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57 ta ish

Ish: Inference of the Mass Composition of Cosmic Rays with Energies from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mn>10</mml:mn><mml:mn>18.5</mml:mn></mml:msup></mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mn>20</mml:mn></mml:mrow></mml:msup><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>eV</mml:mi></mml:mrow></mml:math> Using the Pierre Auger Observatory and Deep Learning

  1. Monte Carlo simulations for the Pierre Auger Observatory using the VO auger grid resources

    Eva Santos, P. Abreu, M. Aglietta +97

    Maqola20213 iqtibos
    ABI

  2. THE COSMIC-RAY ENERGY SPECTRUM OBSERVED WITH THE SURFACE DETECTOR OF THE TELESCOPE ARRAY EXPERIMENT

    T. Abu-Zayyad, R. Aida, M. Allen +97

    Maqola20133 iqtibos
    ABI

  3. First Observation of the Greisen-Zatsepin-Kuzmin Suppression

    R. U. Abbasi, T. Abu‐Zayyad, M. Allen +57

    Maqola20083 iqtibos
    ABI

  4. Sarlavhasiz

    Boshqa3 iqtibos
    ABI

  5. Observation of high-energy neutrinos from the Galactic plane

    R. Abbasi, M. Ackermann, J. Adams +95

    Maqola20233 iqtibos
    ABI

  6. Testing hadronic-model predictions of depth of maximum of air-shower profiles and ground-particle signals using hybrid data of the Pierre Auger Observatory

    Adila Abdul Halim, P. Abreu, M. Aglietta +96

    MaqolaAstrophysics and Cosmic PhenomenaPhysical review. D/Physical review. D.20242 iqtibos
    ABI

  7. CORSIKA: A Monte Carlo code to simulate extensive air showers

    D. Heck, J. Knapp, J.N. Capdevielle +2

    Maqola19982 iqtibos
    ABI

  8. Implications of the cosmic ray spectrum for the mass composition at the highest energies

    D. Allard, Nicolás G. Busca, G. Decerprit +2

    Maqola20082 iqtibos
    ABI

  9. Trigger and aperture of the surface detector array of the Pierre Auger Observatory

    J. Abraham, P. Abreu, M. Aglietta +96

    Maqola20102 iqtibos
    ABI

  10. Measurement of the Depth of Maximum of Extensive Air Showers above<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mn>10</mml:mn><mml:mn>18</mml:mn></mml:msup><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>eV</mml:mi></mml:math>

    J. Abraham, P. Abreu, M. Aglietta +96

    Maqola20102 iqtibos
    ABI

  11. Measurement of the Proton-Air Cross Section at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo mathvariant="bold">=</mml:mo><mml:mn>57</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>with the Pierre Auger Observatory

    P. Abreu, M. Aglietta, E. J. Ahn +96

    Maqola20122 iqtibos
    ABI

  12. Telescope Array Experiment

    H. Kawai, S. Yoshida, H. Yoshii +89

    Maqola20082 iqtibos
    ABI

  13. A deep learning-based reconstruction of cosmic ray-induced air showers

    M. Erdmann, Jonas Glombitza, D. Walz

    Maqola20172 iqtibos
    ABI

  14. Inferences on mass composition and tests of hadronic interactions from 0.3 to 100 EeV using the water-Cherenkov detectors of the Pierre Auger Observatory

    A. Aab, P. Abreu, M. Aglietta +97

    Maqola20172 iqtibos
    ABI

  15. Air-Shower Reconstruction at the Pierre Auger Observatory based on Deep Learning

    Jonas Glombitza

    Maqola20192 iqtibos
    ABI

  16. Event reconstruction for KM3NeT/ORCA using convolutional neural networks

    S. Aiello, A. Albert, S. Alves Garre +97

    Maqola20202 iqtibos
    ABI

  17. Deep-learning based reconstruction of the shower maximum X <sub>max</sub> using the water-Cherenkov detectors of the Pierre Auger Observatory

    A. Aab, P. Abreu, M. Aglietta +97

    Maqola20212 iqtibos
    ABI

  18. A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory

    R. Abbasi, M. Ackermann, J. Adams +97

    Maqola20212 iqtibos
    ABI

  19. Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks

    A. Aab, P. Abreu, M. Aglietta +97

    Maqola20212 iqtibos
    ABI

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

    Jonas Glombitza, P. Abreu, M. Aglietta +97

    Maqola20212 iqtibos
    ABI

  21. Deep-learning based reconstruction of the shower maximum X$_{max}$ using the water-Cherenkov detectors of the Pierre Auger Observatory

    A. Aab, P. Abreu, M. Aglietta +97

    Maqola20212 iqtibos
    ABI

  22. A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

    Abbasi, R., Ackermann, M., Adams, J. +97

    Maqola20212 iqtibos
    ABI

  23. Mass Composition from 3 EeV to 100 EeV using the Depth of the Maximum of Air-Shower Profiles Estimated with Deep Learning using Surface Detector Data of the Pierre Auger Observatory

    Jonas Glombitza, Adila Abdul Halim, P. Abreu +97

    Maqola20232 iqtibos
    ABI

  24. The Time Evolution of the Surface Detector of the Pierre Auger Observatory

    Orazio Zapparrata, Adila Abdul Halim, P. Abreu +97

    Maqola20232 iqtibos
    ABI

  25. Depth of Maximum of Air-Shower Profiles above $10^{17.8}$eV Measured with the Fluorescence Detector of the Pierre Auger Observatory and Mass Composition Implications

    Thomas Fitoussi, Adila Abdul Halim, P. Abreu +97

    Maqola20232 iqtibos
    ABI