[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119877-en":3,"doc-seo-119877-105":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":20,"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},119877,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Inclination Angles for Be Stars Determined Using Machine Learning","The study evaluates the feasibility of training machine learning algorithms on synthetic Hα line profiles to infer Be-star inclination angles from a single observed spectrum with medium resolution and moderate signal-to-noise. Three approaches are benchmarked: regression neural networks, classification neural networks, and support vector regression. Regression neural networks provide the strongest results, achieving a RMSE of 7.6° for 92 Galactic Be stars with inclinations constrained by Hα profile fitting, gravitational darkening signatures, or interferometric disk resolution. These trained models enable rapid inclination estimates for correlation studies in young open clusters and for extracting equatorial rotation velocity from measured sin i.","arXiv :2310 . 18437v1 [ astro-ph . SR] 27 Oct 2023  \nInclination Angles for Be Stars Determined Using Machine Learning  \nB. D. Lailey 1 ★ and T. A. A. Sigut 1,2  \n1 Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London N6A 3K7, Canada  \n2 Institute for Earth and Space Exploration (IESX), The University of Western Ontario, Canada  \nAccepted 2023 October 24 . Received 2023 October 24; in original form 2023 June 30  \nABSTRACT  \nWe test the viability of training machine learning algorithms with synthetic H 􀁕 line profiles to determine the inclination angles of Be stars (the angle between the central B star’s rotation axis and the observer’s line of sight) from a single observed medium-resolution, moderate S/N, spectrum. The performance of three different machine learning algorithms were compared: neural networks tasked with regression, neural networks tasked with classification, and support vector regression. Of these three algorithms, neural networks tasked with regression consistently outperformed the other methods with a RMSE error of 7 .6◦ on an observational sample of 92 galactic Be stars with inclination angles known from direct H 􀁕 profile fitting, from the spectroscopic signature of gravitational darkening, and, in a few cases, from interferometric observations that resolved the disk. The trained neural networks enable a quick and useful determination of the inclination angles of observed Be stars which can be used to search for correlated spin axes in young open clusters or to extract an equatorial rotation velocity from a measurement of 􀁅 sin 􀀸 .  \nKey words: stars: emission-line, Be –(stars:) circumstellar matter – stars: early-type – stars: fundamental parameters – methods: data analysis – methods: statistical  \n1 INTRODUCTION  \n1.1 Machine Learning in Astronomy  \nAstronomers are increasingly turning to machine learning to provide automated detection, analysis, and classification in response to large scale surveys that produce unprecedentedly large datasets (Baron 2019) . Machine learning differs from traditional model-fitting techniques in that the model is constructed according to the input data rather than being predefined (Ivezić et al. 2020) . The flexible nature of machine learning algorithms make them suited to a wide variety of tasks. In astronomical research, common uses of machine learning include classifying objects of interest from large databases (Domínguez Sánchez et al. 2018; Wang et al. 2022), dimensionality reduction (Portillo et al. 2020; Kovačević et al. 2022), anomaly detection (Baron & Poznanski 2017; Giles & Walkowicz 2020), building models that use more parameters than is possible with classical models (Huertas-Company et al. 2008), and visualizing datasets with a high number of parameters (Giles & Walkowicz 2018; Reis et al. 2021) .  \nBroadly speaking, machine learning can be divided into supervised and unsupervised algorithms. In supervised machine learning, a set of input features are mapped to a target variable based on labels provided by a human expert (Ivezić et al. 2020) . In unsupervised machine learning, labels are not included and the algorithms are frequently used to cluster data into groups, reduce dimensionality, and detect anomalies (Baron 2019) .  \n★ [E-mail: blailey2@uwo.ca](E-mail: blailey2@uwo.ca)  \n© 2023 The Authors  \n1.2 Machine Learning in Be Star Research  \nClassical Be stars are rapidly-rotating, B-type, main sequence stars that are surrounded by an equatorial, circumstellar, decretion disc (Porter & Rivinius 2003) . The defining characteristic of a Be star is the presence of emission in the hydrogen Balmer series, notably H 􀁕, owing to the presence of the disc (Slettebak 1982) . The exact mechanism that puts the disc gas into orbit is unknown, but it is thought to be related to near critical rotation, perhaps driven by theredistribution of angular momentum within the star (Granada et al. 2013; Rivinius et al. 2013) .  \nMachin","cbCaibYCINeduoNq","https://ap.wps.com/l/cbCaibYCINeduoNq","pdf",727334,1,14,"English","en",105,"# Abstract\n# 1 Introduction\n## 1.1 Machine Learning in Astronomy\n## 1.2 Machine Learning in Be Star Research","[{\"question\":\"How does the method determine Be-star inclination angles?\",\"answer\":\"It trains machine learning models on synthetic Hα line profiles and uses a single observed medium-resolution spectrum to predict the inclination angle between the stellar rotation axis and the observer’s line of sight.\"},{\"question\":\"Which machine learning algorithm performs best in the comparison?\",\"answer\":\"Neural networks configured for regression outperform the other tested methods, reaching an RMSE of 7.6° on a sample of 92 Galactic Be stars.\"},{\"question\":\"What types of reference inclination measurements are used for validation?\",\"answer\":\"Inclination angles are taken from direct Hα profile fitting, from the spectroscopic signature of gravitational darkening, and in some cases from interferometric observations that resolve the circumstellar disk.\"}]","Inclination Angles for Be Stars Determined Using Machine Learning | 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does the method determine Be-star inclination angles?","Question",{"text":75,"@type":76},"It trains machine learning models on synthetic Hα line profiles and uses a single observed medium-resolution spectrum to predict the inclination angle between the stellar rotation axis and the observer’s line of sight.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm performs best in the comparison?",{"text":80,"@type":76},"Neural networks configured for regression outperform the other tested methods, reaching an RMSE of 7.6° on a sample of 92 Galactic Be stars.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of reference inclination measurements are used for validation?",{"text":84,"@type":76},"Inclination angles are taken from direct Hα profile fitting, from the spectroscopic signature of gravitational darkening, and in some cases from interferometric observations that resolve the circumstellar 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