Deep learning based algorithms in astroparticle physics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00548785" target="_blank" >RIV/68378271:_____/20:00548785 - isvavai.cz</a>
Result on the web
<a href="https://iopscience.iop.org/article/10.1088/1742-6596/1525/1/012112/pdf" target="_blank" >https://iopscience.iop.org/article/10.1088/1742-6596/1525/1/012112/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/1525/1/012112" target="_blank" >10.1088/1742-6596/1525/1/012112</a>

Result language
angličtina
Original language name
Deep learning based algorithms in astroparticle physics
Original language description
In recent years, great progress has been made in the fields of machine translation, image classification and speech recognition by using deep neural networks and associated techniques (deep learning). Recently, the astroparticle physics community successfully adapted supervised learning algorithms for a wide range of tasks including background rejection, object reconstruction, track segmentation and the denoising of signals. Additionally, the first approaches towards fast simulations and simulation refinement indicate the huge potential of unsupervised learning for astroparticle physics. We summarize the latest results, discuss the algorithms and challenges and further illustrate the opportunities for the astrophysics community offered by deep learning based algorithms.
Czech name
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Czech description
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Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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