检索结果-内蒙古大学图书馆

3rd International Seminar on Application for Technology of Information and Communication, iSemantic 2018

作者： Valentine Masala, Ivana Wu, Shih-Lin Informatics Engineering Department De la Salle Catholic University Manado Kombos KairagiI95000 Indonesia Dept. of Computer Science and Information Engineering Chang Gung University Tao-Yuan333 Taiwan

ISBN: (纸本)9781538674864

To capture the action of the user in the action training and assessment system that will be developed, this work use Kinect as a means of data retrieval. The system is set to capture simple action and complicated action for comparison. The first action is a parameter used in comparison with the user, where the recording is taken from the professional user who has been stored in the database system. Furthermore, the second action is an action that comes from a novice user who performs the action in the current time and the result will be compared with the first action. The action data takes from X - Y coordinates value. The obtained the value of X and Y from the shoulder to the palm. The system proposes with our algorithm to show the assessment result of both actions comparison. It is displayed in the compatible percentages and the opposite one. © 2018 IEEE.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Solving Lur'e equations through zeroing neural networks

引用

Information sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Decoding arm kinematics from EMG signals using Kalman filter 18

Decoding arm kinematics from EMG signals using Kalman filter

引用

8th International Conference on biomedical engineering and Technology, ICBET 2018

作者： ElMohandes, Hend Eldawlatly, Seif Audí, Josep Marcel Cardona Ruff, Roman Hoffmann, Klaus-Peter Center of Informatics Science Nile University Giza Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Dept. Med. Eng. and Neuroprostheses Fraunhofer IBMT Sulzbach Germany

ISBN: (纸本)9781450363693

Myoelectric control of prosthetic arms provides a new hope for providing naturalistic movements to amputees. Extensive work has been made in recent years to use Electromyography (EMG) signals to enhance the operation of prosthetic arms. In this paper, we propose an EMG Kalman filter-based model, where we identify the relationship between the joint angles and recorded EMG signals. EMG signals were recorded from biceps and triceps muscles and used to train a Kalman filter decoder. We assessed the performance of the decoder by computing the correlation and the normalized root mean-square error (NRMSE) between the decoded and actual joint angles. When decoding using biceps EMG only, an average correlation of 0.61 was obtained with a NRMSE of 0.35. For triceps EMG only, an average correlation of 0.5 was obtained with a NRMSE of 0.5. Finally, when decoding the EMG of both the biceps and triceps muscles, the average correlation increased to 0.87 while the average NRMSE decreased to 0.18. These results outperform recent studies in similar applications which indicates the efficacy of the proposed decoder in decoding joint angles from recorded EMG. This could help in enhancing the control of prosthetic arms. © 2018 Association for Computing Machinery.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

CFEA: Collaborative feature ensembling adaptation for domain adaptation in unsupervised optic disc and cup segmentation

arXiv

引用

arXiv 2019年

作者： Liu, Peng Kong, Bin Li, Zhongyu Zhang, Shaoting Fang, Ruogu J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL United States Department of Computer Science UNC Charlotte CharlotteNC United States School of Software Engineering Xi'an Jiaotong University Xi'an China Sensetime Research

Recently, deep neural networks have demonstrated comparable and even better performance with board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models to new testing domains. In this paper, we propose a novel unsupervised domain adaptation framework, called Collaborative Feature Ensembling Adaptation (CFEA), to effectively overcome this challenge. Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights. In particular, we simultaneously achieve domain-invariance and maintain an exponential moving average of the historical predictions, which achieves a better prediction for the unlabeled data, via ensembling weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier and meanwhile benefit the ensembling of smoothing weights. Comprehensive experimental results demonstrate that our CFEA model can overcome performance degradation and outperform the state-of-the-art methods in segmenting retinal optic disc and cup from fundus images. Code is available at https://***/cswin/AWC. Copyright © 2019, The Authors. All rights reserved.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

P10.25.A ATR INHIBITION IN EXPERIMENTAL GLIOMA: DISCOVERY AND VALIDATION OF MOLECULAR TARGETS FOR FUNCTIONALLY-INSTRUCTED COMBINATION THERAPIES

