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  1. Buch ; Online: Jet Flavour Tagging for Future Colliders with Fast Simulation

    Bedeschi, Franco / Gouskos, Loukas / Selvaggi, Michele

    2022  

    Abstract: Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level ... ...

    Abstract Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype.
    Schlagwörter High Energy Physics - Experiment ; High Energy Physics - Phenomenology
    Thema/Rubrik (Code) 621
    Erscheinungsdatum 2022-02-07
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: ParticleNet

    Qu, Huilin / Gouskos, Loukas

    Jet Tagging via Particle Clouds

    2019  

    Abstract: How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such a ... ...

    Abstract How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.

    Comment: 11 pages, 4 figures; v3: updated to match the version published in PRD; Code available at https://github.com/hqucms/ParticleNet
    Schlagwörter High Energy Physics - Phenomenology ; Computer Science - Computer Vision and Pattern Recognition ; High Energy Physics - Experiment
    Erscheinungsdatum 2019-02-22
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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