[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124600-en":3,"doc-seo-124600-105":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},124600,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Searching for Quasi-Periodic Eruptions using Machine Learning - Abstract","Quasi-Periodic Eruptions (QPEs) are rare X-ray flare sequences in the nuclei of galaxies, and only a small number have been observed, leaving many potential undetected sources in archival data. Because manual scanning is impractical at current data volumes, machine learning is applied to identify time-domain features that flag QPE-like lightcurves. A neural network using 14 variability measures reaches over 94% accuracy on simulated data and over 98% on observational data, supported by 12 QPE and 52 non-QPE lightcurves. Analysis of 83,531 X-ray detections from the XMM Serendipitous Source Catalogue recovers known QPE sources and examples of variable stellar categories.","arXiv :2305 .03629v1 [ astro-ph .IM] 5 May 2023  \nSearching for Quasi-Periodic Eruptions using Machine Learning  \nRobbie Webbe, 1★ and A. J. Young 1†  \n1H. H. Wills Physics Laboratory, Tyndall Avenue, Bristol, BS8 1TL, UK  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nQuasi-Periodic Eruptions (QPEs) are a rare phenomenon in which the X-ray emission from the nuclei of galaxies shows a series of large amplitude flares. Only a handful of QPEs have been observed but the possibility remains that there are as yet undetected sources in archival data. Given the volume of data available a manual search is not feasible, and so we consider an application of machine learning to archival data to determine whether a set of time-domain features can be used to identify further lightcurves containing eruptions. Using a neural network and 14 variability measures we are able to classify lightcurves with accuracies of greater than 94% with simulated data and greater than 98% with observational data on a sample consisting of 12 lightcurves with QPEs and 52 lightcurves without QPEs. An analysis of 83,531 X-ray detections from the XMM Serendipitous Source Catalogue allowed us to recover lightcurves of known QPE sources and examples of several categories of variable stellar objects.  \nKey words: Machine Learning – X-rays: galaxies – galaxies: nuclei  \n1 INTRODUCTION  \nThe role of machine learning in astrophysics is becoming progressively important, with the scope and scale of surveys and planned missions resulting in ever-increasing volumes of data and in growing archives for current missions. Citizen science projects, like Galaxy Zoo (e.g. Lintott et al. 2008) will struggle to cope with the volume of data that is expected to be produced with planned survey missions. There is a pressing need to develop automated tools which can process and reduce volumes of data to manageable amounts. Transient events of many types have been the focus of several machine learning approaches. Due to their fleeting nature and the time sensitive nature of follow-up efforts, automation has the potential to increase the number of transient events detected, and to allow for them to be detected sooner. This will allow a greater proportion of their lifetimesto be monitored and including automation in processing pipelines also allows for such events to be detected before a scientist could interact with the observed data. Approaches in using supervised and unsupervised machine learning to detect and classify a greater proportion of transient events in (near) real time (e.g. Narayan et al. 2018; Muthukrishna et al. 2019a,b, 2022) using optical observations have allowed supernovae and some other classes of transients to be detected during the course of the events, although understandably the accuracy of these techniques increases as more of the events are detected. High energy data presents different challenges, as the statistics underpinning observed data are different due to the typically low count rates. Attempts at detecting X-ray transient sources using supervised learning (Random Forest methods) with the 2XMM and 3XMM Serendipitous Source Catalogues have achieved accuracies of ' 97%(Lo et al. 2014) and ' 92%(Farrell et al. 2015) across multi-class classifications using combinations of time-domain and spectroscopic features.  \n★ [https://orcid.org/0000-0003-1689-3723](https://orcid.org/0000-0003-1689-3723)  \n† [https://orcid.org/0000-0003-3626-9151](https://orcid.org/0000-0003-3626-9151)  \nWith new classes of X-ray transients like Quasi-Periodic Eruptions continuing to be discovered it is important to develop methods for detecting these new classes both in archival data and continuing and planned surveys as soon as possible to develop our understanding of these transients. If it is possible to detect QPEs with established machine learning techniques and variability features used to detect other types of variability, this could significantly increase our known QPE h","cbCaihhmMyYSpSqp","https://ap.wps.com/l/cbCaihhmMyYSpSqp","pdf",1762555,1,19,"English","en",105,"# Abstract\n# Introduction\n## Quasi-Periodic Eruptions","[{\"question\":\"Why can’t QPEs be searched manually in archival data?\",\"answer\":\"The available data volume is too large, making manual searching not feasible for systematically identifying QPE candidates.\"},{\"question\":\"How does the method detect QPEs in lightcurves?\",\"answer\":\"A neural network uses 14 variability measures extracted from time-domain lightcurves to classify whether they contain QPE eruptions.\"},{\"question\":\"What performance results are reported for the classifier?\",\"answer\":\"Using simulated data, the approach achieves accuracies above 94%, and with observational data it achieves above 98% on a dataset of 12 QPE and 52 non-QPE lightcurves.\"}]","Searching for Quasi-Periodic Eruptions using Machine Learning - Abstract | PDF",1785893254,48,{"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},"searching-for-quasi-periodic-eruptions-using-machine-learning-abstract","",{"@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/searching-for-quasi-periodic-eruptions-using-machine-learning-abstract/124600/",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","2026-08-05",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},"Why can’t QPEs be searched manually in archival data?","Question",{"text":75,"@type":76},"The available data volume is too large, making manual searching not feasible for systematically identifying QPE candidates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method detect QPEs in lightcurves?",{"text":80,"@type":76},"A neural network uses 14 variability measures extracted from time-domain lightcurves to classify whether they contain QPE eruptions.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for the classifier?",{"text":84,"@type":76},"Using simulated data, the approach achieves accuracies above 94%, and with observational data it achieves above 98% on a dataset of 12 QPE and 52 non-QPE lightcurves.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]