N., Bruesemeister, T., Bucciarelli, B., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Caffau, E., Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Costigan, G., Crifo, F., Cross, N. J. G., Crosta, M., Crowley, C., Dafonte, C., Damerdji, Y., Dapergolas, A., David, P., David, M., De Cat, P., De Felice, F., De Laverny, P., De Luise, F., De March, R., De Martino, D., De Souza, R., Debosscher, J., Del Pozo, E., Delbo, M., Delgado, A., Delgado, H. E., Di Matteo, P., Diakite, S., Distefano, E., Dolding, C., Dos Anjos, S., Drazinos, P., Duran, J., Dzigan, Y., Edvardsson, B., Enke, H., Evans, N. W., Bontemps, G. Eynard, Fabre, C., Fabrizio, M., Faigler, S., Falcao, A. J., Casas, M. Farras, Federici, L., Fedorets, G., Fernandez-Hernandez, J., Fernique, P., Fienga, A., Figueras, F., Filippi, F., Findeisen, K., Fonti, A., Fouesneau, M., Fraile, E., Fraser, M., Fuchs, J., Gai, M., Galleti, S., Galluccio, L., Garabato, D., Garcia-Sedano, F., Garofalo, A., Garralda, N., Gavras, P., Gerssen, J., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomes, M., Gonzalez-Marcos, A., Gonzalez-Nunez, J., Gonzalez-Vidal, J. J., Granvik, M., Guerrier, A., Guillout, P., Guiraud, J., Gurpide, A., Gutierrez-Sanchez, R., Guy, L. P., Haigron, R., Hatzidimitriou, D., Haywood, M., Heiter, U., Helmi, A., Hobbs, D., Hofmann, W., Holl, B., Holland, G., Hunt, J. A. S., Hypki, A., Icardi, V., Irwin, M., De Fombelle, G. Jevardat, Jofre, P., Jonker, P. G., Jorissen, A., Julbe, F., Karampelas, A., Kochoska, A., Kohley, R., Kolenberg, K., Kontizas, E., Koposov, S. E., Kordopatis, G., Koubsky, P., Krone-Martins, A., Kudryashova, M., Kull, I., Bachchan, R. K., Lacoste-Seris, F., Lanza, A. F., Lavigne, J. -B., Le Poncin-Lafitte, C., Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Lemaitre, V., Lenhardt, H., Leroux, F., Liao, S., Licata, E., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loeffler, W., Lopez, M., Lorenz, D., Macdonald, I., Fernandes, T. Magalhacs, Managau, S., Mann, R. G., Mantelet, G., Marchal, O., Marchant, J. M., Marconi, M., Marinoni, S., Marrese, P. M., Marschalko, G., Marshall, D. J., Martin-Fleitas, J. M., Martino, M., Mary, N., Matijevic, G., Mazeh, T., Mcmillan, P. J., Messina, S., Michalik, D., Millar, N. R., Miranda, B. M. H., Molina, D., Molinaro, R., Molinaro, M., Molnar, L., Moniez, M., Montegriffo, P., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morgenthaler, S., Morris, D., Mulone, A. F., Muraveva, T., Musella, I., Narbonne, J., Nelemans, G., Nicastro, L., Noval, L., Ordenovic, C., Ordieres-Mere, J., Osborne, P., Pagani, C., Pagano, I., Pailler, F., Palacin, H., Palaversa, L., Parsons, P., Pecoraro, M., Pedrosa, R., Pentikainen, H., Pichon, B., Piersimoni, A. M., Pincau, F. -X., Plachy, E., Plum, G., Poujoulet, E., Prsa, A., Pulone, L., Ragaini, S., Rago, S., Rambaux, N., Ramos-Lerate, M., Ranalli, P., Rauw, G., Read, A., Regibo, S., Reyle, C., Ribeiro, R. A., Rimoldini, L., Ripepi, V., Riva, A., Rixon, G., Roelens, M., Romero-Gomez, M., Rowell, N., Royer, F., Ruiz-Dern, L., Sadowski, G., Selles, T. Sagrista, Sahlmann, J., Salgado, J., Salguero, E., Sarasso, M., Savietto, H., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Segransan, D., Shih, I. -C., Smareglia, R., Smart, R. L., Solano, E., Solitro, F., Sordo, R., Nieto, S. Soria, Souchay, J., Spagna, A., Spoto, F., Stampa, U., Steele, I. A., Steidelmueller, H., Stephenson, C. A., Stoev, H., Suess, F. F., Suveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Tapiador, D., Taris, F., Tauran, G., Taylor, M. B., Teixeira, R., Terrett, D., Tingley, B., Trager, S. C., Turon, C., Ulla, A., Utrilla, E., Valentini, G., Van