引用

Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii68–ii68页

作者： Walter, B Hirsch, S Kuhlburger, L Stahl, A Schnabel, L Haeusser, L A Tsiami, F Ploeger, S Aghaallaei, N Dick, A M Skokowa, J Schmees, C Templin, M Schenke-Layland, K Tatagiba, M Nahnsen, S Merk, D Tabatabai, G Hertie Institute for Clinical Brain Research Tübingen Germany Quantitative Biology Center Eberhard Karls University Tübingen Tübingen Germany Biomedical Data Science Department of Computer Science Eberhard Karls University Tübingen Tübingen Germany NMI Natural and Medical Sciences Institute at the University of Tübingen Reutlingen Germany German Consortium for Translational Cancer Research (DKTK) Partner site Tübingen Tübingen Germany Division of Translational Oncology Department of Internal Medicine II University Hospital Tübingen Tübingen Germany Institute of Biomedical Engineering Dept. for Medical Technologies and regenerative Medicine Eberhard Karls University Tübingen Tübingen Germany Center for Neuro-Oncology Comprehensive Cancer Center Tübingen-Stuttgart Eberhard Karls University Tübingen Tübingen Germany

来源：评论

学校读者我要写书评

暂无评论

Clustering Inter-Arrival Time of Health Care Encounters for High Utilizers

Clustering Inter-Arrival Time of Health Care Encounters for ...

引用

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Chengliang Yang Chris Delcher Elizabeth Shenkman Sanjay Ranka Dept. of Computer & Information Science & Engineering University of Florida Gainesville FL USA Dept. of Health Outcomes & Biomedical Informatics University of Florida Gainesville USA

ISBN: (纸本)9781538642955

Patients with a large number of health care encounters receive great attention in health care research because their expensive and problematic health care utilization has important implications for the US health care system. The large volume of emergency department (ED) visits and inpatient hospital stays through time provides a unique opportunity to apply data-driven methods for identifying temporal signals associated with these patients. The micro variations in the inter-arrival time of these encounters within this population are not well-studied. Computational approaches for distinguishing various temporal visiting patterns leads to the problem of efficiently clustering asynchronous time series. Thus, we propose a Wasserstein distance based spectral clustering for this problem. Asynchronous time series are first represented as histograms of inter-arrival time and their pairwise similarities are computed under Wasserstein distance. Spectral clustering operates on the similarity matrix as input thereby avoiding the computational bottleneck of Wasserstein barycenters. The effectiveness of this method is demonstrated by synthetic data and application to a large real world health insurance encounters dataset, identifying potential associations between temporal visiting patterns and health factors from a population of frequent ED and inpatient hospital users.

关键词： Histograms Time series analysis Hospitals Conferences Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Search for high-mass Wγ and Zγ resonances using hadronic W/Z boson decays from 139 fb⁻¹ of pp collisions at √s = 13 TeV with the ATLAS detector

引用

Journal of High Energy Physics 2023年第7期2023卷 1-47页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes

A search for high-mass charged and neutral bosons decaying to Wγ and Zγ final states is presented in this paper. The analysis uses a data sample of s = 13 TeV proton-proton collisions with an integrated luminosity of 139 fb⁻¹ collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined using models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. The range of resonance masses explored extends from 1.0 TeV to 6.8 TeV. At these high resonance masses, it is beneficial to target the hadronic decays of the W and Z bosons because of their large branching fractions. The decay products of the high-momentum W/Z bosons are strongly collimated and boosted-boson tagging techniques are employed to improve the sensitivity. No evidence of a signal above the Standard Model backgrounds is observed, and upper limits on the production cross-sections of these bosons times their branching fractions to Wγ and Zγ are derived for various boson production models. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

来源：评论

学校读者我要写书评

暂无评论

Search for a light charged Higgs boson in t → H ^± b decays, with H ^± → cb, in the lepton+jets final state in proton-proton collisions at √s = 13 TeV with the ATLAS detector

引用

Journal of High Energy Physics 2023年第9期2023卷 1-52页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes Unive

A search for a charged Higgs boson, H ±, produced in top-quark decays, t → H ± b, is presented. The search targets H ± decays into a bottom and a charm quark, H ± → cb. The analysis focuses o... 详细信息

A search for a charged Higgs boson, H ^±, produced in top-quark decays, t → H ^± b, is presented. The search targets H ^± decays into a bottom and a charm quark, H ^± → cb. The analysis focuses on a selection enriched in top-quark pair production, where one top quark decays into a leptonically decaying W boson and a bottom quark, and the other top quark decays into a charged Higgs boson and a bottom quark. This topology leads to a lepton-plus-jets final state, characterised by an isolated electron or muon and at least four jets. The search exploits the high multiplicity of jets containing b-hadrons, and deploys a neural network classifier that uses the kinematic differences between the signal and the background. The search uses a dataset of proton-proton collisions collected at a centre-of-mass energy s = 13 TeV between 2015 and 2018 with the ATLAS detector at CERN’s Large Hadron Collider, amounting to an integrated luminosity of 139 fb⁻¹. Observed (expected) 95% confidence-level upper limits between 0.15% (0.09%) and 0.42% (0.25%) are derived for the product of branching fractions B (t → H ^± b) × B(H ^± → cb) for charged Higgs boson masses between 60 and 160 GeV, assuming the SM production of the top-quark pairs. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

