1,702 research outputs found

    First CO(17-16) emission line detected in a z > 6 quasar

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    We report the serendipitous detection of the CO(17-16) emission line toward the quasar SDSSJ114816.64+525150.3 (J1148) at redshift z = 6.4 obtained with the Plateau de Bure Interferometer. The CO(17-16) line is possibly contaminated by OH+ emission, that may account for ~ 35 - 60% of the total flux observed. Photo-Dissociation and X-ray Dominated Regions (PDRs and XDRs) models show that PDRs alone cannot reproduce the high luminosity of the CO(17-16) line relative to low-J CO transitions and that XDRs are required. By adopting a composite PDR+XDR model we derive molecular cloud and radiation field properties in the nuclear region of J1148. Our results show that highly excited CO lines represent a sensitive and possibly unique tool to infer the presence of X-ray faint or obscured supermassive black hole progenitors in high-z galaxies.Comment: 6 pages, 4 figures, accepted for publication in MNRAS Lette

    CO Emission from z>3 Radio Galaxies

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    We report on the detection of the CO(4-3) line with the IRAM Plateau de Bure Interferometer in two z>3 radio galaxies, doubling the number of successful detections in such objects. A comparison of the CO and Ly-alpha velocity profiles indicates that in at least half of the cases, the CO is coincident in velocity with associated HI absorption seen against the Ly-alpha emission. This strongly suggests that the CO and HI originate from the same gas reservoir, and could explain the observed redshift differences between the optical narrow emission lines and the CO. The CO emission traces a mass of H_2 100-1000 times larger than the HI and HII mass traced by Ly-alpha, providing sufficient gas to supply the massive starbursts suggested by their strong thermal dust emission.Comment: 6 Pages, including 3 PostScript figures. To appear in the proceedings of the conference "Radio Galaxies: Past, present and future", Leiden, 11-15 Nov 2002, eds. M. Jarvis et a

    Scalability of Optical Interconnects Based on Microring Resonators

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    This letter investigates the use of optical microring resonators as switching elements (SEs) in large optical interconnection fabrics. We introduce a simple physical-layer model to assess scalability in crossbar- and Benes-based architectures.We also propose a new dilated SE that improves scalability to build fabrics of several terabits per second of aggregate capacit

    Optical Interconnection Networks Based on Microring Resonators

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    Abstract — Interconnection networks must transport an always increasing information density and connect a rising number of processing units. Electronic technologies have been able to sustain the traffic growth rate, but are getting close to their physical limits. In this context, optical interconnection networks are becoming progressively more attractive, especially because new photonic devices can be directly integrated in CMOS technology. Indeed, interest in microring resonators as switching components is rising, but their usability in full optical interconnection architectures is still limited by their physical characteristics. Indeed, differently from classical devices used for switching, switching elements based on microring resonators exhibit asymmetric power losses depending on the output ports input signals are directed to. In this paper, we study classical interconnection architectures such as crossbar, Benes and Clos networks exploiting microring resonators as building blocks. Since classical interconnection networks lack either scalability or complexity, we propose two new architectures to improve performance of microring based interconnection networks while keeping a reasonable complexity. I

    Constraining the abundances of complex organics in the inner regions of solar-type protostars

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    The high abundances of Complex Organic Molecules (COMs) with respect to methanol, the most abundant COM, detected towards low-mass protostars, tend to be underpredicted by astrochemical models. This discrepancy might come from the large beam of the single-dish telescopes, encompassing several components of the studied protostar, commonly used to detect COMs. To address this issue, we have carried out multi-line observations of methanol and several COMs towards the two low-mass protostars NGC1333-IRAS2A and -IRAS4A with the Plateau de Bure interferometer at an angular resolution of 2 arcsec, resulting in the first multi-line detection of the O-bearing species glycolaldehyde and ethanol and of the N-bearing species ethyl cyanide towards low-mass protostars other than IRAS 16293. The high number of detected transitions from COMs (more than 40 methanol transitions for instance) allowed us to accurately derive the source size of their emission and the COMs column densities. The COMs abundances with respect to methanol derived towards IRAS2A and IRAS4A are slightly, but not substantitally, lower than those derived from previous single-dish observations. The COMs abundance ratios do not vary significantly with the protostellar luminosity, over five orders of magnitude, implying that low-mass hot corinos are quite chemically rich as high-mass hot cores. Astrochemical models still underpredict the abundances of key COMs, such as methyl formate or di-methyl ether, suggesting that our understanding of their formation remains incomplete.Comment: 60 pages, 10 figures, 17 tables. Accepted for publication in Ap

    Optical Interconnection Architectures based on Microring Resonators

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    Abstract: Microring resonators are an interesting device to build integrated optical interconnects, but their asymmetric loss behavior could limit the scalability of classical optical interconnects. We present new interconnects able to increase scalability with limited complexity

    X-ray spectroscopy of the z=6.4 quasar J1148+5251

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    We present the 78-ks Chandra observations of the z=6.4z=6.4 quasar SDSS J1148+5251. The source is clearly detected in the energy range 0.3-7 keV with 42 counts (with a significance ≳9σ\gtrsim9\sigma). The X-ray spectrum is best-fitted by a power-law with photon index Γ=1.9\Gamma=1.9 absorbed by a gas column density of NH=2.0−1.5+2.0×1023 cm−2\rm N_{\rm H}=2.0^{+2.0}_{-1.5}\times10^{23}\,\rm cm^{-2}. We measure an intrinsic luminosity at 2-10 keV and 10-40 keV equal to ∌1.5×1045 erg s−1\sim 1.5\times 10^{45}~\rm erg~s^{-1}, comparable with luminous local and intermediate-redshift quasar properties. Moreover, the X-ray to optical power-law slope value (αOX=−1.76±0.14\alpha_{\rm OX}=-1.76\pm 0.14) of J1148 is consistent with the one found in quasars with similar rest-frame 2500 \AA ~luminosity (L2500∌1032 erg s−1L_{\rm 2500}\sim 10^{32}~\rm erg~s^{-1}\AA−1^{-1}). Then we use Chandra data to test a physically motivated model that computes the intrinsic X-ray flux emitted by a quasar starting from the properties of the powering black hole and assuming that X-ray emission is attenuated by intervening, metal-rich (Z≄Z⊙Z\geq \rm Z_{\odot}) molecular clouds distributed on ∌\simkpc scales in the host galaxy. Our analysis favors a black hole mass MBH∌3×109M⊙M_{\rm BH} \sim 3\times 10^9 \rm M_\odot and a molecular hydrogen mass MH2∌2×1010M⊙M_{\rm H_2}\sim 2\times 10^{10} \rm M_\odot, in good agreement with estimates obtained from previous studies. We finally discuss strengths and limits of our analysis.Comment: 9 pages, 3 figures, 1 table, MNRAS in pres

    Implementazione di una Rete Neurale Convoluzionale con Dataset Custom per la Classificazione di Veicoli

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    Negli ultimi anni numerose aziende leader nel settore ICT, come Google, Apple e Amazon, hanno investito nella ricerca e sviluppo di tecniche di Machine Learning. Le Reti Neurali Convoluzionali sono particolari algoritmi di Machine Learning utili nel riconoscimento e classificazione delle immagini. L'elaborato inizia con una breve introduzione al Machine Learning e alle principali tecniche utilizzate per creare gli algoritmi, per poi proseguire con l'implementazione di una Rete Neurale Convoluzionale addestrata al riconoscimento di veicoli. L'elaborato si concentra sul confronto di prestazioni tra vari algoritmi e sull'utilizzo di Dataset custom e Dataset pre costruiti
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