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arXiv 2018年

作者： Ryu, J. Jon Ganguly, Shouvik Kim, Young-Han Noh, Yung-Kyun Lee, Daniel D. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States XCOM Labs San DiegoCA92121 United States Gauss Labs Inc Seoul Korea Republic of Department of Computer Science Hanyang University Seoul04763 Korea Republic of School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of Cornell Tech New YorkNY10044 United States Global AI Center for Samsung Research

A new approach to L2-consistent estimation of a general density functional using k-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function f of the densities at each point. The estimator is designed to be asymptotically unbiased, using the convergence of the normalized volume of a k-nearest neighbor ball to a Gamma distribution in the large-sample limit, and naturally involves the inverse Laplace transform of a scaled version of the function f. Some instantiations of the proposed estimator recover existing k-nearest neighbor based estimators of Shannon and Rényi entropies and Kullback–Leibler and Rényi divergences, and discover new consistent estimators for many other functionals such as logarithmic entropies and divergences. The L2-consistency of the proposed estimator is established for a broad class of densities for general functionals, and the convergence rate in mean squared error is established as a function of the sample size for smooth, bounded densities. Copyright © 2018, The Authors. All rights reserved.

关键词： Mean square error

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Stochastic sequential neural networks with structured inference

arXiv

引用

arXiv 2017年

作者： Liu, Hao Bai, Haoli He, Lirong Xu, Zenglin SMILE Lab Yingcai Honors College University of Electronic Science and Technology of China SMILE Lab Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China

Unsupervised structure learning in high-dimensional time series data has attracted a lot of research interests. For example, segmenting and labelling high dimensional time series can be helpful in behavior understanding and medical diagnosis. Recent advances in generative sequential modeling have suggested to combine recurrent neural networks with state space models (e.g., Hidden Markov Models). This combination can model not only the long term dependency in sequential data, but also the uncertainty included in the hidden states. Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables. For accurate and efficient inference, we present a bi-directional inference network by reparamterizing the categorical segmentation and labels with the recent proposed Gumbel-Softmax approximation and resort to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Observation of the suppressed Λb0→DpK− decay with D→K+π− and measurement of its CP asymmetry

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Physical Review D 2021年第11期104卷 112008-112008页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellán Beteta F. J. Abudinen Gallego T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. Agapopoulou C. A. aidala S. aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato J. L. Amey Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite M. Barbetti R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels S. Bashir G. Bassi B. Batsukh A. Battig A. Bay A. Beck M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura L. Buonincontri A. T. Burke C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli D. Cervenkov A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cic Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institute of Physics E

A study of Λb0 baryon decays to the DpK− final state is presented based on a proton-proton collision data sample corresponding to an integrated luminosity of 9 fb−1 collected with the LHCb detector. Two Λb0 decays are considered, Λb0→DpK− with D→K−π+ and D→K+π−, where D represents a superposition of D0 and D¯0 states. The latter process is expected to be suppressed relative to the former, and is observed for the first time. The ratio of branching fractions of the two decays is measured, and the CP asymmetry of the suppressed mode, which is sensitive to the Cabibbo-Kobayashi-Maskawa angle γ, is also reported.

关键词： Hadronic decays CP violation

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The role of artificial intelligence in achieving the Sustainable Development Goals

arXiv

引用

arXiv 2019年

作者： Vinuesa, Ricardo Azizpour, Hossein Leite, Iolanda Balaam, Madeline Dignum, Virginia Domisch, Sami Felländer, Anna Langhans, Simone Tegmark, Max Nerini, Francesco Fuso Linné FLOW Centre KTH Mechanics StockholmSE-100 44 Sweden School of Electrical Engineering and Computer Science KTH Royal Institute Of Technology Stockholm Sweden Division of Robotics Perception and Learning School of EECS KTH Royal Institute Of Technology Stockholm Sweden Division of Media Technology and Interaction Design KTH Royal Institute of Technology Lindstedtsvägen 3 Stockholm Sweden Responsible AI Lab Umeå University UmeåSE-90358 Sweden Leibniz-Institute of Freshwater Ecology and Inland Fisheries Müggelseedamm 310 Berlin12587 Germany Division of Media Technology and Interaction Design KTH Royal Institute of Technology Lindstedtsvägen 3 Stockholm Sweden Leioa Spain Department of Zoology University of Otago 340 Great King Street Dunedin9016 New Zealand Center for Brains Minds & Machines Massachusetts Institute of Technology CambridgeMA02139 United States KTH Royal Institute of Technology Brinellvagen 68 StockholmSE-100 44 Sweden

The emergence of artificial intelligence (ai) and its progressively wider impact on many sectors across the society requires an assessment of its effect on sustainable development. Here we analyze published evidence of positive or negative impacts of ai on the achievement of each of the 17 goals and 169 targets of the 2030 Agenda for Sustainable Development. We find that ai can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets. Notably, ai enables new technologies that improve efficiency and productivity, but it may also lead to increased inequalities among and within countries, thus hindering the achievement of the 2030 Agenda. The fast development of ai needs to be supported by appropriate policy and regulation. Otherwise, it would lead to gaps in transparency, accountability, safety and ethical standards of ai-based technology, which could be detrimental towards the development and sustainable use of ai. Finally, there is a lack of research assessing the medium- and long-term impacts of ai. It is therefore essential to reinforce the global debate regarding the use of ai and to develop the necessary regulatory insight and oversight for ai-based technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Planning