来源：评论

学校读者我要写书评

暂无评论

Spatial 3D Matern priors for fast Whole-Brain fMRI analysis

arXiv

引用

arXiv 2019年

作者： Siden, Per Lindgren, Finn Bolinz, David Eklund, Anders Villani, Mattias Division of Statistics and Machine Learning Dept. of Computer and Information Science Linkoping University LinkopingSE-58183 School of Mathematics University of Edinburgh Peter Guthrie Tait Road EdinburghEH93FD United Kingdom Division of Applied Mathematics and Statistics Dept. of Mathematical Sciences Chalmers and University of Gothenburg GoteborgSE-41296 Division of Medical Informatics Dept. of Biomedical Engineering Center for Medical Image Science and Visualization Linkoping University LinkopingSE-58183 Department of Statistics Stockholm University StockholmSE-10691 Sweden

Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data. The proposed inference algorithms are computationally demanding however, and the proposed spatial priors have several less appealing properties, such as being improper and having infinite spatial range. Our paper proposes a statistical inference framework for functional magnetic resonance imaging (fMRI) analysis based on the class of Matern covariance functions. The framework uses the Gaussian Markov random field (GMRF) representation of Matern fields via the stochastic partial difierential equation (SPDE) approach of Lindgren et al. (2011). This allows for more exible and interpretable spatial priors, while maintaining the sparsity required for fast inference in the high-dimensional whole-brain setting. We develop an accelerated stochastic gradient descent (SGD) optimization algorithm for empirical Bayes (EB) inference of the spatial hyperparameters. Conditional on the inferred hyperparameters, we make a fully Bayesian treatment of the main parameters of interest, that is, the brain activity coeficients. We apply the Matern prior to both experimental and simulated task-fMRI data and clearly demonstrate that this is a more reasonable choice than the previously used priors, by using prior simulation, cross validation and visual inspection of the resulting activation maps. Additionally, to illustrate the potential of the SPDE formulation, we derive an anisotropic version of the Matern 3D prior. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

TOI-2458 b: A mini-Neptune consistent with in situ hot Jupiter formation

arXiv

引用

arXiv 2024年

作者： Šubjak, Jan Gandolfi, Davide Goffo, Elisa Rapetti, David Jankowski, Dawid Mizuki, Toshiyuki Dai, Fei Serrano, Luisa M. Wilson, Thomas G. Goździewski, Krzysztof Nowak, Grzegorz Jenkins, Jon M. Twicken, Joseph D. Winn, Joshua N. Bieryla, Allyson Ciardi, David R. Cochran, William D. Collins, Karen A. Deeg, Hans J. Garcia, Rafael A. Guenther, Eike W. Hatzes, Artie P. Kabath, Petr Korth, Judith Latham, David W. Livingston, John H. Lund, Michael B. Mathur, Savita Narita, Norio Orell-Miquel, Jaume Palle, Enric Persson, Carina M. Redfield, Seth Schwarz, Richard P. Watanabe, David Ziegler, Carl Astronomical Institute Czech Academy of Sciences Fričova 298 Ondřejov251 65 Czech Republic Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Thuringer Landessternwarte Tautenburg Sternwarte 5 Tautenburg07778 Germany NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Toruń87-100 Poland Astrobiology Center of NINS 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-2 Osawa Mitaka Tokyo181-8588 Japan Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom SETI Institute Mountain ViewCA94043 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States NASA Exoplanet Science Institute-Caltech/IPAC PasadenaCA91125 United States McDonald Observatory The University of Texas AustinTX United States Center for Planetary Systems Habitability The University of Texas AustinTX United States C. Via Lactea S/N Tenerife La LagunaE-38205 Spain Dept. de Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Saclay Universite Paris Cite CEA CNRS AIM Gif-sur-Yvette91191 France Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Chalmers University of Technology Departmen

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the mainsequence with a mass of M*= 1.05 ± 0.03Mo, a radius of R*= 1.31 ± 0.03 Ro, an effective temperature of Teff = 6005 ± 50 K, and a metallicity of -0.10 ± 0.05 dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of ∼3.74 days, a mass of Mp = 13.31±0.99M⊕ and a radius of Rp = 2.83±0.20 R⊕. The host star TOI-2458 shows a short activity cycle of ∼54 days revealed in the HARPS S-index and Hα times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be i*= 10.6+13.3 -10.6 degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of P = 16.55 ± 0.06 days and a minimum mass of Mp sin i = 10.22 ± 1.90M⊕. Using dynamical stability analysis, we constrained the mass of this planet to the range Mc (10, 25)M⊕. © 2024, CC BY.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：