[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123226-en":3,"doc-seo-123226-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123226,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning of the Chemical Inventory and Rare Isotopologues of the Solar-Type Protostellar Source IRAS 16293-2422 B","Machine learning models are applied to predict and fit molecular column densities in source B of the Class 0 protostellar binary IRAS 16293-2422. Previous work on the TMC-1 dark molecular cloud is extended to the chemically distinct environment further along the star-formation pathway. By encoding isotopic composition into molecular feature vectors, the models are evaluated for reproducing isotopic ratios. Trained models generate high-abundance molecule candidates suitable for laboratory spectroscopy and radioastronomical detection.","MACHINE LEARNING OF THE CHEMICAL INVENTORY AND RARE ISOTOPOLOGUES OF THE SOLAR-TYPE PROTOSTELLAR SOURCE IRAS 16293-2422 B  \nZACHARY TAYLOR PHILIP FRIED, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; KELVIN LEE, Accelerated Computing Systems and Graphics, Intel Corporation, Hillsboro, OR, USA; ALEX BYRNE, BRETT A. McGUIRE, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.  \nMachine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. However, in interstellar sources further along the path of star-formation, such as those where a protostar itself has been formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. In this talk, I will describe the ability of various machine learning models to 􀀂t the column densities of the molecules detected in source B of the Class 0 protostellar binary IRAS 16293-2422 . By including a simple encoding of isotopic composition in the molecular feature vectors, I also examine for the 􀀂rst time how well these models can replicate the isotopic ratios. Finally, these trained models provide a list of predicted high-abundance molecules that may be excellent targets for laboratory spectroscopy and subsequent radioastronomical detection in IRAS 16293-2422 B.","cbCaisN6MSxNvTk8","https://ap.wps.com/l/cbCaisN6MSxNvTk8","pdf",13781,1,"English","en",105,"# Modeling molecular column densities\n## Isotopic feature encoding for rare isotopologues\n## Predicted targets for spectroscopy and radioastronomical detection","[{\"question\":\"What does the talk focus on regarding IRAS 16293-2422 B?\",\"answer\":\"It focuses on using multiple machine learning models to fit the column densities of molecules detected in source B of IRAS 16293-2422.\"},{\"question\":\"How are isotopic ratios incorporated into the machine learning models?\",\"answer\":\"Isotopic composition is encoded into the molecular feature vectors, enabling assessment of how well the models reproduce isotopic ratios.\"},{\"question\":\"What outputs are produced after training the models?\",\"answer\":\"The trained models provide a list of predicted high-abundance molecules that can serve as targets for laboratory spectroscopy and subsequent radioastronomical detection.\"}]","Machine Learning of the Chemical Inventory and Rare Isotopologues of the Solar-Type Protostellar Source IRAS 16293-2422 B | 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does the talk focus on regarding IRAS 16293-2422 B?","Question",{"text":73,"@type":74},"It focuses on using multiple machine learning models to fit the column densities of molecules detected in source B of IRAS 16293-2422.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How are isotopic ratios incorporated into the machine learning models?",{"text":78,"@type":74},"Isotopic composition is encoded into the molecular feature vectors, enabling assessment of how well the models reproduce isotopic ratios.",{"name":80,"@type":71,"acceptedAnswer":81},"What outputs are produced after training the models?",{"text":82,"@type":74},"The trained models provide a list of predicted high-abundance molecules that can serve as targets for laboratory spectroscopy and subsequent radioastronomical 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