[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124445-en":3,"doc-seo-124445-105":29,"detail-sidebar-cat-0-en-105":90},{"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":21,"html_lang":23,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124445,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Unveiling the lowest surface brightness regions in dwarf galaxies - a machine learning approach with Gaia early DR3","Recent observations suggest dwarf galaxies can host extremely faint, extended tidal structures or debris from disrupted systems, which are difficult to identify yet carry information about past interactions. This thesis develops and compares machine-learning methods to compute membership probabilities for individual stars in Local Group dwarf galaxies, with emphasis on recovering stars belonging to outer halos and tidal tails. Using four Gaia eDR3-based mock catalogs with differing foreground conditions, three ML-driven modeling strategies are tested. The normalizing flow approach most consistently recovers external structures.","UNIVERSITÀ DEGLI STUDI DI PADOVA Dipartimento di Fisica e Astronomia 􀀐Galileo Galilei􀀑  \nMaster Degree in Astrophysics and Cosmology  \nMaster Thesis  \nUnveiling the lowest surface brightness regions in dwarf galaxies:  \na machine learning approach with Gaia early DR3  \nSupervisor Candidate  \nDr. Giulia Rodighiero Marco Boscato  \nCo-supervisor Matricola 2096921  \nDr. Giuseppina Battaglia Co-supervisor  \nDr. Giuliano Iorio  \nAccademic Year 2024/2025  \nAbstract  \nRecent observations suggest that dwarf galaxies appear to host extended tidal structures or debris from disrupted systems that contribute to their stellar halos. These features are extremely challenging to identify, yet they are relics of past interactions and provide crucial constraints on the nature of dark matter and the evolutionary history of galaxies.  \nIn this thesis, I investigated di􀀛erent methods to calculate membership probabilities for individual stars in dwarf galaxies of the Local Group, with a particular focus on identifying members in external structures such as tidal tails and outer halos. The analysis was performed on four mock catalogs constructed from Gaia eDR3, designed to reproduce di􀀛erent foreground conditions and dwarf galaxies, including cases with tidal tails. Three approaches were explored, all implemented within a machine learning framework but di􀀛ering in the way the data were modeled: (i) an updated, machine learning based version of the probabilistic method used in Battaglia et al.(2022), (ii) a dimensionality-reduction approach, and (iii) a normalizing 􀀝ow model capable of mapping complex distributions into simpler ones.  \nAll methods demonstrated a high capacity for detecting true members; however, the 􀀜rst two primarily identi􀀜ed stars in the central regions of the galaxies, whereas the normalizing 􀀝ow method was the only one able to consistently recover the external structures, highlighting its potential as a powerful tool for probing the outskirts of dwarf galaxies.  \nContents  \n1 Introduction 5  \n2 Data 9  \n2.1 Local Group dwarf galaxies in Gaia eDR3 ................ 9  \n2.2 Mock catalogs ................................ 11  \n3 Methods 15  \n3.1 Battaglia 2022 ............................... 17  \n3.1.1 Spatial distribution ......................... 17  \n3.1.2 Proper motion distribution .................... 18  \n3.1.3 Color-magnitude distribution ................... 19  \n3.2 B22 with ML ................................ 20  \n3.2.1 Data preparation .......................... 20  \n3.2.2 Training the model ......................... 23  \n3.2.3 Making prediction ......................... 23  \nFirst Level: ............................. 25  \nSecond Level: ............................ 25  \nThird Level: ............................. 25  \n3.3 Dimensional Reduction ........................... 26  \n3.3.1 Data Preparation .......................... 27  \n3.3.2 Single UMAP application ..................... 27  \n3.3.3 Multiple UMAP application .................... 28  \n3.4 Normalizing Flow .............................. 28  \n3.4.1 Basic logic of NFs ......................... 29  \n3.4.2 Real NVP .............................. 31  \n3.4.3 Application of the NF algorithm ................. 32  \nNormalization phase ........................ 33  \nDensity 􀀜tting phase-GMM ................... 35  \nBayesian inference phase ...................... 37  \n4 Results 40  \n4.1 B22 vs B22 ML ............................... 40  \n4.1.1 First Level ............................. 42  \n4.1.2 Second Level ............................ 44  \n4.1.3 Third Level ............................. 46  \n4.2 Dimensional Reduction: UMAP ...................... 47  \n4.2.1 SculptorInSextans mock catalog ................. 47  \n4.2.2 Sextans mock catalogs ....................... 49  \n4.2.3 DracoInDraco mock catalog .................... 49  \n4.2.4 UMAP without spatial information ................ 50  \n4.3 Normalizing Flow .............................. 52  \n4.3.1 SculptorInSextans mock catalog .....","cbCaipDZAi7apCfK","https://ap.wps.com/l/cbCaipDZAi7apCfK","pdf",21411224,1,105,"English","en","# Introduction\n## Dwarf galaxy definition and formation context\n# Data\n## Local Group dwarf galaxies in Gaia eDR3\n## Mock catalogs\n# Methods\n## Battaglia 2022 method\n## Battaglia 2022 with machine learning\n## Dimensionality reduction (UMAP)\n## Normalizing flow (Real NVP)\n# Results\n## B22 vs B22 ML\n## Dimensional reduction with UMAP\n## Normalizing flow results\n# Discussion and conclusions\n# Appendix","[{\"question\":\"What scientific challenge motivates this thesis?\",\"answer\":\"Detecting the lowest surface brightness regions in dwarf galaxies, such as extended tidal tails and outer-halo structures, is extremely challenging observationally but crucial for understanding galaxy evolution and dark matter constraints.\"},{\"question\":\"How is the membership probability for individual stars computed?\",\"answer\":\"The work tests three machine-learning-based strategies to model data and estimate which stars are genuine members of dwarf galaxies, including members in external structures like tidal tails and outer halos.\"},{\"question\":\"Which method performs best for recovering external structures?\",\"answer\":\"The normalizing flow approach is the only method that consistently recovers external structures, while the first two approaches mainly identify stars in the central regions.\"}]","Unveiling the lowest surface brightness regions in dwarf galaxies - a machine learning approach with Gaia early DR3 | PDF",1785822334,265,{"code":4,"msg":30,"data":31},"ok",{"site_id":21,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"unveiling-the-lowest-surface-brightness-regions-in-dwarf-galaxies-a-machine-learning-approach-with-gaia-early-dr3","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/unveiling-the-lowest-surface-brightness-regions-in-dwarf-galaxies-a-machine-learning-approach-with-gaia-early-dr3/124445/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What scientific challenge motivates this thesis?","Question",{"text":74,"@type":75},"Detecting the lowest surface brightness regions in dwarf galaxies, such as extended tidal tails and outer-halo structures, is extremely challenging observationally but crucial for understanding galaxy evolution and dark matter constraints.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is the membership probability for individual stars computed?",{"text":79,"@type":75},"The work tests three machine-learning-based strategies to model data and estimate which stars are genuine members of dwarf galaxies, including members in external structures like tidal tails and outer halos.",{"name":81,"@type":72,"acceptedAnswer":82},"Which method performs best for recovering external structures?",{"text":83,"@type":75},"The normalizing flow approach is the only method that consistently recovers external structures, while the first two approaches mainly identify stars in the central regions.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":21},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]