.zenodo.json

{
    "access_right": "open",
    "communities": [
        {
            "identifier": "escape2020"
        }
    ],
    "contributors": [
        {
            "affiliation": "Facultad de Ingenier\u00eda Inform\u00e1tica, Universidad Complutense de Madrid, Madrid, Spain",
            "name": "Sevilla, Jaime",
            "type": "Researcher"
        }
    ],
    "creators": [
        {
            "name": "Brill, Ari",
            "orcid": " 0000-0002-6208-5244"
        },
        {
            "affiliation": "University of California Los Angeles, Division of Astronomy and Astrophysics, Los Angeles, CA, USA",
            "name": "Kim, Bryan"
        },
        {
            "affiliation": "Instituto de F\u00edsica de Part\u00edculas y del Cosmos, Departamento de EMFTEL, Universidad Complutense de Madrid, Madrid, Spain ",
            "name": "Nieto, Daniel",
            "orcid": "0000-0003-3343-0755"
        },
        {
            "affiliation": "Instituto de F\u00edsica de Part\u00edculas y del Cosmos, Departamento de EMFTEL, Universidad Complutense de Madrid, Madrid, Spain ",
            "name": "Miener, Tjark",
            "orcid": "0000-0003-1821-7964"
        },
        {
            "affiliation": "Barnard College, Columbia University, New York, NY, USA",
            "name": "Feng, Qi",
            "orcid": " 0000-0001-6674-4238"
        }
    ],
    "description": "CTLearn is a high-level Python package providing a backend for training deep learning models for the reconstruction of imaging atmospheric Cherenkov telescope events using TensorFlow.",
    "grants": [
        {
            "id": "10.13039/501100000780::824064"
        }
    ],
    "keywords": [
        "Imaging atmospheric Cherenkov telescopes",
        "Deep learning",
        "High energy physics",
        "Event reconstruction"
    ],
    "language": "eng",
    "license": "BSD-3-Clause",
    "publication_date": "2023-05-08",
    "related_identifiers": [
        {
            "identifier": "https://github.com/ctlearn-project/ctlearn",
            "relation": "isDerivedFrom",
            "resource_type": "software",
            "scheme": "url"
        }
    ],
    "title": "CTLearn: Deep learning for imaging atmospheric Cherenkov telescopes event reconstruction",
    "upload_type": "software",
    "version": "0.9.0"
}