37 research outputs found

    Dermatitis herpetiforme

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    ResumenLa dermatitis herpetiforme es una enfermedad autoinmune ampollosa subepidérmica caracterizada por una erupción pápulo-vesiculosa pruriginosa asociada a depósitos granulares de IgA a nivel de la dermis papilar detectados por IFD. Esta enfermedad en piel se encuentra asociada a una enteropatía sensible al gluten la cual es generalmente asintomática.[Armand B, Ávila MY, Pierard GE, Arrese JE. Dermatitis herpetiforme. MedUNAB 2002; 5(14):75-82].Palabras clave: Dermatitis herpetiforme, IgA granular, dapsona, ampollas subepidérmicas

    First results from the AugerPrime Radio Detector

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    Update of the Offline Framework for AugerPrime

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    Event-by-event reconstruction of the shower maximum XmaxX_{\mathrm{max}} with the Surface Detector of the Pierre Auger Observatory using deep learning

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    Reconstruction of Events Recorded with the Water-Cherenkov and Scintillator Surface Detectors of the Pierre Auger Observatory

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    Status and performance of the underground muon detector of the Pierre Auger Observatory

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    The XY Scanner - A Versatile Method of the Absolute End-to-End Calibration of Fluorescence Detectors

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    Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

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    We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration

    Outreach activities at the Pierre Auger Observatory

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    The ultra-high-energy cosmic-ray sky above 32 EeV viewed from the Pierre Auger Observatory

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