Machine learning aided noise filtration and signal classification for CREDO experiment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19630%2F22%3AA0000245" target="_blank" >RIV/47813059:19630/22:A0000245 - isvavai.cz</a>

Result on the web

<a href="https://pos.sissa.it/395/227" target="_blank" >https://pos.sissa.it/395/227</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22323/1.395.0227" target="_blank" >10.22323/1.395.0227</a>

Result language

angličtina

Original language name

Machine learning aided noise filtration and signal classification for CREDO experiment

Original language description

The wealth of smartphone data collected by the Cosmic Ray Extremely Distributed Observatory (CREDO) greatly surpasses the capabilities of manual analysis. So, efficient means of rejecting the non-cosmic-ray noise and identification of signals attributable to extensive air showers are necessary. To address these problems we discuss a Convolutional Neural Network-based method of artefact rejection and complementary method of particle identification based on common statistical classifiers as well as their ensemble extensions. These approaches are based on supervised learning, so we need to provide a representative subset of the CREDO dataset for training and validation. According to this approach over 2300 images were chosen and manually labeled by 5 judges. The images were split into spot, track, worm (collectively named signals) and artefact classes. Then the preprocessing consisting of luminance summation of RGB channels (grayscaling) and background removal by adaptive thresholding was performed. For purposes of artefact rejection the binary CNN-based classifier was proposed which is able to distinguish between artefacts and signals. The classifier was fed with input data in the form of Daubechies wavelet transformed images. In the case of cosmic ray signal classification, the well-known feature-based classifiers were considered. As feature descriptors, we used Zernike moments with additional feature related to total image luminance. For the problem of artefact rejection, we obtained an accuracy of 99%. For the 4-class signal classification, the best performing classifiers achieved a recognition rate of 88%.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10308 - Astronomy (including astrophysics,space science)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Article name in the collection

Proceedings of Science

ISBN

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ISSN

1824-8039

e-ISSN

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Number of pages

9

Pages from-to

„227-1“-„227-9“

Publisher name

Sissa Medialab Srl

Place of publication

Itálie

Event location

Berlin

Event date

Jul 12, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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