ANOMALY DETECTION IN SIGNALS USING AUTOENCODER: AN ARTIFICIAL INTELLIGENCE APPROACH IN THE JUNO EXPERIMEN

Аннотация

The mass of neutrinos and their oscillation behavior remain one of the most complex and unresolved problems in modern nuclear and particle physics. The Jiangmen Underground Neutrino Observatory (JUNO) is a major international scientific experiment designed specifically to address these issues. By determining the hierarchy of neutrino masses, the project is expected to significantly advance our understanding of particle physics and the Standard Model.This paper focuses on the application of artificial intelligence—particularly, an autoencoder model—for detecting rare signals observed in the JUNO experiment. This approach has proven effective in identifying infrequent but scientifically important events that might otherwise be missed. Autoencoders, as unsupervised learning models, enhance the ability to detect anomalies and extract meaningful patterns from large-scale experimental data. It is important to note that JUNO is not merely a study of subatomic particles. Rather, it represents a scientific journey toward uncovering the deeper mysteries of the universe. Sometimes, the quietest and mostunassuming signals may hold thekey tothemost fundamental laws ofnature. Аннотация:Масса нейтрино и их колебательное поведение остаются одними из самых сложных и нерешённых вопросов современной ядерной и физики элементарных частиц. Цзянмэньская подземная нейтринная обсерватория (JUNO) — это крупный международный научный эксперимент, специально разработанный для изучения этих проблем. Определив иерархию масс нейтрино, проект, как ожидается, внесёт значительный вклад в развитие физики частиц и Стандартной модели. В данной работе рассматривается применение искусственного интеллекта — в частности, автоэнкодеров — для обнаружения редких сигналов, наблюдаемых в эксперименте JUNO. Этот подход доказал свою эффективность в выявлении редких, но научно значимых событий, которые в противном случае могли бы остаться незамеченными. Автоэнкодеры, как модели обучения без учителя, повышают способность обнаруживать аномалии и извлекать значимые закономерности из крупномасштабных экспериментальных данных. Важно отметить, что JUNO — это не просто исследование субатомных частиц. Это научное путешествие к раскрытию глубинных тайн Вселенной. Порой самые тихие и неприметные сигналы могут содержать ключ к пониманию фундаментальных законов природы. page92 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue06,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai Keywords:JUNO, neutrino physics, autoencoder, mass hierarchy, liquid scintillator, photomultipliertube,oscillation, fundamental particles, modern technologies Ключевые слова: JUNO, нейтринная физика, автоэнкодер, иерархия масс, жидкий сцинтиллятор, фотоумножитель, осцилляции, фундаментальные частицы, современные технологии Neutrinos are considered one of the most mysterious particles in modern physics today. Although there is evidence proving that neutrinos have mass, the exact ordering of these masses remains an open question. To address this challenge, a major international project—Jiangmen Underground Neutrino Observatory (JUNO)—was established. The project officially began its operations in 2014 and is based on a specially constructed neutrino detector located 700 meters underground in Guangdong Province, China.In November 2024, the main structure of the detector, including a 35.4-meter diameter acrylic sphere and over 45,000 photomultiplier tubes (PMTs), was completed. While the JUNO team planned to finalize all construction and installation work by the end of November 2024, the full commissioning of the detector is scheduled for August 2025[1]. A significant amount of funding has been allocated for the design and construction of this scientific facility, with its total estimated cost reaching approximately 300 million USD [2]. The detector was designed to surpass other existing neutrino laboratories in terms of both volume and sensitivity. Its technical foundation consists of a 20,000-ton liquid scintillator, thousands ofphotodetectors, and highly precise measurement systems [3]. The primary objective of the JUNO project is to determine the neutrino mass hierarchy—that is, to identify which of the three neutrino types is the heaviest and which is the lightest. The project aims not only to detect neutrinos from reactor sources but also to observe those originating from the Sun, supernovae, and the atmosphere [3,4]. Experiments of this scale demand complex technological solutions, precision, and extensive international collaboration. As of today, the JUNO project includes hundreds of scientists from more than ten countries, includingChina, Italy, Germany, France, Russia, theUnited States,and others [5]. In fact, the first experiments on neutrinos date back to the mid-20th century. For example, in 1956, reactor neutrinos were detected for the first time by Clyde Cowan and Frederick Reines [6]. La