{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/386266"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/386266","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Simulation-based Bayesian machine learning methods for Cosmology and beyond","abstract":"This thesis presents a newly developed algorithm PolySwyft. This sequential simulation- based nested sampler is motivated by the limitations of likelihood-based Bayesian inference in sky-averaged 21-cm Cosmology. Moreover, PolySwyft merges nested sampling and neural ratio estimation into a general Bayesian framework, and the method is a general-purpose algorithm applicable beyond Cosmology. This thesis is divided into five sections. In the first chapter, I elaborate on the physics background of 21-cm Cosmology and its current challenges and issues on sky-averaged 21-cm parameter inference, identified as theoretical, experimental, or of statistical origin. As this thesis focuses on the data analytical aspect of sky-averaged 21-cm signal parameter inference, I introduce the fundamental principles of Bayesian inference and its algorithmic tools used in practice in chapter two. I elaborate on nested sampling and neural networks, two algorithmic methods commonly used in current cosmological inference. Moreover, I will introduce Simulation-Based Inference (SBI), an emerging statistical paradigm within Cosmology, and I will present Neural Ratio Estimation (NRE) as a method used in SBI for Cosmology. These methods are the algorithmic tools I will utilize throughout this thesis. The third chapter is on the data analysis of simulated sky-averaged 21-cm signal datasets using the REACH radio instrument. I probe artificially injected physical and statistical systematics effects on 21-cm signal parameter inference and its implications on cosmological model comparison. The fourth chapter stems from the data analytical limitations discovered in chapter three. To mitigate these statistical limitations, I apply SBI and present a novel method PolySwyft that merges nested sampling and NREs (more broadly, SBI) into a general Bayesian frame- work. I apply this new algorithm on 100 (data) times 5 (parameter) dimensional toy problems with known analytical ground truth solutions and a CMB power spectrum toy problem. Finally, in the fifth chapter, I elaborate on future research directions that this thesis and method can motivate for subsequent work.","abstract_html":"This thesis presents a newly developed algorithm PolySwyft. This sequential simulation- based nested sampler is motivated by the limitations of likelihood-based Bayesian inference in sky-averaged 21-cm Cosmology. Moreover, PolySwyft merges nested sampling and neural ratio estimation into a general Bayesian framework, and the method is a general-purpose algorithm applicable beyond Cosmology. This thesis is divided into five sections. In the first chapter, I elaborate on the physics background of 21-cm Cosmology and its current challenges and issues on sky-averaged 21-cm parameter inference, identified as theoretical, experimental, or of statistical origin. As this thesis focuses on the data analytical aspect of sky-averaged 21-cm signal parameter inference, I introduce the fundamental principles of Bayesian inference and its algorithmic tools used in practice in chapter two. I elaborate on nested sampling and neural networks, two algorithmic methods commonly used in current cosmological inference. Moreover, I will introduce Simulation-Based Inference (SBI), an emerging statistical paradigm within Cosmology, and I will present Neural Ratio Estimation (NRE) as a method used in SBI for Cosmology. These methods are the algorithmic tools I will utilize throughout this thesis. The third chapter is on the data analysis of simulated sky-averaged 21-cm signal datasets using the REACH radio instrument. I probe artificially injected physical and statistical systematics effects on 21-cm signal parameter inference and its implications on cosmological model comparison. The fourth chapter stems from the data analytical limitations discovered in chapter three. To mitigate these statistical limitations, I apply SBI and present a novel method PolySwyft that merges nested sampling and NREs (more broadly, SBI) into a general Bayesian frame- work. I apply this new algorithm on 100 (data) times 5 (parameter) dimensional toy problems with known analytical ground truth solutions and a CMB power spectrum toy problem. Finally, in the fifth chapter, I elaborate on future research directions that this thesis and method can motivate for subsequent work.","abstract_has_math":false,"creators":["Scheutwinkel, Kilian Hikaru"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["De Lera Acedo, Eloy","Handley, Will"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-10-26","date_published":"2024-10-26","updated_at":"2026-07-22T22:23:54Z","subjects":["21-cm cosmology","astrophysics","bayesian data analysis","bayesian inference","cosmology","high dimensional inference","likelihood-free inference","machine learning","monte carlo methods","nested sampling","neural ratio estimation","numerical methods","parallel computing","physics","radio cosmology","REACH","sequential methods","simulation-based inference","statistical methods"],"languages":[],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/3df61411-9ca0-4200-890e-fcb1a36ef138/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.119569","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["De Lera Acedo, Eloy","Handley, Will"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Hans Werthén Foundation PhD enrichment scheme by the Alan Turing Institute PhD grant by G-Research"]},{"key":"dc:creator","label":"Author","values":["Scheutwinkel, Kilian Hikaru"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-10-26"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/386266"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["21-cm cosmology","astrophysics","bayesian data analysis","bayesian inference","cosmology","high dimensional inference","likelihood-free inference","machine learning","monte carlo methods","nested sampling","neural ratio estimation","numerical methods","parallel computing","physics","radio cosmology","REACH","sequential methods","simulation-based inference","statistical methods"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/3df61411-9ca0-4200-890e-fcb1a36ef138/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.119569"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/425cc84b-33a6-4226-869c-9b73aa66eb20/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents a newly developed algorithm PolySwyft. 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Moreover, I will introduce Simulation-Based Inference (SBI), an emerging statistical paradigm within Cosmology, and I will present Neural Ratio Estimation (NRE) as a method used in SBI for Cosmology. These methods are the algorithmic tools I will utilize throughout this thesis. The third chapter is on the data analysis of simulated sky-averaged 21-cm signal datasets using the REACH radio instrument. I probe artificially injected physical and statistical systematics effects on 21-cm signal parameter inference and its implications on cosmological model comparison. The fourth chapter stems from the data analytical limitations discovered in chapter three. To mitigate these statistical limitations, I apply SBI and present a novel method PolySwyft that merges nested sampling and NREs (more broadly, SBI) into a general Bayesian frame- work. 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Moreover, I will introduce Simulation-Based Inference (SBI), an emerging statistical paradigm within Cosmology, and I will present Neural Ratio Estimation (NRE) as a method used in SBI for Cosmology. These methods are the algorithmic tools I will utilize throughout this thesis. The third chapter is on the data analysis of simulated sky-averaged 21-cm signal datasets using the REACH radio instrument. I probe artificially injected physical and statistical systematics effects on 21-cm signal parameter inference and its implications on cosmological model comparison. The fourth chapter stems from the data analytical limitations discovered in chapter three. To mitigate these statistical limitations, I apply SBI and present a novel method PolySwyft that merges nested sampling and NREs (more broadly, SBI) into a general Bayesian frame- work. 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