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CNN-Based Classifier as an Offline Trigger for the CREDO Experiment
Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth fr...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309790/ https://www.ncbi.nlm.nih.gov/pubmed/34300544 http://dx.doi.org/10.3390/s21144804 |
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author | Piekarczyk, Marcin Bar, Olaf Bibrzycki, Łukasz Niedźwiecki, Michał Rzecki, Krzysztof Stuglik, Sławomir Andersen, Thomas Budnev, Nikolay M. Alvarez-Castillo, David E. Cheminant, Kévin Almeida Góra, Dariusz Gupta, Alok C. Hnatyk, Bohdan Homola, Piotr Kamiński, Robert Kasztelan, Marcin Knap, Marek Kovács, Péter Łozowski, Bartosz Miszczyk, Justyna Mozgova, Alona Nazari, Vahab Pawlik, Maciej Rosas, Matías Sushchov, Oleksandr Smelcerz, Katarzyna Smolek, Karel Stasielak, Jarosław Wibig, Tadeusz Woźniak, Krzysztof W. Zamora-Saa, Jilberto |
author_facet | Piekarczyk, Marcin Bar, Olaf Bibrzycki, Łukasz Niedźwiecki, Michał Rzecki, Krzysztof Stuglik, Sławomir Andersen, Thomas Budnev, Nikolay M. Alvarez-Castillo, David E. Cheminant, Kévin Almeida Góra, Dariusz Gupta, Alok C. Hnatyk, Bohdan Homola, Piotr Kamiński, Robert Kasztelan, Marcin Knap, Marek Kovács, Péter Łozowski, Bartosz Miszczyk, Justyna Mozgova, Alona Nazari, Vahab Pawlik, Maciej Rosas, Matías Sushchov, Oleksandr Smelcerz, Katarzyna Smolek, Karel Stasielak, Jarosław Wibig, Tadeusz Woźniak, Krzysztof W. Zamora-Saa, Jilberto |
author_sort | Piekarczyk, Marcin |
collection | PubMed |
description | Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process. |
format | Online Article Text |
id | pubmed-8309790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83097902021-07-25 CNN-Based Classifier as an Offline Trigger for the CREDO Experiment Piekarczyk, Marcin Bar, Olaf Bibrzycki, Łukasz Niedźwiecki, Michał Rzecki, Krzysztof Stuglik, Sławomir Andersen, Thomas Budnev, Nikolay M. Alvarez-Castillo, David E. Cheminant, Kévin Almeida Góra, Dariusz Gupta, Alok C. Hnatyk, Bohdan Homola, Piotr Kamiński, Robert Kasztelan, Marcin Knap, Marek Kovács, Péter Łozowski, Bartosz Miszczyk, Justyna Mozgova, Alona Nazari, Vahab Pawlik, Maciej Rosas, Matías Sushchov, Oleksandr Smelcerz, Katarzyna Smolek, Karel Stasielak, Jarosław Wibig, Tadeusz Woźniak, Krzysztof W. Zamora-Saa, Jilberto Sensors (Basel) Article Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process. MDPI 2021-07-14 /pmc/articles/PMC8309790/ /pubmed/34300544 http://dx.doi.org/10.3390/s21144804 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Piekarczyk, Marcin Bar, Olaf Bibrzycki, Łukasz Niedźwiecki, Michał Rzecki, Krzysztof Stuglik, Sławomir Andersen, Thomas Budnev, Nikolay M. Alvarez-Castillo, David E. Cheminant, Kévin Almeida Góra, Dariusz Gupta, Alok C. Hnatyk, Bohdan Homola, Piotr Kamiński, Robert Kasztelan, Marcin Knap, Marek Kovács, Péter Łozowski, Bartosz Miszczyk, Justyna Mozgova, Alona Nazari, Vahab Pawlik, Maciej Rosas, Matías Sushchov, Oleksandr Smelcerz, Katarzyna Smolek, Karel Stasielak, Jarosław Wibig, Tadeusz Woźniak, Krzysztof W. Zamora-Saa, Jilberto CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title | CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title_full | CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title_fullStr | CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title_full_unstemmed | CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title_short | CNN-Based Classifier as an Offline Trigger for the CREDO Experiment |
title_sort | cnn-based classifier as an offline trigger for the credo experiment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309790/ https://www.ncbi.nlm.nih.gov/pubmed/34300544 http://dx.doi.org/10.3390/s21144804 |
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