[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124843-en":3,"doc-seo-124843-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124843,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning for Multi-Messenger Astronomy - Doctoral Dissertation","Direct observation of gravitational waves (GWs) in 2015 initiated a new era of GW astronomy, enabling independent constraints on the Universe when combined with electromagnetic (EM) observations. The thesis analyzes multi-messenger events, showing how GW170817 can be used to address systematics in Hubble-constant estimates, and proposes a Bayesian model for an unbiased H0. It further develops machine-learning detection and classification for core-collapse supernovae signals and identifies anomalous spectra in DESI data using variational autoencoders.","Machine Learning for Multi-Messenger Astronomy  \nConstantina Nicolaou  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nof  \nUniversity College London.  \nDepartment of Physics and Astronomy University College London  \nOctober 8, 2023  \n2  \n3  \nI, Constantina Nicolaou, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nAbstract  \nThe direct observation of gravitational waves (GWs) in 2015 marked the beginning of a new era of GW astronomy, unlocking an independent probe for studying the Universe. GW170817, was the first event detected in both GWs and electromagnetic (EM) observations. The implications of this multi-messenger event in the field of physics are far-reaching.  \nMulti-messenger events can independently estimate the Hubble constant. In Chapter 2, I demonstrate the presence of a potential systematic error associated with the peculiar velocity of the host galaxy of nearby GWs, biasing the H0 estimate. I study the GW170817 event and formulate a Bayesian model that accounts for this error. Under the proposed model an unbiased estimate of the Hubble constant from nearby GW sources is obtained, H0 = 68.61845.0 kms−1Mpc−1, which is crucial when considering the H0 tension.  \nIn Chapter 3, I present the study of detecting and classifying GWs from corecollapse supernovae (CCSNe), which are promising multi-messenger events yet tobe observed. Simulated CCSNe signals were injected into real detector noise data. I implement a two-step approach comprised of wavelet-based transient detection and machine learning for classification. I compared the performance of 1D, 2D CNNs (convolutional neural networks) and LSTM (long short-term memory) and showed that 2D CNNs perform the best overall.  \nLarge galaxy surveys, play an instrumental role in EM observations of multimessenger events and studies of their properties. DESI is expected to observe 35 million galaxies. In Chapter 4, I apply Variational Autoencoders to detect anomalous spectra in DESI data. The dataset used in this analysis is composed of ∼ 208 , 000  \n6 Abstract  \nspectra. The outliers identified fall into two broad categories: spectra with unique physical features and spectra with artefacts. The latter can be used to improve the DESI spectroscopic pipeline while the former can lead to the identification of transients, unusual objects and potential scientific discoveries.  \nImpact Statement  \nThe work presented in this thesis focuses on the analysis of gravitational wave (GW) events for estimating cosmological parameters, the detection and classification of GWs from core-collapse supernovae (CCSNe) and the identification of anomalous objects or events in large spectroscopic surveys.  \nThe work discussed in Chapter 2, has a direct impact in the estimation of the Hubble constant, H0, from GWs. The current estimates of H0 from the cosmic microwave background and the cosmic distance ladder are in tension. GWs offer an entirely new and independent method for estimating GWs which can help elucidate the tension. While currently, H0 estimates from GWs are broad, expected GW observations in the next few years promise constraints similar to current methods. For GWs to shed light on this tension, they need to be free of systematic errors and offer an unbiased estimate of H0. In our work we demonstrate a previously unaccounted for systematic uncertainty arising from the estimation of the peculiar velocity of the host galaxy of nearby GWs, and propose a method to mitigate this.  \nThe work presented in Chapter 3, can facilitate the future detection of GWs from core collapse supernovae (CCSNe) . We implement a two-step approach for detecting and classifying GW signals from CCSNe using a wavelet decomposition filter and machine learning. Beyond the direct impact in the discovery of such signals, the approach presen","cbCaigJ88CUDByB0","https://ap.wps.com/l/cbCaigJ88CUDByB0","pdf",13236097,1,232,"English","en",105,"# Abstract\n## Hubble-constant estimation with multi-messenger events\n## GW detection and classification for core-collapse supernovae\n## Anomalous spectra detection in DESI using variational autoencoders\n## Impact Statement","[{\"question\":\"How are anomalous spectra detected in DESI data and what are the outcomes?\",\"answer\":\"Variational autoencoders are applied to identify anomalous spectra in a DESI dataset of about 208,000 spectra. Outliers are grouped into spectra with unique physical features and spectra with artefacts, with both categories useful for discovery and pipeline improvement.\"}]","Machine Learning for Multi-Messenger Astronomy - Doctoral Dissertation | PDF",1785894956,585,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-for-multi-messenger-astronomy-doctoral-dissertation","",{"@graph":36,"@context":77},[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/machine-learning-for-multi-messenger-astronomy-doctoral-dissertation/124843/",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],{"name":72,"@type":73,"acceptedAnswer":74},"How are anomalous spectra detected in DESI data and what are the outcomes?","Question",{"text":75,"@type":76},"Variational autoencoders are applied to identify anomalous spectra in a DESI dataset of about 208,000 spectra. Outliers are grouped into spectra with unique physical features and spectra with artefacts, with both categories useful for discovery and pipeline improvement.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]