Elteren, A., Van Hemelryck, E., Van Leeuwen, M., Varadi, M., Vecchiato, A., Veljanoski, J., Via, T., Vicente, D., Vogt, S., Voss, H., Votruba, V., Voutsinas, S., Walmsley, G., Weiler, M., Weingrill, K., Wevers, T., Wyrzykowski, L., Yoldas, A., Zerjal, M., Zucker, S., Zurbach, C., Zwitter, T., Alecu, A., Allen, M., Prieto, C. Allende, Amorim, A., Anglada-Escude, G., Arsenijevic, V., Azaz, S., Balm, P., Beck, M., Bernstein, H. -H., Bigot, L., Bijaoui, A., Blasco, C., Bonfigli, M., Bono, G., Boudreault, S., Bressan, A., Brown, S., Brunet, P. -M., Bunclark, P., Buonanno, R., Butkevich, A. G., Carret, C., Carrion, C., Chemin, L., Chereau, F., Corcione, L., Darmigny, E., De Boer, K. S., De Teodoro, P., De Zeeuw, P. T., Delle Luche, C., Domingues, C. D., Dubath, P., Fodor, F., Frezouls, B., Fries, A., Fustes, D., Fyfe, D., Gallardo, E., Gallegos, J., Gardiol, D., Gebran, M., Gomboc, A., Gomez, A., Grux, E., Gueguen, A., Heyrovsky, A., Hoar, J., Iannicola, G., Parache, Y. Isasi, Janotto, A. -M., Joliet, E., Jonckheere, A., Keil, R., Kim, D. -W., Klagyivik, P., Klar, J., Knude, J., Kochukhov, O., Kolka, I., Kos, J., Kutka, A., Lainey, V., Lebouquin, D., Liu, C., Loreggia, D., Makarov, V. V., Marseille, M. G., Martayan, C., Martinez-Rubi, O., Massart, B., Meynadier, F., Mignot, S., Munari, U., Nguyen, A. -T., Nordlander, T., Ocvirk, P., O'Flaherty, K. S., Sanz, A. Olias, Ortiz, P., Osorio, J., Oszkiewicz, D., Ouzounis, A., Palmer, M., Park, P., Pasquato, E., Peltzer, C., Peralta, J., Peturaud, F., Pieniluoma, T., Pigozzi, E., Poels, J., Prat, G., Prod'Homme, T., Raison, F., Rebordao, J. M., Risquez, D., Rocca-Volmerange, B., Rosen, S., Ruiz-Fuertes, M. I., Russo, F., Sembay, S., Vizcaino, I. Serraller, Short, A., Siebert, A., Silva, H., Sinachopoulos, D., Slezak, E., Soffel, M., Sosnowska, D., Straizys, V., Ter Linden, M., Terrell, D., Theil, S., Tiede, C., Troisi, L., Tsalmantza, P., Tur, D., Vaccari, M., Vachier, F., Valles, P., Van Hamme, W., Veltz, L., Virtanen, J., Wallut, J. -M., Wichmann, R., Wilkinson, M. I., Ziaeepour, H., Zschocke, S., and Gaia Collaboration
Subjects: proper motions, surveys, parallaxes, astrometry, catalogs, Catalogs, astrometry, parallaxes, proper motions, surveys, QB
Abstract: At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims: A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods: The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results: Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues - a realisation of the Tycho-Gaia Astrometric Solution (TGAS) - and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of 3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr-1 for the proper motions. A systematic component of 0.3 mas should be added to the parallax uncertainties. For the subset of 94 000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr-1. For the secondary astrometric data set, the typical uncertainty of the positions is 10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to 0.03 mag over the magnitude range 5 to 20.7. Conclusions: Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data. 23 pages, 13 figures. This paper has 591 co-authors. Accepted by A&A in the Gaia Special Feature. The Gaia data, documentation and access facilities can be found at http://archives.esac.esa.int/gaia/ ispartof: Astronomy & Astrophysics vol:595 status: published
Published: 2016

