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Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education. / Tokareva, Victoria; Kostunin, Dmitriy; Plokhikh, Ivan et al.

In: Proceedings of Science, Vol. 410, 007, 12.01.2022.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Tokareva, V, Kostunin, D, Plokhikh, I & Sotnikov, V 2022, 'Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education', Proceedings of Science, vol. 410, 007.

APA

Tokareva, V., Kostunin, D., Plokhikh, I., & Sotnikov, V. (2022). Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education. Proceedings of Science, 410, [007].

Vancouver

Tokareva V, Kostunin D, Plokhikh I, Sotnikov V. Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education. Proceedings of Science. 2022 Jan 12;410:007.

Author

Tokareva, Victoria ; Kostunin, Dmitriy ; Plokhikh, Ivan et al. / Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education. In: Proceedings of Science. 2022 ; Vol. 410.

BibTeX

@article{70f9562f7ac64958b268f8191724cfb9,
title = "Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education",
abstract = "Modern astroparticle physics makes wide use of machine learning methods in such problems as noise suppression, image recognition, event classification. When using these methods, in addition to obtaining new scientific knowledge, it is important also to take advantage of their educational potential. In this work we present a demo version of the machine-learning based application we have created, which helps students and a broader audience to get more familiar with the cosmic ray physics, and shows how machine learning methods can be used to analyze data. The work discusses the prospects for expanding the application{\textquoteright}s functionality and methodological approaches to the development of interactive outreach materials in this area.",
author = "Victoria Tokareva and Dmitriy Kostunin and Ivan Plokhikh and Vladimir Sotnikov",
note = "Funding Information: This work was supported by the Russian Science Foundation (Grant No. 18-41-06003) and Helmholtz Society (Grant No. HRSF-0027). Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).; 5th International Workshop on Deep Learning in Computational Physics, DLCP 2021 ; Conference date: 28-06-2021 Through 29-06-2021",
year = "2022",
month = jan,
day = "12",
language = "English",
volume = "410",
journal = "Proceedings of Science",
issn = "1824-8039",
publisher = "Sissa Medialab Srl",

}

RIS

TY - JOUR

T1 - Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education

AU - Tokareva, Victoria

AU - Kostunin, Dmitriy

AU - Plokhikh, Ivan

AU - Sotnikov, Vladimir

N1 - Funding Information: This work was supported by the Russian Science Foundation (Grant No. 18-41-06003) and Helmholtz Society (Grant No. HRSF-0027). Publisher Copyright: © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

PY - 2022/1/12

Y1 - 2022/1/12

N2 - Modern astroparticle physics makes wide use of machine learning methods in such problems as noise suppression, image recognition, event classification. When using these methods, in addition to obtaining new scientific knowledge, it is important also to take advantage of their educational potential. In this work we present a demo version of the machine-learning based application we have created, which helps students and a broader audience to get more familiar with the cosmic ray physics, and shows how machine learning methods can be used to analyze data. The work discusses the prospects for expanding the application’s functionality and methodological approaches to the development of interactive outreach materials in this area.

AB - Modern astroparticle physics makes wide use of machine learning methods in such problems as noise suppression, image recognition, event classification. When using these methods, in addition to obtaining new scientific knowledge, it is important also to take advantage of their educational potential. In this work we present a demo version of the machine-learning based application we have created, which helps students and a broader audience to get more familiar with the cosmic ray physics, and shows how machine learning methods can be used to analyze data. The work discusses the prospects for expanding the application’s functionality and methodological approaches to the development of interactive outreach materials in this area.

UR - http://www.scopus.com/inward/record.url?scp=85124068301&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85124068301

VL - 410

JO - Proceedings of Science

JF - Proceedings of Science

SN - 1824-8039

M1 - 007

T2 - 5th International Workshop on Deep Learning in Computational Physics, DLCP 2021

Y2 - 28 June 2021 through 29 June 2021

ER -

ID: 35429957