[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125777-en":3,"doc-seo-125777-105":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},125777,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Galaxy stellar and total mass estimation using machine learning - Research paper","Conventional galaxy mass estimation relies on restrictive model assumptions and faces degeneracies when separating baryonic and dark matter. A machine-learning approach can reduce dependence on such assumptions by learning the mapping between present-day observations and the predicted stellar and dark-matter mass distributions. Using TNG100 simulated galaxies, multibranch CNN models ingest galaxy images together with spatially resolved mean velocity and velocity dispersion maps to predict central stellar mass, total mass, and stellar mass-to-light ratio, while breaking component degeneracy and achieving measurable predictive uncertainties.","MNRAS 528, 6354–6369 (2024) [https://doi.org/10.1093/mnras/stae406](https://doi.org/10.1093/mnras/stae406)  \nAdvance Access publication 2024 February 7  \nGalaxy stellar and total mass estimation using machine learning  \nJiani Chu  , 1‹ Hongming Tang , 1‹ Dandan Xu,1 Shengdong Lu 2 and Richard Long1,3  \n1Department of Astronomy, Tsinghua University, Beijing 100084, China  \n2Department of Physics, Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE, UK  \n3Department of Physics and Astronomy, Jodrell Bank Centrefor Astrophysics, The University of Manchester, Oxford Road, Manchester M13 9PL, UK  \nAccepted 2024 January 30. Received 2024 January 25; in original form 2023 November 16  \nABSTRACT  \nConventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning (ML), which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multibranch convolutional neural network (CNN) based ML methods to predict the central (i.e. within 1−2 effective radii) stellar and total masses, and the stellar mass-to-light ratio (M∗/L) . These models take galaxy images and spatially resolved mean velocity and velocity dispersion maps as inputs. Such CNN-based models can, in general, break the degeneracy between baryonic and dark matter in the sense that the model can make reliable predictions on the individual contributions of each component. For example, with r-band images and two galaxy kinematic maps as inputs, our model predicting M∗/L has a prediction uncertainty of 0.04 dex. Moreover, to investigate which (global) features significantly contribute to the correct predictions of the properties above, we utilize a gradient-boosting machine. We find that galaxy luminosity dominates the prediction of all masses in the central regions, with stellar velocity dispersion coming next. We also investigate the main contributing features when predicting stellar and dark matter mass fractions (f∗ , fDM ) and the dark matter mass MDM , and discuss the underlying astrophysics.  \nKey words: methods: data analysis–galaxies: kinematics and dynamics.  \n1 INTRODUCTION  \nAchieving a full understanding of galaxy evolution requires accurate measurements of the ‘unseen’ matter. This is why, among the many areas in astrophysical measurements and modelling, galaxy dynamics and gravitational lensing play unique roles, as they provide meaningful constraints on dark matter. On the other side of these measurements lies the distribution of the stellar component. Properties, such as stellar mass-to-light ratio (M∗/L) and initial mass function (IMF), are essential properties to solving the galaxy evolution puzzle. Therefore, a fundamental task in this regard comes down to accurate determinations of the different contributions of dark matter and baryons – a central goal of galaxy dynamics and gravitational lensing studies.  \nIn this regard, recent integral field spectroscopic observations have provided good data sets to study the dynamical properties of galaxies for a large sample of galaxies across a wide range of Hubble types, both in the nearby Universe, for example, those from the ATLAS3D (Cappellari et al. 2011), MaNGA (Bundy et al. 2015), and SAMI (Fogarty et al. 2014) surveys, and at high redshifts, e.g. the KMOS Galaxy Evolution Survey (KGES, Turner et al. 2017) . Historically, people have developed various dynamical modelling methods (e.g. Jeans 1922; Schwarzschild 1979; Syer & Tremaine  \n􀀂 E-mail: [zjn20@mails.tsinghua.edu.cn](zjn20@mails.tsinghua.edu.cn) (JC); [hongmingt@tsinghua.edu.cn](hongmingt@tsinghua.edu.cn)[ ](hongmingt@tsinghua.edu.cn)(HT)  \n1996), which typically combine a single-band image of a galaxy with stellar Integral Field Unit (IFU) kinematic maps to constrain matt","cbCaimn0FDfU9otU","https://ap.wps.com/l/cbCaimn0FDfU9otU","pdf",2592477,1,16,"English","en",105,"# Abstract\n# Introduction\n## Motivations for accurate dark matter and baryon measurements\n## Existing dynamical modelling methods using IFU kinematics\n## Limitations from assumptions and approximations","[{\"question\":\"Why do conventional galaxy mass estimation methods face challenges?\",\"answer\":\"They depend on model assumptions and encounter degeneracies when separating baryonic and dark matter contributions.\"},{\"question\":\"What inputs are used by the multibranch CNN models in this study?\",\"answer\":\"The models take galaxy images and spatially resolved mean velocity and velocity dispersion maps as inputs to predict central masses and M∗/L.\"},{\"question\":\"Which features most strongly influence the model predictions in central regions?\",\"answer\":\"Gradient-boosting feature analysis indicates galaxy luminosity dominates predictions of masses in the central regions, with stellar velocity dispersion next.\"}]","Galaxy stellar and total mass estimation using machine learning - 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