[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127970-en":3,"doc-seo-127970-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127970,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Modeling Blazar Broadband Emission with Machine Learning - Toward a Physical Interpretation of the Blazar","Blazars, a class of jetted active galactic nuclei, are the most numerous persistent extragalactic gamma-ray sources. Their double-bumped spectral energy distributions (SEDs) are commonly interpreted as non-thermal emission from a relativistic jet aligned with the line of sight. Population studies suggest a “blazar sequence”, linking the low-energy peak frequency and bolometric luminosity, though its origin remains uncertain. This thesis models TeV-detected BL Lac type SEDs using the Synchrotron Self-Compton scenario and applies analytical and machine-learning methods to identify trends and characterize spectral quantities supporting an updated TeV-inclusive sequence.","UNIVERSITÀ DEGLI STUDI DI PADOVA  \nDipartimento di Fisica e Astronomia “Galileo Galilei”  \nMaster Degree in Physics  \nFinal Dissertation  \nModeling Blazar Broadband Emission with Machine Learning: Toward a Physical Interpretation of the Blazar  \nSequence  \nThesis supervisor Candidate  \nDott.ssa Elisa Prandini Francesca Bovolon  \nThesis co-supervisor  \nDott.ssa Ilaria Viale  \nAcademic Year 2023/2024  \nA mamma, papà, e pure a Cate  \nAbstract  \nBlazars, a class of jetted active galactic nuclei, are the most numerous permanent extragalactic gamma-ray sources. Their peculiar double-bumped spectral energy distributions (SEDs) are usually interpreted as non-thermal emission from a relativistic jet of particles closely aligned with the line of sight. Population studies have highlighted a “blazar sequence”, i. e. an anticorrelation between the frequency of the low-energy peak and its bolometric luminosity. Its existence and origin are still unclear, despite the influx of new data, including in the TeV band. This work thus aims at contributing to anew sequence that finally includes very high-energy gamma-ray spectra. A number of representative SEDs from a sample of TeV-detected blazars ofthe “BL Lac” type, binned according to their low-energy peak frequencies, were modeled based on the standard“Synchrotron Self-Compton” scenario: best-fit parameters were compared to search for trends hinting at the mechanisms underlying the sequence. Different techniques, including analytical tools and machine learning, were used to characterize spectral quantities of the selected sources, and their outcomes and performances were discussed.  \nContents  \nList of Figures xi  \nList of Tables xiii  \nList of Code Snippets xvii  \nList of Acronyms xix  \n1 Blazars: Observations 1  \n1. 1 Active Galactic Nuclei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Blazars: Observational Properties ....................... 2  \n1.2.1 Radio Properties ............................ 2  \n1.2.2 Time Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.2.3 Spectral Energy Distribution ..................... 7  \n1.2.4 The Highest Energies .......................... 12  \n1.2.5 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14  \n1.3 The Blazar Sequence .............................. 16  \n2 Blazars: Theoretical Models 21  \n2.1 The Spectral Model: Synchrotron Self-Compton .............. 21  \n2.1.1 Synchrotron Emission ......................... 21  \n2.1.2 Synchrotron Self-Compton ...................... 23  \n2.1.3 Other Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26  \n2.2 The Physical Model, Part I: Unification .................... 28  \n2.3 The Physical Model, Part II: Jets ........................ 31  \n2.3.1 Accretion and the Birth of Jets .................... 31  \n2.3.2 Jet Structure ............................... 32  \n2.3.3 Acceleration Mechanisms . . . . . . . . . . . . . . . . . . . . . . . 33  \n2.4 Physics of the Blazar Sequence . . . . . . . . . . . . . . . . . . . . . . . . 35  \nCONTENTS  \n3 Spectral Modeling 39  \n3.1 Aim: the TeV Blazar Sequence ......................... 39  \n3.2 Data Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42  \n3.2. 1 Source Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42  \n3.2.2 Data Points Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 47  \n3.3 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54  \n3.3.1 agnpy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54  \n3.3.2 MMDC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56  \n3.4 Modeling ..................................... 59  \n3.4.1 Implementation ............................. 59  \n3.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61  \n3.4.3 Final Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . 76  \n4 Estimation of ","cbCaiuyr01xbNNXd","https://ap.wps.com/l/cbCaiuyr01xbNNXd","pdf",10680133,2,1,166,"English","en",105,"# Blazars: Observations\n## Active Galactic Nuclei\n## Blazars: Observational Properties\n## The Blazar Sequence\n# Blazars: Theoretical Models\n## The Spectral Model: Synchrotron Self-Compton\n## The Physical Model, Part I: Unification\n## The Physical Model, Part II: Jets\n# Spectral Modeling\n## Aim: the TeV Blazar Sequence\n## Data Selection\n## Tools\n## Modeling\n# Estimation of Spectral Quantities in Blazars with Machine Learning\n## Purpose\n## Algorithms\n## Implementation\n## Results\n# Conclusions\n# References\n# A Spectral Modeling Plots and Tables\n# B Machine Learning","[{\"question\":\"What problem does the thesis address about the blazar sequence?\",\"answer\":\"It addresses the uncertain existence and physical origin of the “blazar sequence”, including the need to extend it to very high-energy (TeV) gamma-ray spectra.\"},{\"question\":\"How are the blazar broadband emissions modeled in this work?\",\"answer\":\"Representative TeV-detected BL Lac SEDs are modeled using the Synchrotron Self-Compton scenario, with best-fit parameters compared to find trends.\"},{\"question\":\"Which machine learning methods are used to estimate spectral quantities?\",\"answer\":\"The thesis employs algorithms including Random Forest and Gradient Boosted Decision Tree variants (including histogram-based approaches), followed by training, evaluation, and prediction on new data.\"}]","Modeling Blazar Broadband Emission with Machine Learning - Toward a Physical Interpretation of the Blazar | PDF",1785943467,418,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"modeling-blazar-broadband-emission-with-machine-learning-toward-a-physical-interpretation-of-the-blazar","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/modeling-blazar-broadband-emission-with-machine-learning-toward-a-physical-interpretation-of-the-blazar/127970/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-29","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address about the blazar sequence?","Question",{"text":76,"@type":77},"It addresses the uncertain existence and physical origin of the “blazar sequence”, including the need to extend it to very high-energy (TeV) gamma-ray spectra.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the blazar broadband emissions modeled in this work?",{"text":81,"@type":77},"Representative TeV-detected BL Lac SEDs are modeled using the Synchrotron Self-Compton scenario, with best-fit parameters compared to find trends.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning methods are used to estimate spectral quantities?",{"text":85,"@type":77},"The thesis employs algorithms including Random Forest and Gradient Boosted Decision Tree variants (including histogram-based approaches), followed by training, evaluation, and prediction on new data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"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":107,"slug":139},19,"General","general"]