Explorations of the viability of ARM and Xeon Phi for physics processing

We report on our investigations into the viability of the ARM processor and the Intel Xeon Phi co-processor for scientific computing. We describe our experience porting software to these processors and running benchmarks using real physics applications to explore the potential of these processors for production physics processing.Comment: Submitted to proceedings of the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP13), Amsterda

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

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

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

A study of CP violation in B-+/- -> DK +/- and B-+/- -> D pi(+/-) decays with D -> (KSK +/-)-K-0 pi(-/+) final states

A first study of CP violation in the decay modes $B^\pm\to [K^0_{\rm S} K^\pm \pi^\mp]_D h^\pm$ and $B^\pm\to [K^0_{\rm S} K^\mp \pi^\pm]_D h^\pm$, where $h$ labels a $K$ or $\pi$ meson and $D$ labels a $D^0$ or $\overline{D}^0$ meson, is performed. The analysis uses the LHCb data set collected in $pp$ collisions, corresponding to an integrated luminosity of 3 fb$^{-1}$. The analysis is sensitive to the CP-violating CKM phase $\gamma$ through seven observables: one charge asymmetry in each of the four modes and three ratios of the charge-integrated yields. The results are consistent with measurements of $\gamma$ using other decay modes

Measurement of Upsilon production in collisions at root s=2.76 TeV

The production of $\Upsilon(1S)$, $\Upsilon(2S)$ and $\Upsilon(3S)$ mesons decaying into the dimuon final state is studied with the LHCb detector using a data sample corresponding to an integrated luminosity of 3.3 $pb^{-1}$ collected in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=2.76$ TeV. The differential production cross-sections times dimuon branching fractions are measured as functions of the $\Upsilon$ transverse momentum and rapidity, over the ranges $p_{\rm T} Upsilon(1S) X) x B(Upsilon(1S) -> mu+mu-) = 1.111 +/- 0.043 +/- 0.044 nb, sigma(pp -> Upsilon(2S) X) x B(Upsilon(2S) -> mu+mu-) = 0.264 +/- 0.023 +/- 0.011 nb, sigma(pp -> Upsilon(3S) X) x B(Upsilon(3S) -> mu+mu-) = 0.159 +/- 0.020 +/- 0.007 nb, where the first uncertainty is statistical and the second systematic

Deep Neural Networks for energy reconstruction of Inverse Beta Decay events in JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) is a scintillation detector, currently under construction, which aims to solve the neutrino mass hierarchy by measuring reactor electron antineutrino energy spectrum with a a resolution of 3%/sqrt(E(MeV)) – the highest ever achieved in a large mass neutrino detector. Several approaches for energy reconstruction are being evaluated on simulated data, and Deep Learning methods have already shown promising results, both in accuracy and in efficiency. In this work, a new Convolutional Neural Network with a rotational invariant architecture is trained on a small dataset of 160k instances, and is fine-tuned to exploit the detector’s spherical symmetry and make use of position and timing data from individual photomultipliers. This approach proves to be insensitive to the presence of dark noise from thermal fluctuations, leading to a (2.45+-0.03)% visual energy resolution at 2 MeV, only slightly higher than the 2.2% expected from theory, with a reconstruction bias well below 1%. However, a simpler Fully Connected Neural Network, replicated from previous work, which uses only integral data and is trained on a larger dataset (750k instances), leads to a slightly better resolution of (2.26+-0.05)% at 2 MeV, while being more sensitive to added noise – proving that there could still be some margin of improvement for more complex methods

A first principle model of Free Recall

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Large-scale DAQ tests for the LHCb upgrade

The Data Acquisition (DAQ) of the LHCb experiment[1] will be upgraded in 2020 to a high-bandwidth trigger-less readout system. In the new DAQ event fragments will be forwarded to the to the Event Builder (EB) computing farm at 40 MHz. Therefore the front-end boards will be connected directly to the EB farm through optical links and PCI Express based interface cards. The EB is requested to provide a total network capacity of 32Tb/s, exploiting about 500 nodes. In order to get the required network capacity we are testing various technology and network protocols on large scale clusters. We developed on this purpose an Event Builder implementation designed for an InfiniBand interconnect infrastructure. We present the results of the measurements performed to evaluate throughput and scalability measurements on HPC scale facilities

Trigger and Data Acquisition System (TriDAS) for KM3NeT-Italy

<p>TriDAS is a software which implements the Trigger and Data Acquisition system for the KM3NeT- Italy underwater neutrino telescope. The detector is based on ”all data to shore” approach in order to reduce the complexity of the submarine hardware. At the shore station the TriDAS collects, processes and filters all the data coming from the detector, storing triggered events to a permanent storage for subsequent analysis.</p

Endoscopic dilation in pediatric esophageal strictures: a literature review

Esophageal strictures in pediatric age are a quite common condition due to different etiologies. Esophageal strictures can be divided in congenital, acquired and functional. Clinical manifestations are similar and when symptoms arise, endoscopic dilation is the treatment of choice. Our aim was to consider the efficacy of this technique in pediatric population, through a wide review of the literature