[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120562-en":3,"doc-seo-120562-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120562,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Paricle Identi􀀌cation at VAMOS++ with Machine Learning Techniques","Multi-nucleon transfer reaction between 136Xe beam and 198 Pt target was performed using the VAMOS++ spectrometer at GANIL to investigate the structure of neutron-rich nuclei around N=126. Unambiguous charge-state identification was achieved by combining two supervised machine learning approaches: a deep neural network (DNN) and a positional correction based on a gradient-boosting decision tree (GBDT). The proposed framework simplified kinetic-energy calibration and improved charge-state resolution by 8% compared with the conventional procedure.","Paricle Identi􀀌cation at VAMOS++ with Machine Learning Techniques  \nY. Choa,b,c , Y. H. Kimc,􀀃, S. Choia,b , J. Parkc , S. Baec , K. I. Hahnc , Y. Sona,b,c , A. Navind , A. Lemassond , M. Rejmundd , D. Ramosd , D. Ackermannd , A. Utepovd , C. Fourgeresd , J. C. Thomasd , J. Goupild , G. Fremontd , G. de Franced , Y. X. Watanabee , Y. Hirayamae , S. Jeonge , T. Niwasee , H. Miyatakee ,  \nP. Schuryr , M. Rosenbuschr , K. Chaef , C. Kimf , S. Kimf , G. M. Guf , M. J. Kimf , P. Johng ,  \nA. N. Andreyevh , W. Korteni , F. Recchiaj , G. de Angelisk , R. Perez Vidalk , K. Rezynkinal , J. Ham ,  \nF. Didierjeann , P. Marinio , D. Treasao , I. Tsekhanovicho , J. Dudouetp , S. Bhattacharyyaq , G. Mukherjeeq ,  \nR. Banikq , S. Bhattacharyaq , M. Mukair  \na Department of Physics and Astronomy, Seoul National University, Seoul 08826, Republic of Korea b Institute for Nuclear and Particle Astrophysics, Seoul National University, Seoul 08826, Republic of Korea c Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon 34126, Republic of Korea  \ndGrand Acc􀀓el􀀓erateur National d’Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, F-14076 CAEN Cedex 05, France e Wako Nuclear Science Center, IPNS, High Energy Accelerator Research Organization (KEK), Wako, Saitama 351-0198,  \nJapan  \nfDepartment of Physics, Sungkyunkwan University, Suwon 16419, Korea  \ng Technische Universit¨at Darmstadt, Karolinenplatz 5, 64289 Darmstadt  \nh School of Physics, Engineering and Technology, University of York, Heslington, York, North Yorkshire YO10 5DD, United  \nKingdom  \niFrench Alternative Energies and Atomic Energy Commission, 17 rue des Martyrs 38054 Grenoble Cedex 9 France j University of Padua, Via 8 Febbraio, 1848, 2, 35122 Padua, Italy  \nkINFN Laboratori Nazionali di Legnaro, IT-35020 Legnaro, Italy  \nlINFN Sezione di Padova and Dipartimento di Fisica e Astronomia dell’Universita’, I-35131 Padova, Italy mKatholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium  \nn Institut Pluridisciplinaire Hubert Curien, Batiment 27, BP28, 67037 Cedex 2, 23 Rue du Loess, 67200 Strasbourg, France  \no Centre Etudes Nucl􀀓eaires de Bordeaux Gradignan, 19 Chem. du Solarium, 33170 Gradignan, France pInsitut de Physique Nucl􀀓eaire de Lyon, Universit􀀓e de Lyon, Universit􀀓e Lyon 1, CNRS-IN2P3, F-69622 Villeurbanne,  \nFrance  \nq Variable Energy Cyclotron Centre, 1/AF, Bidhannagar, Kolkata, West Bengal 700064, India  \nr RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan  \nAbstract  \nMulti-nucleon transfer reaction between 136Xe beam and 198 Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126 . Unambiguous charge state identi􀀌cation was obtained by combining two supervised machine learning methods, deep neural network (DNN) and positional correction using a gradient-boosting decision tree (GBDT) . The new method reduced the complexity of the kinetic energy calibration and outperformed the conventional method improving the charge state resolution by 8% .  \nKeywords: VAMOS++, Machine learning, Multi-nucleon transfer reaction  \n1. Introduction  \nMulti-nucleon transfer (MNT) reactions near the Coulomb barrier gained renewed interest in accessing nuclides which are challenging to produce using  \n5 conventional reactions [1, 2] . One of the main chal- 10  \nlenges in such experiments using a magnetic spectrometer is particle identi􀀌cation because of the reaction fragment’s large mass number (A), atomic number (Z), and wide range of charge states (Q) .  \nRecently, machine learning methods are being applied in nuclear physics theories and experiments, due to their e􀀋ectiveness and ease of use. Some ap-  \n􀀃 Corresponding author  \nEmail address: [yunghee.kim@ibs.re.kr](yunghee.kim@ibs.re.kr) (Y. H. Kim)  \nplications of machine learning methods in particle identi􀀌cation are discussed [3] .  \nPreprint submitted to Nucl. Instr. and Methods in Phys. Res. Sec. B May 26, 2023  \n15 However, the applications o","cbCain8g6bAVLNjd","https://ap.wps.com/l/cbCain8g6bAVLNjd","pdf",344106,1,4,"English","en",105,"# Introduction\n# Experimental setup\n# Paticle identi􀀌cation methods","[{\"question\":\"What reaction and target were used to study neutron-rich nuclei around N=126?\",\"answer\":\"The study used a multi-nucleon transfer reaction between a 136Xe beam and a 198Pt target at GANIL to populate neutron-rich nuclides toward the N=126 shell closure.\"},{\"question\":\"How was unambiguous charge-state identification obtained in the experiment?\",\"answer\":\"Charge-state identification combined two supervised machine learning methods: a deep neural network (DNN) and a positional correction using a gradient-boosting decision tree (GBDT).\"},{\"question\":\"What improvement did the machine learning method bring over the conventional approach?\",\"answer\":\"The new method reduced the complexity of kinetic-energy calibration and improved charge-state resolution by 8% compared with the conventional method.\"}]","Paricle Identi􀀌cation at VAMOS++ with Machine Learning Techniques | PDF",1785730661,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"paricle-identification-at-vamos-with-machine-learning-techniques","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/paricle-identification-at-vamos-with-machine-learning-techniques/120562/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What reaction and target were used to study neutron-rich nuclei around N=126?","Question",{"text":74,"@type":75},"The study used a multi-nucleon transfer reaction between a 136Xe beam and a 198Pt target at GANIL to populate neutron-rich nuclides toward the N=126 shell closure.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How was unambiguous charge-state identification obtained in the experiment?",{"text":79,"@type":75},"Charge-state identification combined two supervised machine learning methods: a deep neural network (DNN) and a positional correction using a gradient-boosting decision tree (GBDT).",{"name":81,"@type":72,"acceptedAnswer":82},"What improvement did the machine learning method bring over the conventional approach?",{"text":83,"@type":75},"The new method reduced the complexity of kinetic-energy calibration and improved charge-state resolution by 8% compared with the conventional method.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]