Browsing by Author "Roy, N."

Now showing 1 - 5 of 5
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    Discovery of Two Einstein Crosses from Massive Post-blue Nugget Galaxies at z > 1 in KiDS*
    (2020) Napolitano, N. R.; Li, R.; Spiniello, C.; Tortora, C.; Sergeyev, A.; D'Ago, G.; Guo, X.; Xie, L.; Radovich, M.; Roy, N.; Koopmans, L. V. E.; Kuijken, K.; Bilicki, M.; Erben, T.; Getman, F.; Heymans, C.; Hildebrandt, H.; Moya, C.; Shan, H. Y.; Vernardos, G.; Wright, A. H.
    We report the discovery of two Einstein Crosses (ECs) in the footprint of the Kilo-Degree Survey (KiDS): KIDS J232940-340922 and KIDS J122456+005048. Using integral field spectroscopy from the Multi Unit Spectroscopic Explorer at the Very Large Telescope, we confirm their gravitational-lens nature. In both cases, the four spectra of the source clearly show a prominence of absorption features, hence revealing an evolved stellar population with little star formation. The lensing model of the two systems, assuming a singular isothermal ellipsoid (SIE) with external shear, shows that: (1) the two crosses, located at redshift z = 0.38 and 0.24, have Einstein radius R-E = 5.2 kpc and 5.4 kpc, respectively; (2) their projected dark matter fractions inside the half effective radius are 0.60 and 0.56 (Chabrier initial mass function); (3) the sources are ultra-compact galaxies, R-e similar to 0.9 kpc (at redshift, z(s) = 1.59) and R-e similar to 0.5 kpc (z(s) = 1.10), respectively. These results are unaffected by the underlying mass density assumption. Due to size, blue color, and absorption-dominated spectra, corroborated by low specific star formation rates derived from optical-near-infrared spectral energy distribution fitting, we argue that the two lensed sources in these ECs are blue nuggets migrating toward their quenching phase.
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    Indoor scene recognition by a mobile robot through adaptive object detection
    (2013) Espinace Ronda, Pablo Andrés; Soto Arriaza, Álvaro Marcelo; Kollar, T.; Roy, N.
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    Indoor scene recognition through object detection
    (IEEE, 2010) Espinace Ronda, Pablo Andrés; Kollar, T.; Soto, A.; Roy, N.
    Scene recognition is a highly valuable perceptual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in performance for the case of indoor scenes. We believe that this can be explained by the high appearance variability of indoor environments. This stresses the need to include high-level semantic information in the recognition process. In this work we propose a new approach for indoor scene recognition based on a generative probabilistic hierarchical model that uses common objects as an intermediate semantic representation. Under this model, we use object classifiers to associate low-level visual features to objects, and at the same time, we use contextual relations to associate objects to scenes. As a further contribution, we improve the performance of current state-of-the-art category-level object classifiers by including geometrical information obtained from a 3D range sensor that facilitates the implementation of a focus of attention mechanism within a Monte Carlo sampling scheme. We test our approach using real data, showing significant advantages with respect to previous state-of-the-art methods.
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    Nature versus nurture : relic nature and environment of the most massive passive galaxies at z < 0.5
    (2020) Tortora, C.; Napolitano, N. R.; Radovich, M.; Spiniello, C.; Hunt, L.; Roy, N.; Moscardini, L.; Scognamiglio, D.; Spavone, M.; D'Ago, Giuseppe; Cavuoti, S.; Longo, G.; Bellagamba, F.; Maturi, M.; Roncarelli, M.
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    Neutrino physics with an opaque detector
    (2021) Cabrera, A.; Abusleme, A.; dos Anjos, J.; Bezerra, T. J. C.; Bongrand, M.; Bourgeois, C.; Breton, D.; Buck, C.; Busto, J.; Calvo, E.; Chauveau, E.; Chen, M.; Chimenti, P.; Dal Corso, F.; De Conto, G.; Dusini, S.; Fiorentini, G.; Martins, C. Frigerio; Givaudan, A.; Govoni, P.; Gramlich, B.; Grassi, M.; Han, Y.; Hartnell, J.; Hugon, C.; Jimenez, S.; de Kerret, H.; Le Neve, A.; Loaiza, P.; Maalmi, J.; Mantovani, F.; Manzanillas, L.; Marquet, C.; Martino, J.; Navas-Nicolas, D.; Nunokawa, H.; Obolensky, M.; Ochoa-Ricoux, J. P.; Ortona, G.; Palomares, C.; Pessina, F.; Pin, A.; Porter, J. C. C.; Pravikoff, M. S.; Roche, M.; Roskovec, B.; Roy, N.; Santos, C.; Schoppmann, S.; Serafini, A.; Simard, L.; Sisti, M.; Stanco, L.; Strati, V; Stutzmann, J-S; Suekane, F.; Verdugo, A.; Viaud, B.; Volpe, C.; Vrignon, C.; Wagner, S.; Yermia, F.
    In 1956 Reines & Cowan discovered the neutrino using a liquid scintillator detector. The neutrinos interacted with the scintillator, producing light that propagated across transparent volumes to surrounding photo-sensors. This approach has remained one of the most widespread and successful neutrino detection technologies used since. This article introduces a concept that breaks with the conventional paradigm of transparency by confining and collecting light near its creation point with an opaque scintillator and a dense array of optical fibres. This technique, called LiquidO, can provide high-resolution imaging to enable efficient identification of individual particles event-by-event. A natural affinity for adding dopants at high concentrations is provided by the use of an opaque medium. With these and other capabilities, the potential of our detector concept to unlock opportunities in neutrino physics is presented here, alongside the results of the first experimental validation.