[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122681-en":3,"doc-seo-122681-105":30,"detail-sidebar-cat-0-en-105":95},{"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},122681,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","CMR exploration II - filament identification with machine learning","Magnetohydrodynamics simulations are used to model the formation of filamentary molecular clouds through the collision-induced magnetic reconnection (CMR) mechanism under varied physical conditions. Radiative transfer with radmc-3d generates synthetic dust emission maps of CMR filaments, which are then analyzed using the casi-2d machine-learning approach together with a diffusion model. Both methods show high accuracy on a test dataset, with detection rates above 80% and 70% at a 5% false detection rate. The models are applied to Herschel dust observations, yielding high-confidence CMR filament candidates, including Orion A candidates consistent with previous molecular-line results.","arXiv :2308 .06641v1 [ astro-ph .GA] 12 Aug 2023  \nDraft version August 15, 2023  \nTypeset using LATEX preprint2 style in AASTeX63  \nCMR exploration II – filament identification with machine learning  \nDuo Xu, 1 Shuo Kong,2 Avichal Kaul,2 H´ector G. Arce,3 and Volker Ossenkopf-Okada4  \n1 Department of Astronomy, University of Virginia, Charlottesville, VA 22904, USA  \n2 Steward Observatory, University of Arizona, Tucson, AZ 85719, USA  \n3 Department of Astronomy, Yale University, New Haven, CT 06511, USA  \n4 I. Physikalisches Institut, Universit¨at zu K¨oln, Z¨ulpicher Str. 77, D-50937 K¨oln, Germany  \nABSTRACT  \nWe adopt magnetohydrodynamics (MHD) simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varying physical conditions. We conduct radiative transfer using radmc-3d to generate synthetic dust emission of CMR filaments. We use the previously developed machine learning technique casi-2d along with the diffusion model to identify the location of CMR filaments in dust emission. Both models showed a high level of accuracy in identifying CMR filaments in the test dataset, with detection rates of over 80% and 70%, respectively, at a false detection rate of 5% . We then apply the models to real Herschel dust observations of different molecular clouds, successfully identifying several high-confidence CMR filament candidates. Notably, the models are able to detect high-confidence CMR filament candidates in Orion A from dust emission, which have previously been identified using molecular line emission.  \nKeywords: Interstellar medium (847)—Interstellar filaments (842)—Convolutional neural networks (1938)—Molecular clouds (1072)—Interstellar magnetic fields (845)—Magnetohydrodynamics(1964)  \n1. INTRODUCTION  \nFilaments are an omnipresent structure in the interstellar medium (Andr´e et al. 2014), and are of great significance in the star formation process, as they contribute to the formation of dense cores and the origin of the initial mass function (Andr´e et al. 2010; K¨onyves et al. 2015) . However, the formation mechanism of filaments in molecular clouds remains a subject of debate (Hacar et al. 2022) . Observations conducted by the Herschel Space Observatory suggest that filaments have a typical width of 0.1 pc with a notable scatter of ±0.06 pc (Arzoumanian et al. 2019), which supports the notion that they may form through the dissipation of  \nlarge-scale turbulence, occurring at the transitional scale between sonic and subsonic speeds (Arzoumanian et al. 2011; Ferrand et al. 2020; Federrath et al. 2021) . Other potential mechanisms for the formation of filamentary structures include the self-gravitational fragmentation of a sheet-like cloud (Tomisaka & Ikeuchi 1983), the elongation of overdensities by turbulent shear flows, resulting in the development of small line-mass filaments (Hennebelle 2013), the shock compression of magnetized clumps (Abe et al. 2021), the interaction between two shockcompressed sheets (Padoan & Nordlund 1999), the convergence of gas flow along local magnetic fields (Chen & Ostriker 2014 , 2015), the accre-  \n2 Xu et al.  \ntion flow driven by gravity (Naranjo-Romero et al. 2022), the pressure effects of mechanical and radiation feedback on pre-existing density inhomogeneities on the surfaces and edges of clouds (Suri et al. 2019), the aggregation of small subsonic filaments by collapse and shear flows (Smith et al. 2016), and the instabilities induced by large-scale shear and Galactic differential rotation leading to the formation of giant filaments.  \nRecently, Kong et al. (2021, hereafter K21) proposed a novel mechanism for the formation of filaments in molecular clouds, referred to as collision-induced magnetic reconnection (CMR) . The study involved magnetohydrodynamics (MHD) simulations of two colliding clouds. The magnetic fields (B-fields) in the two molecular clouds are oriented in opposite directions. For ex","cbCail2fCw7baOHI","https://ap.wps.com/l/cbCail2fCw7baOHI","pdf",6402211,1,24,"English","en",105,"# Introduction\n## Filaments in the interstellar medium\n## Competing formation mechanisms\n## Collision-induced magnetic reconnection (CMR)\n## Challenges of identifying CMR filaments observationally\n## Machine learning for filament identification","[{\"question\":\"What physical scenario does the document model for filament formation?\",\"answer\":\"It models filamentary molecular cloud formation via the collision-induced magnetic reconnection (CMR) mechanism using magnetohydrodynamics simulations under varying physical conditions.\"},{\"question\":\"How are synthetic dust observations produced for training or testing?\",\"answer\":\"Radiative transfer is performed with radmc-3d to generate synthetic dust emission of CMR filaments.\"},{\"question\":\"How does the proposed machine-learning approach perform on data and how is it validated?\",\"answer\":\"The casi-2d method and a diffusion model identify CMR filament locations with high accuracy on the test dataset, achieving detection rates over 80% and 70% at a 5% false detection rate.\"},{\"question\":\"What do the models find when applied to real Herschel observations?\",\"answer\":\"Applied to Herschel dust observations of multiple molecular clouds, the models successfully identify several high-confidence CMR filament candidates, including candidates in Orion A supported by prior molecular-line studies.\"}]","CMR exploration II - filament identification with machine learning | PDF",1785812158,60,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"cmr-exploration-ii-filament-identification-with-machine-learning","",{"@graph":36,"@context":89},[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/cmr-exploration-ii-filament-identification-with-machine-learning/122681/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What physical scenario does the document model for filament formation?","Question",{"text":75,"@type":76},"It models filamentary molecular cloud formation via the collision-induced magnetic reconnection (CMR) mechanism using magnetohydrodynamics simulations under varying physical conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are synthetic dust observations produced for training or testing?",{"text":80,"@type":76},"Radiative transfer is performed with radmc-3d to generate synthetic dust emission of CMR filaments.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine-learning approach perform on data and how is it validated?",{"text":84,"@type":76},"The casi-2d method and a diffusion model identify CMR filament locations with high accuracy on the test dataset, achieving detection rates over 80% and 70% at a 5% false detection rate.",{"name":86,"@type":73,"acceptedAnswer":87},"What do the models find when applied to real Herschel observations?",{"text":88,"@type":76},"Applied to Herschel dust observations of multiple molecular clouds, the models successfully identify several high-confidence CMR filament candidates, including candidates in Orion A supported by prior molecular-line studies.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,113,118,123,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":29,"slug":112},5,"Comic","comic",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},6,"Technology",50,"technology",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":124,"slug":125},30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]