CTLearn: deep learning for gamma-ray astronomy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F21%3A00552169" target="_blank" >RIV/68378271:_____/21:00552169 - isvavai.cz</a>
Result on the web
<a href="https://pos.sissa.it/358/752/pdf" target="_blank" >https://pos.sissa.it/358/752/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.358.0752" target="_blank" >10.22323/1.358.0752</a>
Alternative languages
Result language
angličtina
Original language name
CTLearn: deep learning for gamma-ray astronomy
Original language description
CTLearn is a new Python package under development that uses the deep learning technique to analyze data from imaging atmospheric Cherenkov telescope (IACT) arrays. IACTs use the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays, to form an image of the longitudinal development of the air shower on the camera plane. The spatial, temporal, and calorimetric information of the originating high-energy particle is then recorded electronically.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
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Continuities
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Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Science
ISBN
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ISSN
1824-8039
e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
Sissa Medilab srl
Place of publication
Trieste
Event location
Madison, WI
Event date
Jul 24, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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