English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system islocated on the Canary Island of La Palma and inspects the very high-energy(VHE, few tens of GeV and above) gamma-ray sky. MAGIC consists of two imagingatmospheric Cherenkov telescopes (IACTs), which capture images of the airshowers originating from the absorption of gamma rays and cosmic rays by theatmosphere, through the detection of Cherenkov photons emitted in the shower.The sensitivity of IACTs to gamma-ray sources is mainly determined by theability to reconstruct the properties (type, energy, and arrival direction) ofthe primary particle generating the air shower. The state-of-the-art IACTpipeline for shower reconstruction is based on the parameterization of theshower images by extracting geometric and stereoscopic features and machinelearning algorithms like random forest or boosted decision trees. In thiscontribution, we explore deep convolutional neural networks applied directly tothe pixelized images of the camera as a promising method for IACT full-eventreconstruction and present the performance of the method on observational datausing CTLearn, a package for IACT event reconstruction that exploits deeplearning.

The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn