[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128205-en":3,"doc-seo-128205-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128205,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Application of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection","High-energy particle detection faces a key challenge: accurately separating gamma signals from a complex hadron background in binary classification settings. This research develops a machine learning classification model to distinguish gamma and hadrons as data volume and complexity increase. Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are evaluated using geometric parameters, including fLength, fWidth, fAlpha, fDist, and related photon distribution variables. Feature relevance is analyzed with PCA and correlation, while Monte Carlo simulations validate spectral distributions. Random Forest achieves 87.96% accuracy with F1-score 0.91, identifying fSize, fLength, and fWidth as dominant factors.","Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) 18/2 (2025), 181-205. DOI: [http://dx.doi.org/10.21609/jiki.v18i2.1489](http://dx.doi.org/10.21609/jiki.v18i2.1489)  \nApplication of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection  \nFirdaus Andi Wibowo1, Tomi Yulianto1, Nicholaus Ola Malun, Rizqy Rionaldy1, Verdi Yasin2,  \nRuben Cornelius Siagian3  \n1Data Science Business Informatics, Swiss German University, Banten, Indonesia 2Department of Informatics Engineering, Jayakarta College of Information and Computer Management,  \nJakarta, Indonesia  \n3Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, Medan, Indonesia  \n[E-mail:](E-mail: firdaus.wibowo@student.sgu.ac.id)[ ](E-mail: firdaus.wibowo@student.sgu.ac.id)[firdaus.wibowo@student.sgu.ac.id](E-mail: firdaus.wibowo@student.sgu.ac.id)  \nAbstract  \nA major challenge in particle physics is the binary classification of high-energy gamma signals against a complex hadron background. Accurate identification of these gamma signals is critical for particle detection, especially as the volume and complexity of data increases as technology advances. The research developed a machine learning-based classification model to efficiently and accurately distinguish gamma signals from hadrons. Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are used for classification. Principal Component Analysis (PCA) and correlation analysis identified dominant features, while Monte Carlo simulations validated the distribution of gamma and hadron spectra. This study focuses on geometric parameters such as fLength, fWidth, fAlpha, as well as photon distribution and distance effects (fDist) in gamma signal identification using K-Means clustering. The Random Forest algorithm achieved the highest accuracy of 87.96%, with an F1-score of 0.91, which defines its robustness in the classification task. PCA and correlation analysis showed fSize, fLength, and fWidth as the most influential factors in classification. Monte Carlo simulations successfully replicated the spectral distribution pattern with high experimental validation.  \nThe research presents a novel integration of geometric analysis, clustering techniques, and simulation validation in the classification of high-energy particles. Machine learning methods, in particular Random Forest, effectively distinguish the gamma signal from the hadron background. The combination of PCA and Monte Carlo simulations improves the understanding of data distribution patterns and key classification factors. This research contributes to the development of a more reliable astrophysical signal classification system with potential applications in large-scale astronomical data management.  \nKeywords: Gamma signal detection, particle physics classification, machine learning algorithms, monte carlo simulation, geometric parameter analysis  \n1. Introduction  \nIn particle physics, one of the main challenges is distinguishing between very high-energy gamma signals and the more complex and more numerous hadron background [1],[2] . This process is crucial for particle detection and identification in physics experiments, especially in particle telescopes and astrophysical observatories. Gamma, which is a high-energy photon, is often the signal to look for, but hadrons, which are made up of particles such as protons and neutrons, often produce a distracting background [3],[4],[5],[6] . With the development of detection and data acquisition technologies, such as particle telescopes and gamma-ray detectors, the  \ndata generated is becoming increasingly large and complex [7] . Identifying and separating the gamma signal from the hadron background requires efficient and accurate methods. One promising solution is the use of machine learning techniques to develop predictive models that can distinguish the two types of signals based on available feature","cbCaij2Nm38qS3Jx","https://ap.wps.com/l/cbCaij2Nm38qS3Jx","pdf",1362164,1,25,"English","en",105,"# Introduction\n## Key challenge in gamma–hadron separation\n## Motivation for machine learning approaches\n## Role of geometric and photon-distribution features\n# Method overview\n## Classification algorithms evaluated\n## Feature extraction and relevance analysis (PCA, correlation)\n## Monte Carlo simulation validation\n# Results and discussion\n## Model performance metrics (accuracy, F1-score, AUC-ROC)\n## Most influential factors for classification","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To build and evaluate a machine learning model that classifies high-energy gamma signals versus hadron background using selected features.\"},{\"question\":\"Which classification algorithms are used, and which performs best?\",\"answer\":\"Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are tested; Random Forest yields the highest accuracy of 87.96% and an F1-score of 0.91.\"},{\"question\":\"How are important features identified and validated?\",\"answer\":\"PCA and correlation analysis determine dominant geometric factors (notably fSize, fLength, and fWidth), while Monte Carlo simulations validate the gamma and hadron spectral distribution patterns.\"}]","Application of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection | PDF",1785945570,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"application-of-machine-learning-methods-for-classification-of-gamma-and-hadron-signals-in-high-energy-particle-detection","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-methods-for-classification-of-gamma-and-hadron-signals-in-high-energy-particle-detection/128205/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of this study?","Question",{"text":76,"@type":77},"To build and evaluate a machine learning model that classifies high-energy gamma signals versus hadron background using selected features.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which classification algorithms are used, and which performs best?",{"text":81,"@type":77},"Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are tested; 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