[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125605-en":3,"doc-seo-125605-105":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},125605,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Exploring TeV candidates of Fermi blazars through machine learning - Logistic Regression (LR) selection and TeV candidate identification","Supervised machine learning is applied using a Logistic Regression (LR) model to select TeV blazar candidates from multiple Fermi catalogs (4FGL-DR2/4LAC-DR2, 3FHL, 3HSP, 2BIGB). LR learns a feature hyperplane from optimized parameters and outputs a logistic probability that a source is a TeV candidate. Sources with logistics above 80% among non-TeV blazars are treated as high-confidence TeV candidates. The method identifies 40 high-confidence candidates and builds broadband SEDs to test detectability with instruments, finding 7 likely detectable by existing/upcoming IACT observatories and 1 via EAS particle detector arrays.","arXiv :2303 . 10557v1 [ astro-ph .HE] 19 Mar 2023  \nDRAFT VERSION MARCH 21, 2023  \nTypeset using LATEX default style in AASTeX631  \nExploring TeV candidates ofFermi blazars through machine learning  \nJ. T. ZHU , 1, 2, 3, 4 C. LIN , 1, 3, 4 H. B. XIAO ,5 J. H. FAN , 1, 3, 4 D. BASTIERI , 1, 2 AND G. G. WANG1, 3, 4  \n1 Center for Astrophysics, Guangzhou University, Guangzhou 510006, China  \n2 Department of Physics and Astronomy “G. Galilei”, University of Padova, Padova I-35131, Italy  \n3 Astronomy Science and Technology Research Laboratory of Department of Education of Guangdong Province, Guangzhou 510006, China  \n4 Key Laboratory for Astronomical Observation and Technology of Guangzhou, Guangzhou 510006, China  \n5 Shanghai Key Lab for Astrophysics, Shanghai Normal University Shanghai, 200234, China  \n(Received XXX; Revised YYY; Accepted ZZZ)  \nABSTRACT  \nIn this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a selection of optimal parameters, named features, and hyper-parameters whose values control the learning process and determine the values of features that a learning algorithm ends up learning, to discriminate TeV blazars from non-TeV blazars. In addition, it gives the probability (or logistic) that a source may be considered as a TeV blazar candidate. Non-TeV blazars with logistics greater than 80% are considered high-confidence TeV candidates. Using this technique, we identify 40 high-confidence TeV candidates from the 4FGL-DR2 / 4LAC-DR2 blazars and we build the feature hyper-plane to distinguish TeV and non-TeV blazars. We also calculate the hyper-planes for the 3FHL, 3HSP, and 2BIGB. Finally, we construct the broadband spectral energy distributions (SED) for the 40 candidates, testing for their detectability with various instruments. We find that 7 of them are likely to be detected by existing or upcoming IACT observatories, while  \n1 could be observed with EAS particle detector arrays.  \nKeywords: AGN; galaxies-active; galaxies-Quasars; galaxies-BL Lacertae objects; data analysis; machine learning  \n1. INTRODUCTION  \nBlazars, an extreme subclass of active galactic nuclei (AGNs), are known for prominent observation properties, such as high energy 􀀍-ray emissions, rapid and significant amplitude variability, high luminosity, high and variable polarization, and superluminal motions, etc. (Wills et al. 1992 ; Urry & Padovani 1995 ; Fan 2002 ; Villata et al. 2006 ; Fan et al. 2014 ; Xiao et al. 2015 ; Gupta et al. 2016 ; Xiao et al. 2019 ; Abdollahi et al. 2020 ; Xiao et al. 2020 ; Fan et al. 2021) . Blazars are historically subdivided into two main categories based on the equivalent width (EW) of the optical emission lines: flat spectrum radio quasars (FSRQs)  \n􀀗  \nand BL Lacertae objects (BL Lacs) . In general, FSRQs show an EW greater than 5 A, while BL Lacs illustrate no or weak emis-  \n􀀗  \nsion lines, with EW less than 5 A. Meanwhile, the spectral energy distributions (SEDs) of blazars are generally characterized by two well-separated bumps, a low-energy one is in the infrared to soft X-ray energy range due to synchrotron emission, and a high-energy one is in the region between hard X-ray to 􀀍-ray that is associated with an inverse Compton (IC) radiation according to leptonic model (IC; e.g., Sikora et al. 1994a) . The seed photons undergoing IC scattering could be from the same electron population producing the synchrotron bump in the so-called self-Compton (SSC) model (Ghisellini et al. 1985 ; Maraschi et al. 1992 ; Bloom & Marscher 1996), e.g. 1ES 0347-121, 1ES 0229+200 (Costamante et al. 2018 ; Aharonian et al. 2007a,b), or from external regions (External Compton model, EC), e.g., from the accretion disk (Dermer & Schlickeiser 1993), broad line region (Sikora et al. 1994b), and dust torus (Bła˙zejowski et al. 2000) . While the hadronic proces","cbCaic396eMbCV3b","https://ap.wps.com/l/cbCaic396eMbCV3b","pdf",1645778,1,24,"English","en",105,"# Abstract\n# 1. Introduction\n## Blazars and observational properties\n## SED modeling and emission mechanisms\n## BL Lac subclasses and classification schemes","[{\"question\":\"What machine learning approach is used to find TeV blazar candidates?\",\"answer\":\"A supervised Logistic Regression (LR) model constructs a feature hyperplane to discriminate TeV from non-TeV blazars and estimates a logistic probability for each source.\"},{\"question\":\"How is the “high-confidence” TeV candidate threshold defined?\",\"answer\":\"Non-TeV blazars with logistic values greater than 80% are considered high-confidence TeV candidates.\"},{\"question\":\"How many high-confidence TeV candidates are identified, and how are they evaluated?\",\"answer\":\"The technique identifies 40 high-confidence TeV candidates from the 4FGL-DR2/4LAC-DR2 blazars. Broadband SEDs are then built to test detectability with different instruments.\"}]","Exploring TeV candidates of Fermi blazars through machine learning - Logistic Regression (LR) selection and TeV candidate identification | PDF",1785900184,60,{"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},"exploring-tev-candidates-of-fermi-blazars-through-machine-learning-logistic-regression-lr-selection-and-tev-candidate-identification","",{"@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/exploring-tev-candidates-of-fermi-blazars-through-machine-learning-logistic-regression-lr-selection-and-tev-candidate-identification/125605/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning approach is used to find TeV blazar candidates?","Question",{"text":75,"@type":76},"A supervised Logistic Regression (LR) model constructs a feature hyperplane to discriminate TeV from non-TeV blazars and estimates a logistic probability for each source.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the “high-confidence” TeV candidate threshold defined?",{"text":80,"@type":76},"Non-TeV blazars with logistic values greater than 80% are considered high-confidence TeV candidates.",{"name":82,"@type":73,"acceptedAnswer":83},"How many high-confidence TeV candidates are identified, and how are they evaluated?",{"text":84,"@type":76},"The technique identifies 40 high-confidence TeV candidates from the 4FGL-DR2/4LAC-DR2 blazars. 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