12. The Gaia mission

Author: Prusti, T., de Bruijne, J. H. J., Brown, A. G. A., Vallenari, A., Babusiaux, C., Bailer-Jones, C. A. L., Bastian, U., Biermann, M., Evans, D. W., Eyer, L., Jansen, F., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Mignard, F., Milligan, D. J., Panem, C., Poinsignon, V., Pourbaix, D., Randich, S., Sarri, G., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., van Leeuwen, F., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Drimmel, R., Hog, E., Katz, D., Lattanzi, M. G., O'Mullane, W., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Bramante, L., Cacciari, C., Castaneda, J., Chaoul, L., Cheek, N., De Angeli, F., Fabricius, C., Guerra, R., Hernandez, J., Jean-Antoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordonez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thevenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Clementini, G., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., De Ridder, J., de Torres, A., Delchambre, L., Dell'Oro, A., Ducourant, C., Fremat, Y., Garcia-Torres, M., Gosset, E., Halbwachs, J. -L, Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, Andreas J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M, Recio-Blanco, A., Robin, A. C., Sarro, L. M., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., van Reeven, W., Viala, Y., Abbas, U., Aramburu, A. Abreu, Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Alvarez, M. A., Alves, J., Anderson, R. I., Andrei, A. H., Varela, E. Anglada, Antiche, E., Antoja, T., Anton, S., Arcay, B., Atzei, A., Ayache, L., Bach, N., Baker, S. G., Balaguer-Nunez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Baroni, M., Barrado y Navascues, D., Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., Bellei, G., Garcia, A. Bello, Belokurov, V., Bendjoya, P., Berihuete, A., Bianchi, L., Bienayme, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bouy, H., Bragaglia, A., Breddels, M. A., Brouillet, N., Bruesemeister, T., Bucciarelli, B., Budnik, F., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Au, E. Ca Ff, Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Charvet, P., Chassat, F., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Collins, P., Costigan, G., Crifo, F., Cross, N. J. 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Abstract: Gaia is a cornerstone mission in the science programme of the European Space Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.
Published: 2016
Full Text: View/download PDF

13. An improved way for evaluating competences: A different approach to project management learning

Author: Crespo, E., primary, Gonzalez-Marcos, A., additional, Alba-Elias, F., additional, Ordieres-Mere, J., additional, and Castejon-Limas, M., additional
Published: 2011
Full Text: View/download PDF

14. Learning by doing in project management: Acquiring skills through a collaborative model

Author: Cobo-Benita, J. R., primary, Ordieres-Mere, J., additional, Ortiz-Marcos, I., additional, and Pacios-Alvarez, A., additional
Published: 2010
Full Text: View/download PDF

15. The computer in a roofing slate quarry

Author: Taboada Castro, J., García García, M. M., González Nicieza, C., and Ordieres Mere, J.
Subjects: Pizarras, Galice (Spain), Mining exploitation, Ordenador personal, Personal computer, Slates, Explotación minera, Galicia
Abstract: [Resumen] Sencillas configuraciones informáticas en base a ordenadores personales con programas comerciales de modelización y diseño asistido por ordenador, facilitan el reiterativo trabajo de planificación minera de una explotación de pizarra a cielo abierto. Su utilización es aplicable a tres fases del proyecto minero: Modelización del yacimiento (investigación), proyecto de explotación (viabilidad) y labores mineras (ejecución). [Abstract] Simple integrated systems based on personal computers and cornmercial programmes about modelling and CAD, make easy the reiterative work on exploitation planning in roofing slate quarries. We can use this systems in the three phases of the mining project: bed modelling (Research), exploitation (Viability)wand mining (Performance).
Published: 1993

16. An improved way for evaluating competences: A different approach to project management learning.

Author: Crespo, E., Gonzalez-Marcos, A., Alba-Elias, F., Ordieres-Mere, J., and Castejon-Limas, M.
Published: 2011
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17. Learning by doing in project management: Acquiring skills through a collaborative model.

