CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F21%3A00354721" target="_blank" >RIV/68407700:21670/21:00354721 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/s21144804" target="_blank" >https://doi.org/10.3390/s21144804</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21144804" target="_blank" >10.3390/s21144804</a>

Result language

angličtina

Original language name

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Original language 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10308 - Astronomy (including astrophysics,space science)

Project

<a href="/en/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Engineering applications of microworld physics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

21

Issue of the periodical within the volume

14

Country of publishing house

CH - SWITZERLAND

Number of pages

10

Pages from-to

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UT code for WoS article

000677055800001

EID of the result in the Scopus database

2-s2.0-85110012338