[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119784-en":3,"doc-seo-119784-105":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},119784,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Explaining the Chemical Inventory of Orion KL through Machine Learning","Chemical and physical processes in astrochemically relevant sources require a holistic view of their chemical inventories. Prior work showed that simple regression models can reproduce molecular abundances for TMC-1 and predict new candidate species. This study extends the approach to Orion Kleinmann-Low (Orion KL), a high-mass star-forming region with multiple chemically and kinematically distinct environments. Non-linear correlations and varying column densities can yield unexpected detections or missing likely species across components. The proof-of-concept uses regression models to reproduce column densities from XCLASS fitting.","arXiv :2309 . 14449v1 [ astro-ph .GA] 25 Sep 2023  \nDraft version September 27, 2023  \nTypeset using LATEX twocolumn style in AASTeX631  \nExplaining the Chemical Inventory of Orion KL through Machine Learning  \nHaley N. Scolati  ,1 Anthony J. Remijan  ,2 Eric Herbst  ,1, 3 Brett A. McGuire  ,4, 2 and  \nKin Long Kelvin Lee 5  \n1 Department of Chemistry, University of Virginia, Charlottesville, VA 22903, USA  \n2 National Radio Astronomy Observatory, Charlottesville, VA 22903, USA  \n3 Department of Astronomy, University of Virginia, Charlottesville, VA 22903, USA  \n4 Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  \n5 Accelerated Computing Systems and Graphics, Intel Corporation, 2111 NE 25th Ave, Hillsboro, OR 97124, USA  \nABSTRACT  \nThe interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. (2021) demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a “unicorn” in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star forming region: the Orion Kleinmann-Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have non-linear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. to accurately reproduce the column densities from the XCLASS fitting program presented in Crockett et al. (2014) .  \nKeywords: Astrochemistry, ISM: molecules  \n1. INTRODUCTION  \nChemical models are an invaluable tool for gaining insight into the chemistry and physics occurring within the interstellar medium (ISM) . Molecules can serve astracers to highlight specific processes in astrochemically relevant sources, directly correlating our understanding of these regions and the completeness of their chemical inventories: both detected molecules, and robust upper limits to their abundances. When coupled with new observational detections and experimentally derived reaction rates, chemical models are better constrained to accurately describe the astrophysical processes. However, models heavily rely on understanding the formation chemistry and can be limited in scope in observa  \nCorresponding author: Haley N. Scolati, Kin Long Kelvin Lee  \n[hns3nh@virginia.edu](hns3nh@virginia.edu), [kin.long.kelvin.lee@intel.com](kin.long.kelvin.lee@intel.com)  \ntional sources or molecular families that are not well understood (Remijan et al. 2023) . Incorporating newly detected molecules into the networks can be incredibly time consuming as multiple new production and destruction pathways must be added, in addition to updating previous mechanisms. The work required to maintain complex networks is a combinatorial problem, and ultimately not sustainable with respect to the current pace of observational and experimental efforts (McGuire 2022) .  \nLee et al. (2021) alleviated this bottleneck by coupling supervised and unsupervised machine learning methods with cheminformatics to reproduce the known chemical inventory and predict chemical abundances of unobserved species for the well-studied and characterized source, the Taurus Molecular Cloud 1 (TMC-1) . Traditional chemical models require chemical and physical parameters (e.g. reaction rates, gas density) based on assumptions since these values ca","cbCait1I1M85IeS3","https://ap.wps.com/l/cbCait1I1M85IeS3","pdf",3025887,1,14,"English","en",105,"# Abstract\n# Introduction\n# Model Pipeline & Workflow","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To explain and reproduce the chemical inventory of Orion KL using machine learning, extending methods previously applied to TMC-1.\"},{\"question\":\"How does Orion KL differ from TMC-1 in this context?\",\"answer\":\"Orion KL consists of multiple structurally distinct environments that differ chemically and kinematically, leading to non-linear relationships among molecular column densities.\"},{\"question\":\"What modeling strategy is used to generate column density predictions?\",\"answer\":\"Similar regression models to prior work are used, with molecular species encoded via compact vector representations to predict (non)linear abundance relationships.\"}]","Explaining the Chemical Inventory of Orion KL through Machine Learning | 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is the main goal of the study?","Question",{"text":75,"@type":76},"To explain and reproduce the chemical inventory of Orion KL using machine learning, extending methods previously applied to TMC-1.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Orion KL differ from TMC-1 in this context?",{"text":80,"@type":76},"Orion KL consists of multiple structurally distinct environments that differ chemically and kinematically, leading to non-linear relationships among molecular column densities.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling strategy is used to generate column density predictions?",{"text":84,"@type":76},"Similar regression models to prior work are used, with molecular species encoded via compact vector representations to predict (non)linear abundance 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