Author: Cobo-Benita, J.R., Ordieres-Mere?, J., Ortiz-Marcos, I., and Pacios-A?lvarez, A.
Published: 2010
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18. Parameter optimization of influence functions in mining subsidence

Author: Bello Garci´a, A., primary and Ordieres Mere, J., additional
Published: 1997
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19. VizieR Online Data Catalog: Gaia DR1 open cluster members (Gaia Collaboration+, 2017)

Author: Gaia Collaboration, van Leeuwen F., Vallenari, A., Jordi, C., Lindegren, L., Bastian, U., Prusti, T., Bruijne, J. H. J., Brown, A. G. A., Babusiaux, C., Bailer-Jones, C. A. L., Biermann, M., Evans, D. W., Eyer, L., Jansen, F., Klioner, S. A., Lammers, U., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Siddiqui, H. I., Soubiran, C., Valette, V., Walton, N. A., Aerts, C., Arenou, F., Cropper, M., Drimmel, R., Hog, E., Katz, D., Lattanzi, M. G., O Mullane, W., Grebel, E. K., Holland, A. D., Huc, C., Passot, X., Perryman, M., Bramante, L., Cacciari, C., Castaneda, J., Chaoul, L., Cheek, N., Angeli, F., Fabricius, C., Guerra, R., Hernandez, J., Jean-Antoine-Piccolo, A., Masana, E., Messineo, R., Mowlavi, N., Nienartowicz, K., Ordonez-Blanco, D., Panuzzo, P., Portell, J., Richards, P. J., Riello, M., Seabroke, G. M., Tanga, P., Thevenin, F., Torra, J., Els, S. G., Gracia-Abril, G., Comoretto, G., Garcia-Reinaldos, M., Lock, T., Mercier, E., Altmann, M., Andrae, R., Astraatmadja, T. L., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Busso, G., Carry, B., Cellino, A., Clementini, G., Cowell, S., Creevey, O., Cuypers, J., Davidson, M., Ridder, J., Torres, A., Delchambre, L., Oro, A., Ducourant, C., Fremat, Y., Garcia-Torres, M., Gosset, E., Halbwachs, J. -L, Hambly, N. C., Harrison, D. L., Hauser, M., Hestroffer, D., Hodgkin, S. T., Huckle, H. E., Hutton, A., Jasniewicz, G., Jordan, S., Kontizas, M., Korn, A. J., Lanzafame, A. C., Manteiga, M., Moitinho, A., Muinonen, K., Osinde, J., Pancino, E., Pauwels, T., Petit, J. -M, Recio-Blanco, A., Robin, A. C., Sarro, L. M., Siopis, C., Smith, M., Smith, K. W., Sozzetti, A., Thuillot, W., Reeven, W., Viala, Y., Abbas, U., Abreu Aramburu, A., Accart, S., Aguado, J. J., Allan, P. M., Allasia, W., Altavilla, G., Alvarez, M. A., Joao Alves, Anderson, R. I., Andrei, A. H., Anglada Varela, E., Antiche, E., Antoja, T., Anton, S., Arcay, B., Bach, N., Baker, S. G., Balaguer-Nunez, L., Barache, C., Barata, C., Barbier, A., Barblan, F., Barrado Y Navascues, D., Barros, M., Barstow, M. A., Becciani, U., Bellazzini, M., Bello Garcia, A., Belokuro, V. V., Ben