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A question answering approach to emotion cause extraction

arXiv

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arXiv 2017年

作者： Gui, Lin Hu, Jiannan He, Yulan Xu, Ruifeng Lu, Qin Du, Jiachen Shenzhen Graduate School Harbin Institute of Technology College of Mathematics and Computer Science Fuzhou University School of Engineering and Applied Science Aston University Guangdong Provincial Engineering Technology Research Center for Data Science Department of Computing Hong Kong Polytechnic University

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure. Copyright © 2017, The Authors. All rights reserved.

关键词： Extraction

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Leveraging Neighborhood Summaries for Efficient Queries on Existing Relational RDF Systems

Leveraging Neighborhood Summaries for Efficient Queries on E...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Lei Gai Xiaoming Wang Tengjiao Wang School of Electrical Engineering and Computer Science Peking University Beijing P.R. China CTBT China National Data Center Beijing P.R. China

Using structural informations to summarize graph-structured RDF data is helpful in tackling query performance issues. However, leveraging structural indexes needs to revise or even redesign the internal of RDF systems. Given an RDF dataset that have already been bulk loaded into a relational RDF system, we aim at improving the query performance on such systems. We do so by summarizing neighborhood structures and encoding them into triples which can be managed along side the exist instance data. At query time, we optimally select the effective structural patterns, and adding these patterns to the existing queries to gain an improved query performance. Empirical evaluations shown the effectiveness of our method.

关键词： Resource description framework Indexes Engines Query processing Metadata Encoding Runtime

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How heterogeneous susceptibility and recovery rates affect the spread of epidemics on networks

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Infectious Disease Modelling 2017年第3期2卷 353-367页

作者： Wei Gou Zhen Jin School of Computer Science and Control Engineering North University of ChinaShanxiTaiyuan030012People's Republic of China Complex Systems Research Center Shanxi UniversityShanxiTaiyuan030006People's Republic of China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityShanxiTaiyuan030006People’s Republic of China

In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field *** from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree,our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery ***,we analytically study the basic reproductive number and the final epidemic *** with numerical simulations,it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are *** the mean of these two distributions is identical,increasing the variance of susceptibility may block the spread of epidemics,while the corresponding increase in the variance of disease course has little effect on the final epidemic *** is also shown that positive correlations between individual susceptibility,course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence,whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.

关键词： Networks Heterogeneity Susceptibility Recovery rates Correlation The basic reproductive number The final epidemic size

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Comments on a privacy preserving public auditing mechanism for shared cloud data 17

Comments on a privacy preserving public auditing mechanism f...

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4th Multidisciplinary International Social Networks Conference, MISNC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Wu, Mu-En Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Harbin Institute of Technology Shenzhen Graduate School Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Mathematic Soochow University Taipei Taiwan

ISBN: (纸本)9781450348812

Auditing mechanisms are received much attentions from researchers due to the developing of cloud storage. Recently, Wang et al. proposed a privacy preserving public auditing mechanism for shared cloud data with supporting group dynamic. However, we find some security flaws in their mechanism. In this paper, a forgery attack and a data corruption attack are proposed to demonstrate the insecurity. Then, we propose a modified scheme based on their mechanism. The security analysis is demonstrated that our improvement can resist the mentioned two attacks. © 2017 ACM.

关键词： Cloud storage

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Dark energy survey year 3 results: High-precision measurement and modeling of galaxy-galaxy lensing

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Physical Review D 2022年第8期105卷 083528-083528页

