[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120010-en":3,"doc-seo-120010-105":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},120010,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Inferring the Assembly and Merger Histories of Galaxies - with the IllustrisTNG Simulations and Machine Learning","This thesis investigates galaxy formation and evolution by linking cosmological simulations with observational data through machine learning. Using galaxy outputs from the full IllustrisTNG simulations (TNG50 and TNG100), trained models extract assembly and merger histories and translate them into predictions for observations. The work first infers assembly/merger information from scalar, integrated galaxy features, then addresses the limitation that scalars cover only a portion of real observables. Contrastive learning is introduced to learn image representations from TNG survey-realistic mocks and Hyper Suprime-Cam (HSC) images, enabling simulation-based inference with images. A TNG-trained inference model is further applied to HSC data to retrieve ex-situ fractions and the last major merger’s time and mass, improving interpretation of observations and validating simulation realism.","Dissertation  \nsubmitted to the  \nCombined Faculty of Mathematics, Engineering and Natural Sciences of Heidelberg University, Germany for the degree of  \nDoctor of Natural Sciences  \nPut forward by  \nLukas, Eisert  \nborn in: Künzelsau  \nOral examination: 18.07.2024  \nINFERRING THE ASSEMBLY AND MERGER HISTORIES OF GALAXIES WITH THE IL LUST RIS TNG SIMULATION S AND MACHINE LEARNING  \nlukas ei sert  \nMay 2024  \nReferees:  \nDr. Annalisa Pillepich  \nApl. Prof. Andreas Koch-Hansen  \nThis thesis presents an investigation into galaxy formation and evolution, utilizing cutting-edge cosmological simulations and machine learning methodologies. Galaxy data from the full cosmological simulations TNG50 and TNG100 within the IllustrisTNG project are employed, and machine learning techniques are trained to extract the assembly and merging history of these simulated galaxies. These machine-learning models can then be applied to observational data, offering a novel method to connect simulations and observations. Initially, this is achieved by using scalars representing integrated observable galaxy features, from which we are able to infer scalars describing the assembly and merging history accurately. However, scalars encompass only a fraction of the complete observational data (images, spectra, IFUs, etc.), and accurately reconstructing scalars from simulations with the same observable bias as observations is not trivial. To address this, contrastive learning is employed to perform representation learning on both surveyrealistic mocks of TNG and observed Hyper Suprime-Cam (HSC) image data (in r, g, and i bands), ensuring comparability between simulated and observed images. Remarkably, our findings demonstrate sufficient similarity between the simulated and observed images to justify the idea of simulation-based inference with images. Subsequently, an inference model trained on TNG data is successfully applied to HSC data, allowing the retrieval of information regarding the ex-situ fraction and the time and mass of the last major merger undergone by a galaxy. This interdisciplinary approach merges the domains of cosmological simulations, observational astronomy, and machine learning, offering a new perspective on galaxy formation and evolution. The developed methodologies not only enhance our ability to interpret observational data but also enable the assessment of the realism of cosmological simulations.  \nDiese Arbeit präsentiert eine Untersuchung zur Bildung und Evolution von Galaxien unter Verwendung hochmoderner kosmologischer Simulationen und Methodendes maschinellen Lernens. Dabei werden Galaxiendaten aus den vollständigen kosmologischen Simulationen TNG50 und TNG100 des IllustrisTNG-Projekts genutzt, um maschinelles Lernen darauf zu trainieren, die Entstehungs- und Verschmelzungsgeschichte dieser simulierten Galaxien zu extrahieren. Diese maschinellen Lernmodelle können dann auf Beobachtungsdaten angewendet werden und bieten damit eine neue Methode, um Simulationen und Beobachtungen zu verbinden. Zunächst wird dies durch die Verwendung von skalaren Werten erreicht, die beobachtbare Galaxienmerkmale repräsentieren, aus denen wir dann in der Lage sind, Skalare abzuleiten, welche die Entstehungs-und Verschmelzungsgeschichte genaubeschreiben. Skalare umfassen jedoch nur einen Bruchteil der vollständigen Beobachtungsdaten (Bilder, Spektren, IFUs usw.), und die genaue Rekonstruktion von Skalaren aus Simulationen mit der gleichen beobachtbaren Verzerrung wie Beobachtungen ist nicht trivial. Um dies zu adressieren, wird kontrastives Lernen verwendet, um Repräsentationslernen sowohl auf realistischen Umfragemockups von TNG als auch auf beobachteten Hyper Suprime-Cam (HSC)-Bilddaten (in r, g undi Bändern) durchzuführen, um die Vergleichbarkeit zwischen simulierten und beobachteten Bildern sicherzustellen. Bemerkenswerterweise zeigen unsere Ergebnisse ausreichende Ähnlichkeit zwischen den simulierten und beobachteten Bildern, um die Idee der simul","cbCaieOIpjWkXi2R","https://ap.wps.com/l/cbCaieOIpjWkXi2R","pdf",43454400,1,249,"English","en",105,"# Abstract\n## Methodology: Simulation-to-observation inference\n## Representation learning with contrastive learning\n## Application to Hyper Suprime-Cam (HSC) data\n## Results and impact","[{\"question\":\"What data sources and simulations are used to infer galaxy assembly and merger histories?\",\"answer\":\"The thesis uses galaxy data from the full cosmological simulations TNG50 and TNG100 within the IllustrisTNG project, together with observational Hyper Suprime-Cam (HSC) image data.\"},{\"question\":\"How does the thesis move beyond using integrated scalar galaxy features?\",\"answer\":\"It employs contrastive learning to perform representation learning directly on survey-realistic simulation image mocks and observed HSC images, ensuring comparability between simulated and observed images.\"},{\"question\":\"What observational quantities can be recovered from HSC data using the trained inference model?\",\"answer\":\"A model trained on TNG data is applied to HSC data to retrieve the ex-situ fraction and the time and mass of the last major merger undergone by a galaxy.\"}]","Inferring the Assembly and Merger Histories of Galaxies - with the IllustrisTNG Simulations and Machine Learning | PDF",1785727695,627,{"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},"inferring-the-assembly-and-merger-histories-of-galaxies-with-the-illustristng-simulations-and-machine-learning","",{"@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/inferring-the-assembly-and-merger-histories-of-galaxies-with-the-illustristng-simulations-and-machine-learning/120010/",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","2026-08-03",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},"What data sources and simulations are used to infer galaxy assembly and merger histories?","Question",{"text":75,"@type":76},"The thesis uses galaxy data from the full cosmological simulations TNG50 and TNG100 within the IllustrisTNG project, together with observational Hyper Suprime-Cam (HSC) image data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis move beyond using integrated scalar galaxy features?",{"text":80,"@type":76},"It employs contrastive learning to perform representation learning directly on survey-realistic simulation image mocks and observed HSC images, ensuring comparability between simulated and observed images.",{"name":82,"@type":73,"acceptedAnswer":83},"What observational quantities can be recovered from HSC data using the trained inference model?",{"text":84,"@type":76},"A model trained on TNG data is applied to HSC data to retrieve the ex-situ fraction and the time and mass of the last major merger undergone by a galaxy.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]