[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117818-en":3,"doc-seo-117818-105":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},117818,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","QPOML - A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries","Astronomy is expanding its use of machine learning to analyze large datasets, yet transient quasi-periodic oscillations (QPOs) in the power density spectra of many X-ray binary observations remain largely unexplored by ML. This work proposes and tests new methods to predict QPO presence and infer their properties, enabling early detections and characterizations using ML models. Results rely on raw energy spectra and derived processed features from NICER and Rossi X-ray Timing Explorer archives for GRS 1915+105 and MAXI J1535-571, and motivate ML-driven generalizations of energy and timing behavior. A public Python library, QPOML, is released to support further QPO studies.","arXiv :2306 .04055v1 [ astro-ph .HE] 6 Jun 2023  \nQPOML: A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries  \nThaddaeus J. Kiker, 1,2,3★ , James F. Steiner4 , Cecilia Garraffo4 , Mariano Méndez5 , and Liang Zhang 6  \n1 Sunny Hills High School, 1801 Lancer Way, Fullerton, CA 92833, USA  \n2 Department of Physics, Columbia University, New York, NY 10027, USA  \n3 Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA  \n4 Center for Astrophysics | Harvard & Smithsonian, 60 Garden St. Cambridge, MA 02138, USA  \n5 Kapteyn Astronomical Institute, University of Groningen, P.O. BOX 800, 9700 AV Groningen, The Netherlands  \n6 Key Laboratory for Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China  \nAccepted XXX. Received YYY; in original form 2022 November 9  \nABSTRACT  \nAstronomy is presently experiencing profound growth in the deployment of machine learning to explore large datasets. However, transient quasi-periodic oscillations (QPOs) which appear in power density spectra of many X-ray binary system observations are an intriguing phenomena heretofore not explored with machine learning. In light of this, we propose and experiment with novel methodologies for predicting the presence and properties of QPOs to make the first ever detections and characterizations of QPOs with machine learning models. We base our findings on raw energy spectra and processed features derived from energy spectra using an abundance of data from the NICER and Rossi X-ray Timing Explorer space telescope archives for two black hole low mass X-ray binary sources, GRS 1915+105 and MAXI J1535-571 . We advance these non-traditional methods as a foundation for using machine learning to discover global inter-object generalizations between—and provide unique insights about—energy and timing phenomena to assist with the ongoing challenge of unambiguously understanding the nature and origin of QPOs. Additionally, we have developed a publicly available Python machine learning library, QPOML, to enable further Machine Learning aided investigations into QPOs.  \nKey words: accretion, accretion disks—black hole physics—stars: individual (GRS 1915+105, MAXI J1535+571)—  \nX-rays: binaries  \n1 INTRODUCTION  \nAt the ends of their lives, massive stars “do not go gentle into that good night”(Thomas 1952) . Instead, if their initial mass exceeds ∼ 8 M 􀀌, core collapse leads to spectacular Type II supernovae (Schlegel 1995) . If the compact remnant remains bound or becomes bound to anon-degenerate companion star, the result can be a neutron star (NS) or black hole (BH) remnant (Gilmore 2004) . In special cases, this object maintains a non-degenerate partner, and together these may forman X-ray binary (XRB) system, in which the non-degenerate star engages in mass-exchange with its compact partner (Tauris & van den Heuvel 2006) . Such systems are characterized by accretion from the donor star, through accretion disks (Shakura & Sunyaev 1973) and are the sources for jets (Gallo et al. 2005; van den Eijnden et al. 2018) and winds (Neilsen 2013; Castro Segura et al. 2022) . Additional exotic phenomena like thermonuclear surface burning (Bildsten 1998) have also been observed in neutron star binaries. Both BH and NS systems are both observed to emit thermal X-ray radiation with temperatures ∼ 1 keV that is understood to arise from the conversion of gravitational potential to radiative energy. Neutron stars can produce  \n★ E-mail: [thaddaeuskiker@protonmail.com](thaddaeuskiker@protonmail.com)  \nthermal emission at their surfaces, and the optically thick, geometrically thin accretion disks around both NSs and BHs can produce strong thermal X-ray emission (Shakura & Sunyaev 1973) . Furthermore, BH and NSXRBs both also show hard X-ray flux coming from Compton up-scattering of thermal disk emission by a cloud of hot electrons around the compact source kno","cbCaim4I2hyt8Gha","https://ap.wps.com/l/cbCaim4I2hyt8Gha","pdf",1621008,1,18,"English","en",105,"# Abstract\n# Introduction\n## X-ray binary systems and spectral states\n## QPOs and theoretical interpretations","[{\"question\":\"What problem does the study address about quasi-periodic oscillations (QPOs)?\",\"answer\":\"QPOs appear as narrow peaks in power-density spectra of many X-ray binaries, but transient QPOs have not been explored with machine learning methods in a systematic way.\"},{\"question\":\"How does the approach detect and characterize QPOs?\",\"answer\":\"The study uses machine learning models trained on raw energy spectra and processed features derived from those spectra to predict whether QPOs are present and to estimate their properties.\"},{\"question\":\"Which datasets and sources are used to support the findings?\",\"answer\":\"The analysis uses energy spectral data and features from the NICER and Rossi X-ray Timing Explorer archives, focusing on two black hole low-mass X-ray binary sources: GRS 1915+105 and MAXI J1535-571.\"}]","QPOML - A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries | PDF",1785679740,45,{"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},"qpoml-a-machine-learning-approach-to-detect-and-characterize-quasi-periodic-oscillations-in-x-ray-binaries","",{"@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/qpoml-a-machine-learning-approach-to-detect-and-characterize-quasi-periodic-oscillations-in-x-ray-binaries/117818/",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","2026-08-05","2026-08-02",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 problem does the study address about quasi-periodic oscillations (QPOs)?","Question",{"text":76,"@type":77},"QPOs appear as narrow peaks in power-density spectra of many X-ray binaries, but transient QPOs have not been explored with machine learning methods in a systematic way.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the approach detect and characterize QPOs?",{"text":81,"@type":77},"The study uses machine learning models trained on raw energy spectra and processed features derived from those spectra to predict whether QPOs are present and to estimate their properties.",{"name":83,"@type":74,"acceptedAnswer":84},"Which datasets and sources are used to support the findings?",{"text":85,"@type":77},"The analysis uses energy spectral data and features from the NICER and Rossi X-ray Timing Explorer archives, focusing on two black hole low-mass X-ray binary sources: GRS 1915+105 and MAXI J1535-571.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]