作者： J. Prat J. Blazek C. Sánchez I. Tutusaus S. Pandey J. Elvin-Poole E. Krause M. A. Troxel L. F. Secco A. Amon J. DeRose G. Zacharegkas C. Chang B. Jain N. MacCrann Y. Park E. Sheldon G. Giannini S. Bocquet C. To A. Alarcon O. Alves F. Andrade-Oliveira E. Baxter K. Bechtol M. R. Becker G. M. Bernstein H. Camacho A. Campos A. Carnero Rosell M. Carrasco Kind R. Cawthon R. Chen A. Choi J. Cordero M. Crocce C. Davis J. De Vicente H. T. Diehl S. Dodelson C. Doux A. Drlica-Wagner K. Eckert T. F. Eifler F. Elsner S. Everett X. Fang A. Farahi A. Ferté P. Fosalba O. Friedrich M. Gatti D. Gruen R. A. Gruendl I. Harrison W. G. Hartley K. Herner H. Huang E. M. Huff D. Huterer M. Jarvis N. Kuropatkin P.-F. Leget P. Lemos A. R. Liddle J. McCullough J. Muir J. Myles A. Navarro-Alsina A. Porredon M. Raveri M. Rodriguez-Monroy R. P. Rollins A. Roodman R. Rosenfeld A. J. Ross E. S. Rykoff J. Sanchez I. Sevilla-Noarbe T. Shin A. Troja T. N. Varga N. Weaverdyck R. H. Wechsler B. Yanny B. Yin J. Zuntz T. M. C. Abbott M. Aguena S. Allam J. Annis D. Bacon D. Brooks D. L. Burke J. Carretero C. Conselice M. Costanzi L. N. da Costa M. E. S. Pereira S. Desai J. P. Dietrich P. Doel A. E. Evrard I. Ferrero B. Flaugher J. Frieman J. García-Bellido E. Gaztanaga D. W. Gerdes T. Giannantonio J. Gschwend G. Gutierrez S. R. Hinton D. L. Hollowood K. Honscheid D. J. James K. Kuehn O. Lahav H. Lin M. A. G. Maia J. L. Marshall P. Martini P. Melchior F. Menanteau C. J. Miller R. Miquel J. J. Mohr R. Morgan R. L. C. Ogando A. Palmese F. Paz-Chinchón D. Petravick A. A. Plazas Malagón E. Sanchez S. Serrano M. Smith M. Soares-Santos E. Suchyta G. Tarle D. Thomas J. Weller Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA Laboratory of Astrophysics École Polytechnique Fédérale de Lausanne (EPFL) Observatoire de Sauverny 1290 Versoix Switzerland Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Institut d’Estudis Espacials de Catalunya (IEEC) 08034 Barcelona Spain Institute of Space Sciences (ICE CSIC) Campus UAB Carrer de Can Magrans Sin número 08193 Barcelona Spain Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Department of Physics The Ohio State University Columbus Ohio 43210 USA Department of Astronomy/Steward Observatory University of Arizona 933 North Cherry Avenue Tucson Arizona 85721-0065 USA Department of Physics Duke University Durham North Carolina 27708 USA Kavli Institute for Particle Astrophysics and Cosmology P. O. Box 2450 Stanford University Stanford California 94305 USA Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley California 94720 USA Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge CB3 0WA United Kingdom Kavli Institute for the Physics and Mathematics of the Universe (WPI) UTIAS The University of Tokyo Kashiwa Chiba 277-8583 Japan Brookhaven National Laboratory Building 510 Upton New York 11973 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 81679 Munich Germany Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory Menlo Park California 94025 USA Argonne National Laboratory 9700 South Cass A

We present and characterize the galaxy-galaxy lensing signal measured using the first three years of data from the Dark Energy Survey (DES Y3) covering 4132 deg2. These galaxy-galaxy measurements are used in the DES Y3 3×2 pt cosmological analysis, which combines weak lensing and galaxy clustering information. We use two lens samples: a magnitude-limited sample and the redmagic sample, which span the redshift range ∼0.2–1 with 10.7 and 2.6 M galaxies, respectively. For the source catalog, we use the metacalibration shape sample, consisting of ≃100 M galaxies separated into four tomographic bins. Our galaxy-galaxy lensing estimator is the mean tangential shear, for which we obtain a total SNR of ∼148 for maglim (∼120 for redmagic), and ∼67 (∼55) after applying the scale cuts of 6 Mpc/h. Thus we reach percent-level statistical precision, which requires that our modeling and systematic-error control be of comparable accuracy. The tangential shear model used in the 3×2 pt cosmological analysis includes lens magnification, a five-parameter intrinsic alignment model, marginalization over a point mass to remove information from small scales and a linear galaxy bias model validated with higher-order terms. We explore the impact of these choices on the tangential shear observable and study the significance of effects not included in our model, such as reduced shear, source magnification, and source clustering. We also test the robustness of our measurements to various observational and systematics effects, such as the impact of observing conditions, lens-source clustering, random-point subtraction, scale-dependent metacalibration responses, point spread function residuals, and B modes.

关键词： Cosmological parameters Dark energy Dark matter Gravitational lenses Large scale structure of the Universe Optical, UV, & IR astronomy

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Precise measurement of the fs/fd ratio of fragmentation fractions and of Bs0 decay branching fractions

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Physical Review D 2021年第3期104卷 032005-032005页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. aidala S. aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back S. Baker P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes A. Camboni P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Physics University of Warwick Coventry United Kingdom Imperial College Lo

The ratio of the Bs0 and B0 fragmentation fractions, fs/fd, in proton-proton collisions at the LHC, is obtained as a function of B-meson transverse momentum and collision center-of-mass energy from the combined analysis of different B-decay channels measured by the LHCb experiment. The results are described by a linear function of the meson transverse momentum or with a function inspired by Tsallis statistics. Precise measurements of the branching fractions of the Bs0→J/ψϕ and Bs0→Ds−π+ decays are performed, reducing their uncertainty by about a factor of 2 with respect to previous world averages. Numerous Bs0 decay branching fractions, measured at the LHCb experiment, are also updated using the new values of fs/fd and branching fractions of normalization channels. These results reduce a major source of systematic uncertainty in several searches for new physics performed through measurements of Bs0 branching fractions.

关键词： Branching fraction Fragmentation into hadrons

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