Djoya, P., Berihuete, A., Bianchi, L., Bienayme, O., Billebaud, F., Blagorodnova, N., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Borrachero, R., Bouquillon, S., Bourda, G., Bouy, H., Bragaglia, A., Breddels, M. A., Brouillet, N., Bruesemeister, T., Bucciarelli, B., Burgess, P., Burgon, R., Burlacu, A., Busonero, D., Buzzi, R., Caffau, E., Cambras, J., Campbell, H., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Castellani, M., Charlot, P., Charnas, J., Chiavassa, A., Clotet, M., Cocozza, G., Collins, R. S., Costigan, G., Crifo, F., Cross, N. J. G., Crosta, M., Crowley, C., Dafonte, C., Damerdji, Y., Dapergolas, A., David, P., David, M., Cat, P., de, Felice F., Laverny, P., Luise, F., March, R., Martino, D., Souza, R., Debosscher, J., Del Pozo, E., Delbo, M., Delgado, A., Delgado, H. E., Di Matteo, P., Diakite, S., Distefano, E., Dolding, C., Dos Anjos, S., Drazinos, P., Duran, J., Dzigan, Y., Edvardsson, B., Enke, H., Evans, N. W., Eynard Bontemps, G., Fabre, C., Fabrizio, M., Faigler, S., Falcao, A. J., Farras Casas, M., Fede Rici, L., Fedorets, G., Fernandez-Hernandez, J., Fernique, P., Fienga, A., Figueras, F., Filippi, F., Findeisen, K., Fonti, A., Fouesneau, M., Fraile, E., Fraser, M., Fuchs, J., Gai, M., Galleti, S., Galluccio, L., Garabato, D., Garcia-Sedano, F., Garofalo, A., Garralda, N., Gavras, P., Gerssen, J., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomes, M., Gonzalez-Marcos, A., Gonzalez-Nunez, J., Gonzalez-Vidal, J. J., Granvik, M., Guerrier, A., Guillout, P., Guiraud, J., Gurpide, A., Gutierrez-Sanchez, R., Guy, L. P., Haigron, R., Hatzidimitriou, D., Haywood, M., Heiter, U., Helmi, A., Hobbs, D., Hofmann, W., Holl, B., Holland, G., Hunt, J. A. S., Hypki, A., Icardi, V., Irwin, M., Jevardat Fombelle, G., Jofre, P., Jonker, P. G., Jorissen, A., Julbe, F., Karampelas, A., Kochoska, A., Kohley, R., Kolenberg, K., Kontizas, E., Koposov, S. E., Kordopatis, G., Koubsky, P., Krone-Martins, A., Kudryashova, M., Kull, I., Bachchan, R. K., Lacoste-Seris, F., Lanza, A. F., Lavigne, J. -B, Le Poncin-Lafitte, C., Lebreton, Y., Lebzelter, T., Leccia, S., Lecler, C. N., Lec Oeur-Taibi, I., Lemaitre, V., Lenhardt, H., Leroux, F., Liao, S., Licata, E., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loeffler, W., Lopez, M., Lorenz, D., Macdonald, I., Magalhaes Fernandes, T., Managau, S., Mann, R. G., Mantelet, G., Marchal, O., Marchant, J. M., Marconi, M., Marinoni, S., Marrese, P. M., Marschalko, G., Marshall, D. J., Martin-Fleitas, J. M., Martino, M., Mary, N., Matijevic, G., Mazeh, T., Mcmillan, P. J., Messina, S., Michalik, D., Millar, N. R., Miranda, B. M. H., Molina, D., Molinaro, R., Molinaro, M., Molnar, L., Moniez, M., Montegriffo, P., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morgenthaler, S., Morris, D., Mulone, A. F., Muraveva, T., Musella, I., Narbonne, J., Nelemans, G., Nicastro, L., Noval, L., Ordenovic, C., Ordieres-Mere, J., Osborne, P., Pagani, C., Pagano, I., Pailler, F., Palacin, H., Palaversa, L., Parsons, P., Pecoraro, M., Pedrosa, R., Pentikaeinen, H., Pichon, B., Piersimoni, A. M., Pineau, F. -X, Plachy, E., Plum, G., Poujoulet, E., Prsa, A., Pulone, L., Ragaini, S., Rago, S., Rambaux, N., Ramos-Lerate, M., Ranalli, P., Rauw, G., Read, A., Regibo, S., Reyle, C., Ribeiro, R. A., Rimoldini, L., Ripepi, V., Riva, A., Rixon, G., Roelens, M., Romero-Gomez, M., Rowell, N., Royer, F., Ruiz-Dern, L., Sadowski, G., Sagrista