165 research outputs found

    High rate, fast timing Glass RPC for the high η\eta CMS muon detectors

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    The HL-LHC phase is designed to increase by an order of magnitude the amount of data to be collected by the LHC experiments. To achieve this goal in a reasonable time scale the instantaneous luminosity would also increase by an order of magnitude up to 6⋅10346 \cdot 10^{34} cm−2^{-2}s−1^{-1}. The region of the forward muon spectrometer (∣η∣>1.6|\eta| > 1.6) is not equipped with RPC stations. The increase of the expected particles rate up to 2 kHz/cm2^2 ( including a safety factor 3 ) motivates the installation of RPC chambers to guarantee redundancy with the CSC chambers already present. The actual RPC technology of CMS cannot sustain the expected background level. A new generation Glass-RPC (GRPC) using low resistivity glass (LR) is proposed to equip at least the two most far away of the four high eta muon stations of CMS. The design of small size prototypes and the studies of their performances under high rate particles flux is presented.Comment: 5 pages, 5 figures, proceeding for the conference VCI 201

    Skiroc: A Front-end Chip to Read Out the Imaging Silicon-Tungsten Calorimeter for ILC

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    Integration and low-power consumption of the read-out ASIC for the International Linear Collider (ILC) 82-millionchannel W-Si calorimeter must reach an unprecedented level as it will be embedded inside the detector. Uniformity and dynamic range performance has to reach the accuracy to achieve calorimetric measurement. A first step towards this goal has been a 10,000-channel physics prototype of 18*18 cm which is currently in test beam in CERN. A new version of a full integrated read out chip (SKIROC) has been designed to equip the technologic prototype to be built for 2009. Based on the running physics prototype ASIC (FLC_PHY3), it embeds most of the required features expected for the final detector. The dynamic range has been improved from 500 to 2000 MIP. An auto-trigger capability has been added allowing built-in zero suppress. The number of channel has been doubled reaching 36 to fit smaller silicon pads and the lownoise charge preamplifier now accepts both AC and DC coupled detectors. After an exhaustive description, the measurement results of that new front-end chip will be presented. The results on the technological R&D concurrently conducted on the ultra-thin PCB hosting both the front-end electronic and the silicon detectors will also be described

    Salvage radiotherapy for patients with PSA relapse after radical prostatectomy: a single institution experience

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    <p>Abstract</p> <p>Background</p> <p>To assess the efficacy of salvage radiotherapy (RT) for persistent or rising PSA after radical prostatectomy and to determine prognostic factors identifying patients who may benefit from salvage RT.</p> <p>Methods</p> <p>Between 1990 and 2003, 59 patients underwent RT for PSA recurrence after radical prostatectomy. Patients received a median of 66 Gy to the prostate bed with 3D or 2D RT. The main end point was biochemical failure after salvage RT, defined as an increase of the serum PSA value >0.2 ng/ml confirmed by a second elevation.</p> <p>Results</p> <p>Median follow-up was 38 months. The 3-year and 5-year bDFS rates were 56.1% and 41.2% respectively. According to multivariate analysis, only preRT PSA ≄1 ng/ml was associated with biochemical relapse.</p> <p>Conclusion</p> <p>When delivered early, RT is an effective treatment after radical prostatectomy. Only preRT PSA ≄1 ng/ml predicted relapse.</p

    Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data

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    Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visible range but also in the UV. We present two simple artificial neural networks that allow for recognizing meteor signals in the Mini-EUSO data with high accuracy in terms of a binary classification problem. We expect that similar architectures can be effectively used for signal recognition in other fluorescence telescopes, regardless of the nature of the signal. Due to their simplicity, the networks can be implemented in onboard electronics of future orbital or balloon experiments.Comment: 15 page

    Potential of Core-Collapse Supernova Neutrino Detection at JUNO

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    JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

    Detection of the Diffuse Supernova Neutrino Background with JUNO

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    As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

    Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

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    Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30M⊙M_{\odot} for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
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