[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126144-en":3,"doc-seo-126144-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126144,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Machine Learning Techniques To Young Stellar Object Classification - Master of Science Thesis","Among the earliest public observations from the James Webb Space Telescope, this thesis analyzes the star-forming region NGC 3324, colloquially known as the “Cosmic Cliffs.” A photometric catalog is built and processed with a Probabilistic Random Forest machine learning approach to identify young stellar objects (YSOs). The work finds 496 YSOs within 19,497 objects, with 474 previously unreported, and estimates a local star formation rate of 1×10–4 M⊙/yr. Results show YSO surface density aligns with Herschel-derived column densities (up to 1.37×1022 cm–2), demonstrating improved detection in dusty regions compared with Spitzer.","Application of Machine Learning Techniques To Young Stellar Object Classification  \nby  \nBreanna Lydia Crompvoets  \nB.Sc., University of Regina, 2021  \nA Thesis Submitted in Partial Fulfillment of the  \nRequirements for the Degree of  \nMASTER OF SCIENCE  \nin the Department of Physics and Astronomy  \n© Breanna Lydia Crompvoets, 2023  \nUniversity of Victoria  \nAll rights reserved. This Thesis may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author.  \nii  \nApplication of Machine Learning Techniques To Young Stellar Object Classification  \nby  \nBreanna Lydia Crompvoets  \nB.Sc., University of Regina, 2021  \nSupervisory Committee  \nDr. J. Di Francesco, Co-Supervisor (Department of Physics and Astronomy)  \nDr. J. Willis, Co-Supervisor  \n(Department of Physics and Astronomy)  \niii  \nAbstract  \nAmong the first observations released to the public from the James Webb Space Telescope (JWST) was a section of the star-forming region NGC 3324 known colloquially as the “Cosmic Cliffs.” We build a photometric catalog of the region and analyze these data using the Probabilistic Random Forest machine learning method. We find 496 YSOs out of 19 497 total objects within the field, 474 of which have not been found in previous works. Using the obtained probabilities of objects being YSOs, we employ a Monte Carlo approach to determine a local star formation rate of 1 × 10–4 M⊙/yr, for the region. We also find that the surface density of YSOs in the Cosmic Cliffs is largely coincident with column densities derived from Herschel data, up to a column density of 1 .37 × 1022 cm–2 . The newly determined number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that JWST is far more capable of detecting YSOs in dusty regions than Spitzer.  \niv  \nTable of Contents  \nSupervisory Committee ii  \nAbstract iii  \nTable of Contents iv  \nList of Tables vi  \nList of Figures viii  \nAcknowledgements xi  \nDedication xii  \n1 Introduction 1  \n1.1 Star Formation .................................. 2  \n1.2 YSO Identification ................................ 5  \n1.3 Machine Learning Tools ............................. 10  \n1.3.1 The Various Methods ........................... 10  \n1.3.2 The Probabilistic Random Forest Method ............... 12  \n1.3.3 Previous works .............................. 13  \n2 Finding YSOs Within Webb Data, a Case Study of NGC 3324 16  \n2.1 Introduction .................................... 16  \n2.2 Background .................................... 18  \n2.2.1 NGC 3324 and Gum 31 ......................... 18  \n2.2.2 JWST Data ................................ 20  \n2.3 Methodology ................................... 21  \n2.3.1 Catalog Creation ............................. 21  \n2.3.2 Testing Validity of Probabilistic Random Forest method ....... 23  \nv  \n2.3.3 Applying the Probabilistic Random Forest method .......... 26  \n2.4 Results ....................................... 31  \n2.4.1 Comparison to Previous Works ..................... 35  \n2.5 Discussion ..................................... 40  \n2.5.1 CMDs and CCDs ............................. 40  \n2.5.2 YSO Surface Density ........................... 44  \n2.6 Conclusions .................................... 48  \n3 Future Work 51  \n3.1 Analyzing MIRI Data ............................... 51  \n3.2 Other Star-Forming Regions ........................... 53  \n3.3 Monoceros R2 Giant Molecular Cloud ...................... 54  \n3.4 Further Compelling Avenues ........................... 57  \nBibliography 60  \nA Photometry 66  \nA.1 Preparing the data (from JWST MAST files) ................. 66  \nA.2 Running DAOPHOT ............................... 67  \nA.3 Analyzing Photometry .............................. 70  \nB PRF Code 75  \nC Outflow driving sources in the Cosmic Cliffs 77  \nD Sample of Magnitudes and Probabilities for some cYSOs 80  \nvi  \nList of Tables  \nTable 2.1 JWST filters used by ERO to image NGC 3324, their exposure times, and thei","cbCaiccwETEcPybH","https://ap.wps.com/l/cbCaiccwETEcPybH","pdf",24156774,5,1,107,"English","en",105,"# 1 Introduction\n## 1.1 Star Formation\n## 1.2 YSO Identification\n## 1.3 Machine Learning Tools\n# 2 Finding YSOs Within Webb Data, a Case Study of NGC 3324\n## 2.1 Introduction\n## 2.2 Background\n## 2.3 Methodology\n## 2.4 Results\n## 2.5 Discussion\n# 3 Future Work","[{\"question\":\"What machine learning method is used to classify young stellar objects in the thesis?\",\"answer\":\"The thesis uses a Probabilistic Random Forest method to assign probabilities that photometric objects are YSOs.\"},{\"question\":\"How many young stellar objects are found in the NGC 3324 “Cosmic Cliffs” field?\",\"answer\":\"The method identifies 496 YSOs out of 19,497 total objects, including 474 not previously reported in earlier works.\"},{\"question\":\"What star formation rate and comparison are derived from the YSO probabilities?\",\"answer\":\"A Monte Carlo approach yields a local star formation rate of 1×10–4 M⊙/yr, and the thesis finds that YSO surface density largely matches Herschel column densities up to 1.37×10^22 cm–2.\"}]","Application of Machine Learning Techniques To Young Stellar Object Classification - Master of Science Thesis | PDF",1785903386,270,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"application-of-machine-learning-techniques-to-young-stellar-object-classification-master-of-science-thesis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/application-of-machine-learning-techniques-to-young-stellar-object-classification-master-of-science-thesis/126144/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What machine learning method is used to classify young stellar objects in the thesis?","Question",{"text":77,"@type":78},"The thesis uses a Probabilistic Random Forest method to assign probabilities that photometric objects are YSOs.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How many young stellar objects are found in the NGC 3324 “Cosmic Cliffs” field?",{"text":82,"@type":78},"The method identifies 496 YSOs out of 19,497 total objects, including 474 not previously reported in earlier works.",{"name":84,"@type":75,"acceptedAnswer":85},"What star formation rate and comparison are derived from the YSO probabilities?",{"text":86,"@type":78},"A Monte Carlo approach yields a local star formation rate of 1×10–4 M⊙/yr, and the thesis finds that YSO surface density largely matches Herschel column densities up to 1.37×10^22 cm–2.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]