[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121824-en":3,"doc-seo-121824-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121824,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics - Paper","Diagnostics are essential for commercial nuclear fusion because accurate plasma measurements sustain fusion reactions. Gamma spectroscopy is widely used to infer neutron energy spectra via activation analysis, but detection limits are constrained by Compton scattering that creates a background continuum overlapping low-energy gamma peaks. This work introduces a digital machine learning Compton suppression algorithm (MLCSA) for HPGe detectors using pulse-shape discrimination to reject Compton events and reduce the low-energy background. Performance is validated with Am-241 and Co-60, improving Am-241 MDA by 51% and signal-to-background by 49% while partially preserving Co-60 peaks and enabling detector-agnostic deployment.","Machine learning based compton suppression for nuclear fusion plasma Diagnostics  \nLENNON, Kimberley, SHAND, Chantal and SMITH, Robin \u003C [http://orcid.org/0000-0002-9671-8599](http://orcid.org/0000-0002-9671-8599)>  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [http://shura.shu.ac.uk/33686/](http://shura.shu.ac.uk/33686/)  \nThis document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it.  \nPublished version  \nLENNON, Kimberley, SHAND, Chantal and SMITH, Robin (2024) . Machine learning based compton suppression for nuclear fusion plasma Diagnostics. Journal of Fusion Energy, 43 (17) .  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nJournal of Fusion Energy (2024) 43:17  \n[https://doi.org/10.1007/s10894-024-00408-9](https://doi.org/10.1007/s10894-024-00408-9)  \nMachine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics  \nKimberley Lennon1,2 · Chantal Shand2 · Robin Smith1  \nAccepted: 7 May 2024 © Crown 2024  \nAbstract  \nDiagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA ofAm-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%) . The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.  \nKeywords Machine learning · Gamma spectroscopy · Fusion · Diagnostics  \nIntroduction  \nGamma spectroscopy is a common method used in the nuclear industry to identify and quantify the presence of radiation. High purity germanium (HPGe) detectors are often selected to measure low intensity or complex gammaray signatures due to their excellent ∼ keV resolution and  \n* Kimberley Lennon  \nkimberley.lennon@ukaea.uk  \nChantal Shand  \nchantal.shand@ukaea.uk  \nRobin Smith  \n[robin.smith@shu.ac.uk](robin.smith@shu.ac.uk)  \n1 Materials and Engineering Research Institute, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK  \n2 Applied Radiation Technology Group, UK Atomic Energy Authority, Culham Campus, Abingdon OX14 3DB, UK  \nwill be the focus of this paper. In the nuclear fusion field, gamma spectroscopy is used in waste characterisation, materials research for future fusion machines, neutron flux quantification via ac","cbCainqev8jgCPyJ","https://ap.wps.com/l/cbCainqev8jgCPyJ","pdf",1368014,1,10,"English","en",105,"# Abstract\n# Introduction\n## Gamma spectroscopy in fusion and diagnostics\n## Compton scattering background and its impact\n## Existing Compton suppression approaches","[{\"question\":\"为什么核聚变等离子体诊断中需要关注康普顿散射？\",\"answer\":\"康普顿散射会在测得的能谱中形成与低能γ射线峰相同能区的连续背景，从而限制检测与表征能力，并提高最低可探测活度（MDA）。\"},{\"question\":\"MLCSA是如何区分光电峰与康普顿散射事件的？\",\"answer\":\"MLCSA通过提取单个脉冲的关键特征，利用机器学习进行脉冲形状判别，将由光电吸收产生的脉冲与由康普顿散射产生的脉冲区分开来。\"},{\"question\":\"论文报告了哪些性能提升结果？\",\"answer\":\"使用HPGe探测器并在含Am-241与Co-60的γ谱上验证后，Am-241的MDA提高51%，信噪比提高49%，同时Co-60峰得到部分保留（降低78%）。\"}]","Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics - Paper | PDF",1785807051,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-compton-suppression-for-nuclear-fusion-plasma-diagnostics-paper","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-based-compton-suppression-for-nuclear-fusion-plasma-diagnostics-paper/121824/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么核聚变等离子体诊断中需要关注康普顿散射？","Question",{"text":75,"@type":76},"康普顿散射会在测得的能谱中形成与低能γ射线峰相同能区的连续背景，从而限制检测与表征能力，并提高最低可探测活度（MDA）。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"MLCSA是如何区分光电峰与康普顿散射事件的？",{"text":80,"@type":76},"MLCSA通过提取单个脉冲的关键特征，利用机器学习进行脉冲形状判别，将由光电吸收产生的脉冲与由康普顿散射产生的脉冲区分开来。",{"name":82,"@type":73,"acceptedAnswer":83},"论文报告了哪些性能提升结果？",{"text":84,"@type":76},"使用HPGe探测器并在含Am-241与Co-60的γ谱上验证后，Am-241的MDA提高51%，信噪比提高49%，同时Co-60峰得到部分保留（降低78%）。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]