66 research outputs found

    Sensitivity and pointing accuracy of the NEMO km3 telescope

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    In this paper we present the results of Monte Carlo simulation studies on the capability of the proposed NEMO km3 telescope to detect high-energy neutrinos. We calculated the detector sensitivity to muon neutrinos coming from a generic point-like source. We also simulated the lack of atmospheric muons in correspondence to the Moon disk in order to determine the detector angular resolution and to check the absolute pointing capability

    L'importanza dei neutrini cosmici di altissima energia

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    Presentiamo una breve ed elementare introduzione ai neutrini cosmici di altissime energia, esaminando le ragioni del loro interesse, le connessioni con i raggi cosmici e con i raggi gamma, e alcune plausibili direzioni di sviluppo del campo

    Observation of the Moon shadow with the ANTARES neutrino telescope

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    International audience; ANTARES is currently the largest neutrino telescope on the Northern Hemisphere. It consists of a three-dimensional array of PMTs deployed at a depth of 2475 m off-shore the Toulon cost in France. Operating since May 2008 in its full configuration, it aims to detect high-energy cosmic neutrinos. In the absence of an astrophysical standard cand le, a possible way to measure the angular resolution and the pointing accuracy for a neutrino telescope is to look at "Moon shadow" in the atmospheric muon flux. The analysis method and the first results of the observation of the Moon shadow with ANTARES will be presented and discussed. The data, analysed in this work,were collected between Jan 31st, 2007 and Dec 31th 2010, for a total live time of 814 days

    L'importanza dei neutrini cosmici di altissima energia

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    Presentiamo una breve ed elementare introduzione ai neutrini cosmici di altissime energia, esaminando le ragioni del loro interesse, le connessioni con i raggi cosmici e con i raggi gamma, e alcune plausibili direzioni di sviluppo del campo

    Photometric monitoring of Luminous Blue Variables

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    We present some preliminary results from our program of intensive near-infrared photometric monitoring ofa sample of confirmed and candidate Luminous Blue Variables (LBVs) conducted from 2008 to 2010. Clear long-term variability has been observed for Wray 17-96 and V481 Sct, with overall brightness variation greater than 1 mag in the J band. Other sources, such as LBV 1806-20 showed detectable variability with amplitudes of few tenths of a magnitude with a time-scale of about 60 days

    NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea

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    The NEutrinoMediterranean Observatory—Submarine Network 1 (NEMO-SN1) seafloor observatory is located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI) since 2006. EMSO will specifically address long-term monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards

    Architecture and performance of the KM3NeT front-end firmware

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    The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches
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