Inference of cosmic-ray source properties by conditional invertible neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F22%3A00564461" target="_blank" >RIV/68378271:_____/22:00564461 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1140/epjc/s10052-022-10138-x" target="_blank" >https://doi.org/10.1140/epjc/s10052-022-10138-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1140/epjc/s10052-022-10138-x" target="_blank" >10.1140/epjc/s10052-022-10138-x</a>
Alternative languages
Result language
angličtina
Original language name
Inference of cosmic-ray source properties by conditional invertible neural networks
Original language description
The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to their probability-preserving bijective mapping properties. They enable training the parameter-observation correspondence in one mapping direction and evaluating the parameter posterior distributions in the reverse direction. Here, we study the inference of cosmic-ray source properties from cosmic-ray observations on Earth using extensive astrophysical simulations. We compare the performance of conditional invertible neural networks (cINNs) with the frequently used Markov Chain Monte Carlo (MCMC) method. While cINNs are trained to directly predict the parameters' posterior distributions, the MCMC method extracts the posterior distributions through a likelihood function that matches simulations with observations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
—
Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Physical Journal C
ISSN
1434-6044
e-ISSN
1434-6052
Volume of the periodical
82
Issue of the periodical within the volume
2
Country of publishing house
DE - GERMANY
Number of pages
10
Pages from-to
171
UT code for WoS article
000760965400006
EID of the result in the Scopus database
2-s2.0-85125497014