23 research outputs found

    LAMPSS: Discovery of Metal-Poor Stars in the Galactic Halo with the CaHK filter on CFHT MegaCam

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    We present the Lancaster Astrophysics Metal Poor Star Search (LAMPSS) in the Milky Way halo, conducted using the metallicity-sensitive Ca H & K lines. We use the CaHK filter of the MegaPrime/MegaCam on the Canada-France-Hawaii Telescope (CFHT) to survey an area of 1.010deg2 over the COSMOS field. We combine our new CaHK data with broadband optical and near-infrared photometry of COSMOS to recover stars down to mHK ‚ąľ 26. After removing galaxies, we find 70% completion and 30% contamination for our remaining 1772 stars. By exploring a range of spectral types and metallicities, we derive metallicity-sensitive colour-colour diagrams which we use to isolate different spectral types and metallicities (‚ąí5 ‚ȧ [Fe/H ‚ȧ 0). From these, we identify 16 potentially extremely metal-poor stars. One of which, LAMPS 1229, we predict to have [Fe/H] ‚ąľ ‚ąí5.0 at a distance of (78.21 ¬Ī 9.37) kpc. We construct a Metallicity Distribution Function for our sample of metal-poor stars, finding an expected sharp decrease in count as [Fe/H] decreases

    The DUNE Far Detector Vertical Drift Technology, Technical Design Report

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    International audienceDUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals

    Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

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    International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/cc charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1¬Ī0.6\pm0.6% and 84.1¬Ī0.6\pm0.6%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

    Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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    International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

    Highly-parallelized simulation of a pixelated LArTPC on a GPU