Selles, T., Sahlmann, J., Salgado, J., Salguero, E., Sarasso, M., Savietto, H., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Segransan, D., Shih, I. -C, Smareglia, R., Smart, R. L., Solano, E., Solitro, F., Sordo, R., Soria Nieto, S., Souchay, J., Spagna, A., Spoto, F., Stampa, U., Steele, I. A., Steidelmueller, H., Stephenson, C. A., Stoev, H., Suess, F. F., Sueveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Tapiador, D., Taris, F., Tauran, G., Taylor, M. B., Teixeira, R., Terrett, D., Tingley, B., Trager, S. C., Turon, C., Ulla, A., Utrilla, E., Valentini, G., Elteren, A., Hemelryck, E., Vanleeuwen, M., Varadi, M., Vecchiato, A., Veljanoski, J., Via, T., Vicente, D., Vogt, S., Voss, H., Votruba, V., Voutsinas, S., Walmsley, G., Weiler, M., Weingrill, K., Wevers, T., Wyrzykowski, L., Yoldas, A., Zerjal, M., Zucker, S., Zurbach, C., Zwitter, T., Alecu, A., Allen, M., Allende Prieto, C., Amorim, A., Anglada-Escude, G., Arsenijevic, V., Azaz, S., Balm, P., Beck, M., Bernstein, H. -H, Bigot, L., Bijaoui, A., Blasco, C., Bonfigli, M., Bono, G., Boudreault, S., Bressan, A., Brown, S., Brunet, P. -M, Bunclark, P., Buonanno, R., Butkevich, A. G., Carret, C., Carrion, C., Chemin, L., Chereau, F., Corcione, L., Darmigny, E., Boer, K. S., Teodoro, P., Zeeuw, P. T., Delle Luche, C., Domingues, C. D., Dubath, P., Fodor, F., Frezouls, B., Fries, A., Fustes, D., Fyfe, D., Gallardo, E., Gallegos, J., Gardiol, D., Gebran, M., Gomboc, A., Gomez, A., Grux, E., Gueguen, A., Heyrovsky, A., Hoar, J., Iannicola, G., Isasi Parache, Y., Janotto, A. -M, Joliet, E., Jonckheere, A., Keil, R., Kim, D. -W, Klagyivik, P., Klar, J., Knude, J., Kochukhov, O., Kolka, I., Kos, J., Kutka, A., Lainey, V., Lebouquin, D., Liu, C., Loreggia, D., Makarov, V. V., Marseille, M. G., Martayan, C., Martinez-Rubi, O., Massart, B., Meynadier, F., Mignot, S., Munari, U., Nguyen, A. -T, Nordlander, T., O Flaherty, K. S., Ocvirk, P., O Lias Sanz, A., Ortiz, P., Osorio, J., Oszkiewicz, D., Ouzounis, A., Palmer, M., Park, P., Pasquato, E., Peltzer, C., Peralta, J., Peturaud, F., Pieniluoma, T., Pigozzi, E., Poels, J., Prat, G., Prod Homme, T., Raison, F., Rebordao, J. M., Risquez, D., Rocca-Volmerange, B., Rosen, S., Ruiz-Fuertes, M. I., Russo, F., Sembay, S., Serraller Vizcaino, I., Short, A., Siebert, A., Silva, H., Sinachopoulos, D., Slezak, E., Soffel, M., Sosnowska, D., Straizys, V., Ter Linden, M., Terrell, D., Theil, S., Tiede, C., Troisi, L., Tsalmantza, P., Tur, D., Vaccari, M., Vachier, F., Valles, P., Hamme, W., Veltz, L., Virtanen, J., Wallut, J. -M, Wichmann, R., Wilkinson, M. I., Ziaeepour, H., and Zschocke, S.

