Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger observatory

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00635949" target="_blank" >RIV/68378271:_____/24:00635949 - isvavai.cz</a>
Result on the web
<a href="https://pos.sissa.it/444/275/pdf" target="_blank" >https://pos.sissa.it/444/275/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.444.0275" target="_blank" >10.22323/1.444.0275</a>

Result language
angličtina
Original language name
Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger observatory
Original language description
Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes.
Czech name
—
Czech description
—

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)