[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125635-en":3,"doc-seo-125635-105":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},125635,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Galaxy Rotation Curve Fitting Using Machine Learning Tools","Galaxy rotation curve fitting constrains dark matter (DM) halo models, but non-phenomenological DM profiles without analytic density expressions make finding full baryonic+DM parameter best-fits difficult and time-consuming. This work applies a gradient descent approach from neural-network backpropagation to fit the Milky Way’s Grand Rotation Curve from ~1 pc to ~105 pc. The mass model includes a bulge, disk, and a fermionic Ruffini-Argüelles-Rueda (RAR) halo built from first-principle physics. Results deliver best-fit overall Milky Way rotation-curve parameters in CPU hours over 10−2–105 pc, offering an efficient state-of-the-art machine learning solution validated with compact fermion-core data.","universe   \nArticle  \nGalaxy Rotation Curve Fitting Using Machine Learning Tools  \nCarlos. R. Argüelles 1,2, *,† and Santiago Collazo 1, *,†  \n[ astro-ph .GA] 16 Aug 2023  \narXiv :2308 .08420v1  \nCitation: Argüelles, C.R.; Collazo S. Rotation Curve Fitting Using Machine Learning Tools. Universe 2023, 9, 372 . [https://doi.org/10.3390/universe9080372](https://doi.org/10.3390/universe9080372)  \nAcademic Editor: Firstname Lastname  \nReceived: 5 July 2023  \nRevised: 27 July 2023  \nAccepted:  \nPublished:  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Instituto de Astrofísica de La Plata, UNLP-CONICET, Paseo del Bosque s/n, La Plata B1900FWA, Argentina  \n2 ICRANet, Piazza della Repubblica 10, I-65122 Pescara, Italy  \n* Correspondence: [carguelles@fcaglp.unlp.edu.ar](carguelles@fcaglp.unlp.edu.ar) (C.R.A.); [scollazo@fcaglp.unlp.edu.ar](scollazo@fcaglp.unlp.edu.ar) (S.C.)† These authors contributed equally to this work.  \nAbstract: Galaxy rotation curve (RC) fitting is an important technique which allows the placement of constraints on different kinds of dark matter (DM) halo models. In the case of non-phenomenological DM profiles with no analytic expressions, the art of finding RC best-fits including the full baryonic + DM free parameters can be difficult and time-consuming. In the present work, we use a gradient descent method used in the backpropagation process of training a neural network, to fit the so-called Grand Rotation Curve of the Milky Way (MW) ranging from ∼1 pc all the way to ∼105 pc. We model the mass distribution of our Galaxy including a bulge (inner + main), a disk, and a fermionic dark matter (DM) halo known as the Ruffini-Argüelles-Rueda (RAR) model. This is a semi-analytical model built from first-principle physics such as (quantum) statistical mechanics and thermodynamics, whose more general density profile has a dense core – diluted halo morphology with no analytic expression. As shown recently and further verified here, the dark and compact fermion-core can work as an alternative to the central black hole in SgrA* when including data at milliparsec scales from the S-cluster stars. Thus, we show the ability of this state-of-the-art machine learning tool in providing the best-fit parameters to the overall MW RC in the 10−2–105 pc range, in a few hours of CPU time.  \nKeywords: dark matter; Milky Way; rotation curves; numerical methods  \n1. Introduction  \nDisk galaxies, like our own, are rotational supported structures with the advantage of having baryonic (or luminous) mass tracers in approximate circular orbits from which it is possible to obtain the so-called RC. The specific DM distribution, usually dubbed asthe DM density profile, is inferred by fitting the observed velocity RC as a function of the galactocentric radius. Typically, this is carried out by assuming a given underlying DM profile together with different mass models for the luminous components such as the bulge, disc, etc. (see, e.g., [1] for a review) . Most of these studies assume phenomenological DM profiles (in spherical symmetry) obtained from classical N-body cosmological simulations with a given analytic expression, besides the visible mass components. However, other kinds of DM profiles can be obtained from first-principle physics (i.e., thermodynamics and statistical mechanics) while accounting for the quantum nature of the particles, such asthe RAR model for fermions (see [2] for a review, and references therein) and, e.g., [3,4] for bosonic DM, with no analytic expressions for the profiles.  \nThe RAR model consist in a self-gravitating system of fermions in General Relativity, and therefore is built upon a coupled system of (","cbCaiuXx0lt5nls8","https://ap.wps.com/l/cbCaiuXx0lt5nls8","pdf",1230352,1,10,"English","en",105,"# Introduction\n## Rotation curves and DM density profiles\n## RAR fermionic DM model and parameterization\n## Numerical solution and boundary conditions","[{\"question\":\"Why is galaxy rotation curve fitting important for dark matter studies?\",\"answer\":\"It constrains different dark matter halo models by comparing observed rotation curves with predictions from chosen mass distributions and DM density profiles.\"},{\"question\":\"What machine learning method is used to fit the Milky Way rotation curve?\",\"answer\":\"A gradient descent method from neural-network backpropagation is used to obtain best-fit parameters efficiently.\"},{\"question\":\"How is the dark matter halo modeled in this work?\",\"answer\":\"The study uses a fermionic Ruffini-Argüelles-Rueda (RAR) DM halo model whose underlying profile has no analytic expression and is solved numerically with free parameters.\"}]","Galaxy Rotation Curve Fitting Using Machine Learning Tools | 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is galaxy rotation curve fitting important for dark matter studies?","Question",{"text":75,"@type":76},"It constrains different dark matter halo models by comparing observed rotation curves with predictions from chosen mass distributions and DM density profiles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning method is used to fit the Milky Way rotation curve?",{"text":80,"@type":76},"A gradient descent method from neural-network backpropagation is used to obtain best-fit parameters efficiently.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dark matter halo modeled in this work?",{"text":84,"@type":76},"The study uses a fermionic Ruffini-Argüelles-Rueda (RAR) DM halo model whose underlying profile has no analytic expression and is solved numerically with free 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