20. Parameter optimisation of influence functions in mining subsidence.

Author: Bello Garcia A., Ordieres Mere J., Bello Garcia A., and Ordieres Mere J.
Abstract: Subsidence can be predicted empirically, by theoretical modelling or by using functions which describe the influence exerted at the surface by infinitesimal elements of an extraction area. Functional methods are currently the most reliable and practical, and can be used for complex mine geometry. A program has been developed with an automatic optimisation technique, well adapted to the incoming data, which allows the dynamic addition of new control points in order to improve prediction. The model also allows application of the time factor. It can be used to improve the design of workings in order to reduce damage at the surface, while client-server implementation of the evolutionary algorithm reduces the time needed to solve the problem and thus allows better tracking of the phenomenon., Subsidence can be predicted empirically, by theoretical modelling or by using functions which describe the influence exerted at the surface by infinitesimal elements of an extraction area. Functional methods are currently the most reliable and practical, and can be used for complex mine geometry. A program has been developed with an automatic optimisation technique, well adapted to the incoming data, which allows the dynamic addition of new control points in order to improve prediction. The model also allows application of the time factor. It can be used to improve the design of workings in order to reduce damage at the surface, while client-server implementation of the evolutionary algorithm reduces the time needed to solve the problem and thus allows better tracking of the phenomenon.

21. El ordenador en una explotación a cielo abierto de pizarra de techar

Author: Taboada Castro, J., García García, M. M., González Nicieza, C., Ordieres Mere, J., Taboada Castro, J., García García, M. M., González Nicieza, C., and Ordieres Mere, J.
Abstract: [Resumen] Sencillas configuraciones informáticas en base a ordenadores personales con programas comerciales de modelización y diseño asistido por ordenador, facilitan el reiterativo trabajo de planificación minera de una explotación de pizarra a cielo abierto. Su utilización es aplicable a tres fases del proyecto minero: Modelización del yacimiento (investigación), proyecto de explotación (viabilidad) y labores mineras (ejecución)., [Abstract] Simple integrated systems based on personal computers and cornmercial programmes about modelling and CAD, make easy the reiterative work on exploitation planning in roofing slate quarries. We can use this systems in the three phases of the mining project: bed modelling (Research), exploitation (Viability)wand mining (Performance).

22. Operational information system in a power plant

Author: Ordieres Mere, J., primary, Ortega, F., additional, Bello, A., additional, Menendez, C., additional, and Vallina, V., additional
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23. Operational information system in a power plant.

Author: Ordieres Mere, J., Ortega, F., Bello, A., Menendez, C., and Vallina, V.
Published: 1997
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24. The Use of the Internet of Things for Estimating Personal Pollution Exposure.

Author: Arano KAG, Sun S, Ordieres-Mere J, and Gong AB
Subjects: Cities, Decision Support Techniques, Environmental Monitoring methods, Humans, Proof of Concept Study, Spain, Air Pollution analysis, Environmental Exposure, Internet of Things
Abstract: This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.
Published: 2019
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24 results on '"Ordieres-Mere, J."'

1. Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs

2. The CARMENES search for exoplanets around M dwarfs -- A deep transfer learning method to determine Teff and [M/H] of target stars

3. J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology

4. Metallicities in M dwarfs: Investigating different determination techniques

5. The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars

6. Gaia Data Release 1. Testing the parallaxes with local Cepheids and RR Lyrae stars

7. Gaia Data Release 1. Open cluster astrometry: performance, limitations, and future prospects

8. Program and Project Management Articulation: Evidences from the Infrastructure Sector

9. Finite element analysis of the hyper-elastic contact problem in automotive door sealing

10. Testing parallaxes with local Cepheids and RR Lyrae stars

11. Gaia Data Release 1 Summary of the astrometric, photometric, and survey properties

12. The Gaia mission

13. An improved way for evaluating competences: A different approach to project management learning

14. Learning by doing in project management: Acquiring skills through a collaborative model

15. The computer in a roofing slate quarry

16. An improved way for evaluating competences: A different approach to project management learning.

17. Learning by doing in project management: Acquiring skills through a collaborative model.

18. Parameter optimization of influence functions in mining subsidence

19. VizieR Online Data Catalog: Gaia DR1 open cluster members (Gaia Collaboration+, 2017)

20. Parameter optimisation of influence functions in mining subsidence.

21. El ordenador en una explotación a cielo abierto de pizarra de techar

22. Operational information system in a power plant

23. Operational information system in a power plant.

24. The Use of the Internet of Things for Estimating Personal Pollution Exposure.

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