[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123695-en":3,"doc-seo-123695-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123695,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Accelerating gravitational-wave inference with machine learning - PhD thesis","Gravitational-wave astronomy faces a rapidly expanding data volume from ground- and space-based interferometers, creating major demands for faster analysis. Source characterization relies on Bayesian inference, where Markov Chain Monte Carlo and Nested Sampling are accurate but computationally expensive and slow to scale with added physics such as higher-order modes, precession, and eccentricity. This thesis presents machine-learning-accelerated nested sampling methods that act as drop-in replacements, including nessai and i-nessai, validated on simulated black hole and neutron star populations as well as LVK observing-run events.","Williams, Michael J. (2023) Accelerating gravitational-wave inference with machine learning. PhD thesis.  \n[https://theses.gla.ac.uk/83924/](https://theses.gla.ac.uk/83924/)  \nCopyright and moral rights for this work are retained by the author  \nA copy can be downloaded for personal non-commercial research or study, without prior permission or charge  \nThis work cannot be reproduced or quoted extensively from without first obtaining permission from the author  \nThe content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author  \nWhen referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given  \nEnlighten: Theses  \n[https://theses.gla.ac.uk/](https://theses.gla.ac.uk/)  \n[research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk)  \nAccelerating gravitational-wave inference with  \nmachine learning  \nMichael J. Williams  \nSubmitted in fulfilment of the requirements for the Degree of Doctor of Philosophy  \nSchool of Physics & Astronomy College of Science and Engineering University of Glasgow  \nOctober 2023  \nAbstract  \nThe future for gravitational-wave astronomy is bright, with improvements for existing ground-based interferometers of the LIGO-Virgo-KAGRA Collaboration (LVK) and new ground- and space-based interferometers planned for the near future. As a result, there will imminently be an abundance of data to analyse from these detectors, which will bring with it the chances to probe new regimes. However, this will also bring with it new challenges to address, such as the volume of data and need for new analysis techniques.  \nLeveraging this data hinges on our ability to determine the characteristics of the sources that produce the observed gravitational-wave signals, and Bayesian inference is the method of choice. The main algorithms that have been used in these analyses are Markov Chain Monte Carlo and Nested Sampling. Each have their own advantages and disadvantages. However, both are computationally expensive when applied to gravitational-wave inference, typically taking of order days to weeks for shorter signals and up to months for longer signals, such as those from binary neutron star mergers. Furthermore, the cost of these analyses increases as additional physics is included, such as higher-order modes, precession and eccentricity. These factors, combined with the previously mentioned increase in data, and therefore number of signals, pose a significant challenge. As such, there is a need for faster and more efficient algorithms for gravitational-wave inference. In this work, we present novel algorithms that serve as drop-in replacements for existing approaches but can accelerate inference by an order of magnitude.  \nOur initial approach is to incorporate machine learning into an existing algorithm, namely nested sampling, with the aim of accelerating it whilst leaving the underlying algorithm unchanged. To this end, we introduce nessai, a nested sampling algorithm that includes a novel method for sampling from the likelihood-constrained prior that leverages normalizing flows, a type of machine learning algorithm. Normalizing flows can approximate the distribution of live points during a nested sampling run, and allow for new points to be drawn from it. They are also flexible and can learn complex correlations, thus eliminating the need to use a random walk to propose new samples.  \nWe validate nessai for gravitational-wave inference by analysing a population of simulated binary black holes (BBHs) and demonstrate that it produces statistically consistent results. We also compare nessai to dynesty, the standard nested sampling algorithm  \nABSTRACT ii  \nused by the LVK, and find that, after some improvements, it is on average ∼ 6 times more efficient and enables inference in time scales of order 10 hours on a single core. We also highlight other advantages of nessai, such as the incl","cbCaicEXiRHBQe7w","https://ap.wps.com/l/cbCaicEXiRHBQe7w","pdf",37988028,1,331,"English","en",105,"# Abstract\n## Challenges in gravitational-wave Bayesian inference\n## Machine-learning approaches to nested sampling\n## nessai: normalizing-flow nested sampling\n## i-nessai: tailored importance nested sampling\n## Validation on simulated and real LVK data","[{\"question\":\"Why does gravitational-wave inference require faster algorithms?\",\"answer\":\"Bayesian inference methods like Nested Sampling become computationally expensive as data volumes grow and as additional physics is included, making typical runs take from days to weeks or even months.\"},{\"question\":\"What is nessai and how does it accelerate inference?\",\"answer\":\"nessai is a nested sampling algorithm that incorporates normalizing flows to sample from the likelihood-constrained prior, approximating the live-point distribution and reducing reliance on slow proposal strategies.\"},{\"question\":\"How does i-nessai differ from nessai?\",\"answer\":\"i-nessai uses a custom nested sampling formulation tailored to normalizing flows, addressing bottlenecks by avoiding ordering and prior-distribution requirements and enabling evidence updates with batches of samples.\"},{\"question\":\"How were the proposed methods validated?\",\"answer\":\"The thesis validates nessai and i-nessai using simulated binary black hole toy problems and signal populations, and applies them to real LVK observing-run data, finding consistent agreement with published LVK results.\"}]","Accelerating gravitational-wave inference with machine learning - PhD thesis | PDF",1785818059,834,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"accelerating-gravitational-wave-inference-with-machine-learning-phd-thesis","",{"@graph":36,"@context":89},[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/accelerating-gravitational-wave-inference-with-machine-learning-phd-thesis/123695/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why does gravitational-wave inference require faster algorithms?","Question",{"text":75,"@type":76},"Bayesian inference methods like Nested Sampling become computationally expensive as data volumes grow and as additional physics is included, making typical runs take from days to weeks or even months.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is nessai and how does it accelerate inference?",{"text":80,"@type":76},"nessai is a nested sampling algorithm that incorporates normalizing flows to sample from the likelihood-constrained prior, approximating the live-point distribution and reducing reliance on slow proposal strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"How does i-nessai differ from nessai?",{"text":84,"@type":76},"i-nessai uses a custom nested sampling formulation tailored to normalizing flows, addressing bottlenecks by avoiding ordering and prior-distribution requirements and enabling evidence updates with batches of samples.",{"name":86,"@type":73,"acceptedAnswer":87},"How were the proposed methods validated?",{"text":88,"@type":76},"The thesis validates nessai and i-nessai using simulated binary black hole toy problems and signal populations, and applies them to real LVK observing-run data, finding consistent agreement with published LVK results.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]