Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets
সংরক্ষণ করুন:
| প্রকাশিত: | Atmosphere vol. 15, no. 9 (2024), p. 1039 |
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| প্রধান লেখক: | |
| অন্যান্য লেখক: | , , , , , , , , |
| প্রকাশিত: |
MDPI AG
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
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MARC
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| 001 | 3110395805 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos15091039 |2 doi | |
| 035 | |a 3110395805 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Ticiano Jorge Torres Peralta |u Tucumán Space Weather Center (TSWC), Facultad de Ciencias Exactas y Tecnología (FACET-UNT), San Miguel de Tucumán T4000, Argentina; <email>ttorres@herrera.unt.edu.ar</email>; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425, Argentina | |
| 245 | 1 | |a Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis. | |
| 653 | |a Cerenkov counters | ||
| 653 | |a Datasets | ||
| 653 | |a Observational learning | ||
| 653 | |a Atmospheric radiation | ||
| 653 | |a Unsupervised learning | ||
| 653 | |a Atmosphere | ||
| 653 | |a Downward long wave radiation | ||
| 653 | |a Machine learning | ||
| 653 | |a Particle settling | ||
| 653 | |a Radiation | ||
| 653 | |a Computer simulation | ||
| 653 | |a Data analysis | ||
| 653 | |a Detectors | ||
| 653 | |a Clustering | ||
| 653 | |a Earth atmosphere | ||
| 653 | |a Algorithms | ||
| 653 | |a Observatories | ||
| 653 | |a Real time | ||
| 653 | |a Cosmic radiation | ||
| 653 | |a Solar cycle | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Atmospheric conditions | ||
| 653 | |a Sea level | ||
| 653 | |a Energy | ||
| 653 | |a Cosmic rays | ||
| 653 | |a Fluctuations | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Optics | ||
| 653 | |a Charged particles | ||
| 653 | |a Cosmic ray showers | ||
| 653 | |a Magnetospheres | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Molina, Maria Graciela |u Tucumán Space Weather Center (TSWC), Facultad de Ciencias Exactas y Tecnología (FACET-UNT), San Miguel de Tucumán T4000, Argentina; <email>ttorres@herrera.unt.edu.ar</email>; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425, Argentina; Instituto Nazionale di Geofisica e Vulcanologia (INGV), 00143 Roma, Italy | |
| 700 | 1 | |a Asorey, Hernan |u Medical Physics Department, Centro Atómico Bariloche, Comisión Nacional de Energía Atómica (CNEA), Bariloche R8402, Argentina | |
| 700 | 1 | |a Sidelnik, Ivan |u Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425, Argentina; Departamento de Física de Neutrones, Centro Atómico Bariloche, Comisión Nacional de Energía Atómica (CNEA), Bariloche R8402, Argentina | |
| 700 | 1 | |a Antonio Juan Rubio-Montero |u Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain | |
| 700 | 1 | |a Dasso, Sergio |u Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425, Argentina; Laboratorio Argentino de Meteorología del esPacio (LAMP), Buenos Aires C1428, Argentina; Departamento de Ciencias de la Atmósfera y los Océanos (DCAO), Facultad de Ciencias Exactas y Naturales (FCEN, UBA), Buenos Aires C1428, Argentina; Instituto de Astronomía y Física del Espacio (IAFE), Buenos Aires C1428, Argentina | |
| 700 | 1 | |a Mayo-Garcia, Rafael |u Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain | |
| 700 | 1 | |a Taboada, Alvaro |u Instituto de Tecnologías en Detección y Astropartículas (ITeDA), Buenos Aires B1650, Argentina | |
| 700 | 1 | |a Otiniano, Luis |u Comisión Nacional de Investigación y Desarrollo Aeroespacial (CONIDA), Lima 15046, Peru | |
| 700 | 1 | |a Pulinets, Sergey | |
| 773 | 0 | |t Atmosphere |g vol. 15, no. 9 (2024), p. 1039 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3110395805/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3110395805/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3110395805/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |