Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers
Applying Machine Learning (ML) methods for the analysis of muon lateral distributions in Extensive Air Showers detected by citizen science projects, while taking into account the spatial distribution of detectors requires enormous training data sets. Therefore, generating these data sets with typica...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10019257/ |
| _version_ | 1797902239920029696 |
|---|---|
| author | Tomasz Hachaj Lukasz Bibrzycki Marcin Piekarczyk |
| author_facet | Tomasz Hachaj Lukasz Bibrzycki Marcin Piekarczyk |
| author_sort | Tomasz Hachaj |
| collection | DOAJ |
| description | Applying Machine Learning (ML) methods for the analysis of muon lateral distributions in Extensive Air Showers detected by citizen science projects, while taking into account the spatial distribution of detectors requires enormous training data sets. Therefore, generating these data sets with typical Monte Carlo (MC) generators like CORSIKA is computationally prohibitive. Here we present a method which by the application of special augmentation procedures produces the training dataset that is compatible in all essential aspects to the data produced with regular MC computations while avoiding their time overhead. We utilize the Nakamura-Kamata-Greisen (NKG) distribution which was proven to be an attractive alternative to full-fledged simulations. The simulation of <inline-formula> <tex-math notation="LaTeX">$10^{4}$ </tex-math></inline-formula> muons at the ground level takes just a few seconds using our implementation of the NKG approach. For <inline-formula> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula> muons this figure is still around 1 minute. For comparison, CORSIKA based simulation performed on Prometheus supercomputer at CYFRONET computing center an ensemble of <inline-formula> <tex-math notation="LaTeX">$\sim 100$ </tex-math></inline-formula> showers initiated by a particle of <inline-formula> <tex-math notation="LaTeX">$10^{16} eV$ </tex-math></inline-formula> resulted in <inline-formula> <tex-math notation="LaTeX">$\sim 10^{4}$ </tex-math></inline-formula> muons and <inline-formula> <tex-math notation="LaTeX">$\sim 10^{5}$ </tex-math></inline-formula> electrons required computation time of the order of a few days. |
| first_indexed | 2024-04-10T09:14:34Z |
| format | Article |
| id | doaj.art-a63a60f715fb48a0aae887256371923d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a63a60f715fb48a0aae887256371923d2023-02-21T00:01:15ZengIEEEIEEE Access2169-35362023-01-01117410741910.1109/ACCESS.2023.323780010019257Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray ShowersTomasz Hachaj0https://orcid.org/0000-0003-1390-9021Lukasz Bibrzycki1https://orcid.org/0000-0002-6117-4894Marcin Piekarczyk2https://orcid.org/0000-0003-3699-9955Institute of Computer Science, Pedagogical University of Krakow, Krakow, PolandInstitute of Computer Science, Pedagogical University of Krakow, Krakow, PolandInstitute of Computer Science, Pedagogical University of Krakow, Krakow, PolandApplying Machine Learning (ML) methods for the analysis of muon lateral distributions in Extensive Air Showers detected by citizen science projects, while taking into account the spatial distribution of detectors requires enormous training data sets. Therefore, generating these data sets with typical Monte Carlo (MC) generators like CORSIKA is computationally prohibitive. Here we present a method which by the application of special augmentation procedures produces the training dataset that is compatible in all essential aspects to the data produced with regular MC computations while avoiding their time overhead. We utilize the Nakamura-Kamata-Greisen (NKG) distribution which was proven to be an attractive alternative to full-fledged simulations. The simulation of <inline-formula> <tex-math notation="LaTeX">$10^{4}$ </tex-math></inline-formula> muons at the ground level takes just a few seconds using our implementation of the NKG approach. For <inline-formula> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula> muons this figure is still around 1 minute. For comparison, CORSIKA based simulation performed on Prometheus supercomputer at CYFRONET computing center an ensemble of <inline-formula> <tex-math notation="LaTeX">$\sim 100$ </tex-math></inline-formula> showers initiated by a particle of <inline-formula> <tex-math notation="LaTeX">$10^{16} eV$ </tex-math></inline-formula> resulted in <inline-formula> <tex-math notation="LaTeX">$\sim 10^{4}$ </tex-math></inline-formula> muons and <inline-formula> <tex-math notation="LaTeX">$\sim 10^{5}$ </tex-math></inline-formula> electrons required computation time of the order of a few days.https://ieeexplore.ieee.org/document/10019257/Cosmic ray showersimulationdata generationdetectorsmachine learning |
| spellingShingle | Tomasz Hachaj Lukasz Bibrzycki Marcin Piekarczyk Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers IEEE Access Cosmic ray shower simulation data generation detectors machine learning |
| title | Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers |
| title_full | Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers |
| title_fullStr | Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers |
| title_full_unstemmed | Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers |
| title_short | Fast Training Data Generation for Machine Learning Analysis of Cosmic Ray Showers |
| title_sort | fast training data generation for machine learning analysis of cosmic ray showers |
| topic | Cosmic ray shower simulation data generation detectors machine learning |
| url | https://ieeexplore.ieee.org/document/10019257/ |
| work_keys_str_mv | AT tomaszhachaj fasttrainingdatagenerationformachinelearninganalysisofcosmicrayshowers AT lukaszbibrzycki fasttrainingdatagenerationformachinelearninganalysisofcosmicrayshowers AT marcinpiekarczyk fasttrainingdatagenerationformachinelearninganalysisofcosmicrayshowers |