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Classification of pulsars with Dirichlet process Gaussian mixture model

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arXiv 2019年

作者： Ay, Fahrettin Ince, Gökhan Kamaşak, Mustafa E. Yavuz Ekşi, K. Istanbul Technical University Faculty of Computer and Informatics Computer Engineering Department Istanbul34469 Turkey Istanbul Technical University Faculty of Science and Letters Physics Engineering Department Istanbul34469 Turkey

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINSs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magneto-thermal spin evolution models and fallback discs. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

An information-theoretical approach to the information capacity and cost-effectiveness evaluation of color palettes

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arXiv 2019年

作者： Sirmen, Refik Tanju Üstündağ, Burak Berk Istanbul Technical University Institute of Science Engineering & Tech Turkey Istanbul Technical University Faculty of Computer & Informatics Turkey

Colors are used as effective tools of representing and transferring information. Number of colors in a palette is the direct arbiter of the information conveying capacity. Yet it should be well elaborated, since increasing the entropy by adding colors comes with its cost on decoding. Despite the possible effects upon diverse applications, a methodology for cost-effectiveness evaluation of palettes seems deficient. In this work, this need is being addressed from an information-theoretical perspective, via the articulated metrics and formulae. Besides, the proposed metrics are computed for some developed and known palettes, and observed results are evaluated. Copyright © 2019, The Authors. All rights reserved.

关键词： Color

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Predicting Software Vulnerabilities Using Topic Modeling with Issues

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Predicting Software Vulnerabilities Using Topic Modeling wit...

International Conference on computer Science and engineering (UBMK)

作者： Fatma Gül Bulut Haluk Altunel Ayşe Tosun Softtech Istanbul Turkey Softtech Ankara Turkey Faculty of Computer Engineering and Informatics Istanbul Technical University Istanbul Turkey

The existence of software vulnerabilities is an indicator of the reliability and safety of software products. Software vulnerabilities can be predicted using metrics derived from developers, organization, code and textual data. In this work, we aim to predict the software vulnerabilities using issue records in two different datasets. The first dataset consists of six-months of issue records collected in a corporate, whereas the second dataset consists of Wireshark project bug records from 2017 to 2018. Prediction models were established using six different machine learning for which textual descriptions of issue records were converted into topic models. A regression model was established for the corporate company in which textual description of issue records were used as the input, and the number of vulnerabilities were used as the output of the model. A classification model was established for Wireshark dataset in which textual descriptions of bug records were used as input of the model, and the class of vulnerable-prone or not is used as the output. The best regression model results are 0.23, 0.30, 0.44 MdMRE values, respectively. The best classification model result is 74% recall score.

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Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

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Enhance the Mammogram Images for Both Segmentation and Featu...

2019 International Conference on Advanced Science and engineering, ICOASE 2019

作者： Zebari, Dilovan Asaad Haron, Habibollah Zeebaree, Subhi R.M. Zeebaree, Diyar Qader Faculty of Engineering School of Computing University Teknologi Malaysia Johor Malaysia University Teknologi Malaysia Faculty of Computing Department of Computer Science Johor Malaysia IT Dept. Technical College of Informatics Akre Duhok Scientific Research and Development Center Nawroz University Duhok Iraq

ISBN: (纸本)9781538693438

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of computer-Aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods. © 2019 IEEE.

关键词： Wavelet transforms

An empirical study on arrival rates of limit orders and order cancellation rates in Borsa istanbul

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arXiv 2019年

作者： Altinigne, Can Yilmaz Ozkan, Harun Kupeli, Veli Can Cataltepe, Zehra School of Computer and Communication Sciences EPFL Switzerland Matriks Bilgi Dağıtım Hizmetleri Istanbul34396 Turkey Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Order book dynamics play an important role in both execution time and price formation of orders in an exchange market. In this study, we aim to model the limit order arrival rates in the vicinity of the best bid and the best ask price levels. We use limit order book data for Garanti Bank, which is one of the most traded stocks in Borsa istanbul. In order to model the daily, weekly, and monthly arrival of limit order quantities, three different discrete probability distributions are tested: Geometric, Beta-Binomial and Discrete Weibull. Additionally, two theoretical models, namely, Exponential and Power law are also tested. We aim to model the arrival rates in the first fifteen bid and ask price levels. We use L1 norms in order to calculate the goodness-of-fit statistics. Furthermore, we examine the structure of weekly and monthly mean cancellation rates in the first ten bid and ask price levels. Copyright © 2019, The Authors. All rights reserved.

关键词： Weibull distribution

Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video

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arXiv 2019年

作者： Aslan, Suleyman Gudukbay, Ugur Ugur Toreyin, B. Enis Cetin, A. Department of Computer Engineering Bilkent University Ankara Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Dept. of Electrical and Computer Eng. University of Illinois at Chicago ChicagoIL United States

Real-Time flame detection is crucial in video based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require substantial amount of labeled data. In order to have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-Temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatiotemporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false positive rates in realtime. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

Search for W′ bosons decaying to a top and a bottom quark in leptonic final states in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第5期2024卷 46页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Van Mechelen, P. Bols, E.S. D’hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A search for W′ bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb⁻¹ of proton-proton collisions at a center-of-mass energy of 13 TeV. Good agreement with the standard model expectation is observed and no evidence for the existence of the W′ boson is found over the mass range examined. The largest observed deviation from the standard model expectation is found for a W′ boson mass (mW_′) hypothesis of 3.8 TeV with a relative decay width of 1%, with a local (global) significance of 2.6 (2.0) standard deviations. Upper limits on the production cross sections of W′ bosons decaying to a top and a bottom quark are set. Left- and right-handed W′ bosons with mW_′ below 3.9 and 4.3 TeV, respectively, are excluded at the 95% confidence level, under the assumption that the new particle has a narrow decay width. Limits are also set for relative decay widths up to 30%. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Vector Boson Production

Measurement of the τ lepton polarization in Z boson decays in proton-proton collisions at (Formula presented.)

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Journal of High Energy Physics 2024年第1期2024卷 101页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The polarization of τ leptons is measured using leptonic and hadronic τ lepton decays in Z → τ+ τ− events in proton-proton collisions at (Formula presented.) recorded by CMS at the CERN LHC with an integrated lum... 详细信息

The polarization of τ leptons is measured using leptonic and hadronic τ lepton decays in Z → τ⁺ τ⁻ events in proton-proton collisions at (Formula presented.) recorded by CMS at the CERN LHC with an integrated luminosity of 36.3 fb⁻¹. The measured τ⁻ lepton polarization at the Z boson mass pole is P_τ(Z) = −0.144 ± 0.006 (stat) ± 0.014 (syst) = −0.144 ± 0.015, in good agreement with the measurement of the τ lepton asymmetry parameter of A_τ = 0.1439 ± 0.0043 = − P_τ(Z) at LEP. The τ lepton polarization depends on the ratio of the vector to axial-vector couplings of the τ leptons in the neutral current expression, and thus on the effective weak mixing angle sin² θWeff , independently of the Z boson production mechanism. The obtained value sin² θWeff = 0.2319 ± 0.0008(stat) ± 0.0018(syst) = 0.2319 ± 0.0019 is in good agreement with measurements at e⁺ e⁻ colliders. [Figure not available: see fulltext.]. © 2024, The Author(s).

关键词： Hadron-Hadron Scattering Tau Physics