[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127831-en":3,"doc-seo-127831-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127831,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning for the Automatic Classification of Radio Spectra","Machine Learning for the Automatic Classification of Radio Spectra focuses on SKR, a non-thermal auroral emission whose Low Frequency Extensions (LFEs) extend global intensifications to lower frequencies. LFEs can be triggered by tail reconnection or solar-wind compressions. The work develops a visual selection criterion for LFE identification, then fits precise frequency-time coordinates with polygonal regions. Using these LFEs as training data, a multi-channel image-based model classifies Cassini/RPWS LFEs and outputs binary frequency-time masks. Median IoU values reach 0.97–0.98, enabling detection of 4,874 LFEs with a usable coordinate catalogue.","NATIONAL UNIVERSITY OF IRELAND MAYNOOTH  \nMachine Learning for the Automatic Classification of Radio Spectra  \nAuthor: Elizabeth O’Dwyer  \nA thesis submitted in fulfillment of the requirements for the degree of Research Masters in the  \nDepartment of Mathematics and Statistics  \nCarried out in coordination with the  \nSchool of Cosmic Physics, Dublin Institute of Advanced Studies.  \nOctober 2023  \nHead of Department  \nProf. Stephen Buckley  \nSupervisor: External Supervisor:  \nDr. Katarina Domijan Prof. Caitriona Jackman  \nThis thesis has been prepared in accordance with the PhD regulations of Maynooth University and is subject to copyright. For more information see PhD Regulations (December 2022) .  \nAcknowledgements  \nI would like to thank my supervisors, Caitriona and Katarina. You were an amazing support throughout the project and I surprised myself with how much I could achieve in the last two years, largely in thanks to your expertise and guidance. It’s been a great pleasure to work with you both. I’d like to thank the Planetary Magnetospheres group in DIAS, it’s been wonderful to work with a group so talented and knowledgeable, as well as kind and full of good humour. I would like to acknowlege how important my friends and family have been during this time, thank you for all of the support, advice and a well needed respite from the hard work. I’d also like to thank my partner Conor for bringing me so much joy and encouragement in both the happier and more difficult periods over the last few years.  \nContents  \nAcknowledgements iii  \nAbstract vii  \nList of Publications viii  \n0.1 Introduction .................................... 1  \n0.2 Chapter I ...................................... 4  \n0.3 Chapter II ..................................... 21  \n0.4 Chapter III ..................................... 40  \n0.5 Chapter IV ..................................... 55  \n0.6 Chapter V ..................................... 66  \n0.7 Code ........................................ 75  \n0.8 Datasets ...................................... 76  \n0.9 Conclusion ..................................... 77  \nBibliography 81  \nNATIONAL UNIVERSITY OF IRELAND MAYNOOTH  \nAbstract  \nDepartment of Mathematics and Statistics  \nResearch Masters  \nMachine Learning for the Automatic Classification of Radio Spectra  \nby Elizabeth O’DWYER  \nSKR is a non-thermal, auroral emission with peak emission occurring at 100-400 kHz. Its properties have been extensively studied since Cassini’s arrival at Saturn until mission end with its Radio and Plasma Wave Science (RPWS) experiment. Low Frequency Extensions (LFEs) of SKR, which consist of global intensifications of SKR accompanied by extensions of the main SKR band down to lower frequencies have been studied in particular. LFEs result from internally-driven tail reconnection and from solar wind compressions of the magnetosphere, which also trigger tail reconnection. They have been previously identified with two approaches: through visual inspection and using an intensity threshold for LFEs occurring in 2006 (Reed et al., 2018) . In this work, we describe the method used to develop a visual criterion for LFE selection, and use this method to select a sample of LFEs detected by Cassini/RPWS by fitting their exact frequency-time coordinates with polygons. We use this sample of LFEs as a training set for an imagebased machine learning algorithm to classify all LFEs detected by Cassini/RPWS. The inputs to the model are multi-channel images consisting of spectrogram images in flux density and degree of circular polarisation. The outputs of the model are binary masks showing the exact location of the LFE in frequency-time space. The median IoU (Intersection Over Union) across the testing and training set were calculated tobe 0.97 and 0.98 respectively. 4874 LFEs were detected using this method and the catalogue in the form of frequency-time coordinates is available for use amongst the scientific community.  \nList of Publicat","cbCainROghufHUT6","https://ap.wps.com/l/cbCainROghufHUT6","pdf",28276258,2,1,99,"English","en",105,"# Introduction\n# Chapter I\n# Chapter II\n# Chapter III\n# Chapter IV\n# Chapter V\n# Code\n# Datasets\n# Conclusion\n# Bibliography","[{\"question\":\"What does the thesis target in radio astronomy?\",\"answer\":\"It targets SKR and, in particular, the Low Frequency Extensions (LFEs) of SKR observed in Cassini/RPWS data.\"},{\"question\":\"How are LFEs selected and prepared for training in this work?\",\"answer\":\"The thesis develops a visual criterion for LFE selection and selects events by fitting their exact frequency-time coordinates with polygons, producing a labelled training set.\"},{\"question\":\"What does the machine learning model output and how accurate is it?\",\"answer\":\"The model takes multi-channel spectrogram images and outputs binary masks indicating the LFE location in frequency-time space. Reported median IoU values are 0.97 for testing and 0.98 for training.\"}]","Machine Learning for the Automatic Classification of Radio Spectra | PDF",1785942232,249,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-for-the-automatic-classification-of-radio-spectra","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-for-the-automatic-classification-of-radio-spectra/127831/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","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 does the thesis target in radio astronomy?","Question",{"text":76,"@type":77},"It targets SKR and, in particular, the Low Frequency Extensions (LFEs) of SKR observed in Cassini/RPWS data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are LFEs selected and prepared for training in this work?",{"text":81,"@type":77},"The thesis develops a visual criterion for LFE selection and selects events by fitting their exact frequency-time coordinates with polygons, producing a labelled training set.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the machine learning model output and how accurate is it?",{"text":85,"@type":77},"The model takes multi-channel spectrogram images and outputs binary masks indicating the LFE location in frequency-time space. 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