Determination of spherical coordinates of sampled cosmic ray flux distribution using Principal Components Analysis and deep Encoder-Decoder network
Year: 2024
Type: article
Abstract: In this paper we propose a novel algorithm based on the use of Principal Components Analysis for the determination of spherical coordinates of sampled cosmic ray flux distribution. We have also applied a deep neural network with encoder-decoder (E-D) architecture in order to filter-off variance noises introduced by sampling. We conducted a series of experiments testing the effectiveness of our estimations. The training set consisted of 92250 images and validation set of 37800 images. We have calculated mean absolute error (MAE) between real values and estimations. When E-D is applied, the number of cases (estimations) where MAE < 10 increases from 48% to 79% for θ and from 62% to 65% for ϕ, MAE < 5 increases from 24% to 45% for θ and from 47% to 52% for ϕ, MAE < 1 increases from 6% to 9% for θ and from 12% to 16% for ϕ, where θ is the zenith angle, and ϕ is the azimuthal angle. This is a significant change and it demonstrates the high utility of the E-D network use and shows the accuracy of the PCA-based algorithm. We also publish the source code used in our research in order to make it reproducible.
Source: Machine Graphics and Vision
Institution: AGH University of Krakow
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406639417