[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118511-en":3,"doc-seo-118511-105":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},118511,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning of Rotational spectra analysis in interstellar medium","Machine learning methods support the analysis of rotational spectra in the interstellar medium by improving identification of molecular transitions and enabling discovery across previously inaccessible spectral regions. The approach addresses key limitations of traditional workflows, including handling noise, reducing uncertainty, and avoiding overfitting when extracting chemical information from large telescope datasets. The article reviews supervised and unsupervised learning, deep learning architectures, and spectral line fitting strategies for efficient, automatic interpretation.","Machine learning of Rotational spectra analysis in interstellar medium  \nHumphrey Sam Samuel, *Emmanuel Edet Etim, John Paul Shinggu and Bulus. Bako Received: 14 February 2023/Accepted 20 November 2023/Published 25 November 2023 Abstract: In the investigation of rotating They help make new molecular discoveries spectra concerning the interstellar medium, and enable the research of previously machine-learning approaches have been undiscovered spectral regions in the documented as effective instrument. The electromagnetic spectrum. Despite these understanding of molecular rotational developments, there are still problems to be transitions in space and can be a significant solved, such as handling data noise, source of information on the dynamics, uncertainty, and over fitting. By enabling physical properties, and chemical make-up of effective and automatic extraction of interstellar spaces. Traditional analytical chemical information from complicated techniques are however confronted with datasets, machine learning in rotational difficulties when dealing with the enormous spectra analysis revolutionizes the study of and complicated information produced by interstellar chemistry. It enables scientists to telescopic observations. The handling of learn about the chemical diversity and these massive datasets and the extraction of development of interstellar regions, making useful data from rotating spectra can be crucial contributions to our comprehension accomplished using machine learning of the genesis and development of the methods, which are a promising approach. universe.  \nThis article gives a general overview of the  \ndevelopments of machine learning in the Keywords: Machine learning, artificial analysis of rotational spectra in the intelligence, interstellar molecules,  \ninterstellar medium. It goes over how to  rotational spectroscopy  recognize and describe molecular transitions Humphrey Sam Samuel  \nusing supervised and unsupervised learning Computational Astrochemistry and Bioalgorithms, deep learning architectures, and Simulation Research Group, Federal spectral line fitting methods. Also, machine University Wukari  \nlearning algorithms can aid detection of Email: [humphreysedeke@gmail.com](humphreysedeke@gmail.com)  \n[spectral lines that are weak or infrequent but](spectral lines that are weak or infrequent but Orcid id: 0009-0001-7480-4234)[ Orcid id:](spectral lines that are weak or infrequent but Orcid id: 0009-0001-7480-4234)[ ](spectral lines that are weak or infrequent but Orcid id: 0009-0001-7480-4234)[0009-0001-7480-4234](spectral lines that are weak or infrequent but Orcid id: 0009-0001-7480-4234)[ ](spectral lines that are weak or infrequent but Orcid id: 0009-0001-7480-4234)may contain important data regarding the  \nEmmanuel Edet Etim*  \nDepartment of Chemical Sciences, Federal University Wukari, Taraba State  \nEmail: [emmaetim@gmail.com](emmaetim@gmail.com)  \n[Orcid id:](Orcid id: 0000-0001-8304-9771)[ ](Orcid id: 0000-0001-8304-9771)[0000-0001-8304-9771](Orcid id: 0000-0001-8304-9771)  \nJohn Paul Shinggu  \nDepartment of Chemical Sciences, Federal University Wukari, Taraba State [Email](Email: johnshinggu@gmail.com)[:](Email: johnshinggu@gmail.com)[ johnshinggu@gmail.com](Email: johnshinggu@gmail.com)  \n[Orcid id:](Orcid id: /0009-0005-2216-3155)[ ](Orcid id: /0009-0005-2216-3155)[/0009-0005-2216-3155](Orcid id: /0009-0005-2216-3155)  \n[Bulus. Bako](Bulus. Bako)  \nDepartment of Chemical Sciences, Federal chemical complexity of interstellar areas. University Wukari, Taraba State  \n[Email:](Email: bakobulus01@gmail.com)[ ](Email: bakobulus01@gmail.com)[bakobulus01@gmail.com](Email: bakobulus01@gmail.com)  \n[Orcid id:](Orcid id: 0009-0001-3946-0712)[ ](Orcid id: 0009-0001-3946-0712)[0009-0001-3946-0712](Orcid id: 0009-0001-3946-0712)  \n1.0 Introduction  \nMolecular spectroscopy is a effective and flexible combination of methods that is useful in the investigation of the interaction of electromagnetic radiation","cbCaipJXlC3ga4fP","https://ap.wps.com/l/cbCaipJXlC3ga4fP","pdf",1147602,1,31,"English","en",105,"# Introduction\n## Molecular spectroscopy and interstellar medium\n## Rotational spectra and the need for data extraction\n## Motivation for machine learning methods\n# Machine-learning approaches overview\n## Supervised and unsupervised learning\n## Deep learning architectures\n## Spectral line fitting methods\n## Challenges: noise, uncertainty, overfitting\n# Applications and scientific impact\n## Discovering weak or infrequent spectral lines\n## Advancing astrochemical understanding","[{\"question\":\"Why are machine-learning approaches useful for rotational spectra analysis in the interstellar medium?\",\"answer\":\"They improve the extraction of molecular rotational information from large, complex telescope datasets and help recognize relevant spectral transitions more effectively than purely traditional workflows.\"},{\"question\":\"What main challenges does the article associate with conventional rotational spectral analysis?\",\"answer\":\"Conventional techniques face difficulties handling data noise, uncertainty, and the risk of overfitting when working with enormous and complicated spectral information.\"},{\"question\":\"Which machine-learning strategies are highlighted for analyzing rotational spectra?\",\"answer\":\"The article reviews supervised and unsupervised learning, deep learning architectures, and spectral line fitting methods to support automated transition detection and characterization.\"}]","Machine learning of Rotational spectra analysis in interstellar medium | 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are machine-learning approaches useful for rotational spectra analysis in the interstellar medium?","Question",{"text":75,"@type":76},"They improve the extraction of molecular rotational information from large, complex telescope datasets and help recognize relevant spectral transitions more effectively than purely traditional workflows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What main challenges does the article associate with conventional rotational spectral analysis?",{"text":80,"@type":76},"Conventional techniques face difficulties handling data noise, uncertainty, and the risk of overfitting when working with enormous and complicated spectral information.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning strategies are highlighted for analyzing rotational spectra?",{"text":84,"@type":76},"The article reviews supervised and unsupervised learning, deep learning architectures, and spectral line fitting methods to support automated transition detection 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