Deep learning based algorithms in astroparticle physics

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning based algorithms in astroparticle physics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep learning based algorithms in astroparticle physics

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

—

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

—

ISSN

1742-6588

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IOP Publishing Ltd.

Místo vydání

Bristol

Místo konání akce

Saas-Fee

Datum konání akce

11. 3. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000618973400112