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1. Microsoft Academic Graph: When experts are not enough

Author

Wang, Kuansan, Shen, Zhihong, Huang, Chiyuan, Wu, Chieh-Han, Dong, Yuxiao, and Kanakia, Anshul

Subjects
Science (General), Q1-390
Abstract

An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.

Published
2020
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2. Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding

Author

Zhang, Yu, Cheng, Hao, Shen, Zhihong, Liu, Xiaodong, Wang, Ye-Yi, and Gao, Jianfeng

Subjects
Computer Science - Computation and Language, Computer Science - Digital Libraries, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery. Pre-trained language models (LMs) have shown effectiveness in these tasks, especially when tuned via contrastive learning. However, jointly utilizing pre-training data across multiple heterogeneous tasks (e.g., extreme multi-label paper classification, citation prediction, and literature search) remains largely unexplored. To bridge this gap, we propose a multi-task contrastive learning framework, SciMult, with a focus on facilitating common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. To be specific, we explore two techniques -- task-aware specialization and instruction tuning. The former adopts a Mixture-of-Experts Transformer architecture with task-aware sub-layers; the latter prepends task-specific instructions to the input text so as to produce task-aware outputs. Extensive experiments on a comprehensive collection of benchmark datasets verify the effectiveness of our task-aware specialization strategy, where we outperform state-of-the-art scientific pre-trained LMs. Code, datasets, and pre-trained models can be found at https://scimult.github.io/., Comment: 17 pages; Accepted to Findings of EMNLP 2023 (Project Page: https://scimult.github.io/)

Published
2023

3. S2CTrans: Building a bridge from SPARQL to Cypher

Author

Zhao, Zihao, Ge, Xiaodong, and Shen, Zhihong

Subjects
Computer Science - Databases
Abstract

In graph data applications, data is primarily maintained using two models: RDF (Resource Description Framework) and property graph. The property graph model is widely adopted by industry, leading to property graph databases generally outperforming RDF databases in graph traversal query performance. However, users often prefer SPARQL as their query language, as it is the W3C's recommended standard. Consequently, exploring SPARQL-to-Property-Graph-Query-Language translation is crucial for enhancing graph query language interoperability and enabling effective querying of property graphs using SPARQL. Despite the substantial differences in semantic representation and processing logic between SPARQL and property graph query languages like Cypher, this paper demonstrates the feasibility of translating SPARQL to Cypher for graph traversal queries using graph relational algebra. We present the S2CTrans framework, which achieves SPARQL-to-Cypher translation while preserving the original semantics. Experimental results with the Berlin SPARQL Benchmark (BSBM) datasets show that S2CTrans successfully converts most SELECT queries in the SPARQL 1.1 specification into type-safe Cypher statements, maintaining result consistency and improving the efficiency of data querying using SPARQL.

Published
2023

4. A Reference Software Architecture for Managing, Processing and Serving Multi-field Scientific Data

Author

Wang, Huajin, Shen, Zhihong, Lu, Changfa, Zhu, Xiaojie, Zhou, Yuanchun, Feng, Weihua, Wang, Yongsheng, Chinese Academy of Sciences, Ministry of Education of the PRC, Ministry of Science and Technology of the PRC, China Association for Science and Technology, Chinese Academy of Social Sciences, Chinese Academy of Engineering, National Natural Science Foundation of China, and Chinese Academy of Agricultural Sciences

Published
2024
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5. MMDBench: A Benchmark for Hybrid Query in Multimodal Database

Author

Mao, Along, Hu, Chuan, Li, Chong, Wang, Huajin, Rao, Junjian, Wang, Kainan, Shen, Zhihong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hunold, Sascha, editor, Xie, Biwei, editor, and Shu, Kai, editor

Published
2024
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10. Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification

Author

Zhang, Yu, Shen, Zhihong, Wu, Chieh-Han, Xie, Boya, Hao, Junheng, Wang, Ye-Yi, Wang, Kuansan, and Han, Jiawei

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are often costly to obtain and suffer from a long-tailed label distribution (i.e., many labels occur only a few times in the training set). In this paper, we study LMTC under the zero-shot setting, which does not require any annotated documents with labels and only relies on label surface names and descriptions. To train a classifier that calculates the similarity score between a document and a label, we propose a novel metadata-induced contrastive learning (MICoL) method. Different from previous text-based contrastive learning techniques, MICoL exploits document metadata (e.g., authors, venues, and references of research papers), which are widely available on the Web, to derive similar document-document pairs. Experimental results on two large-scale datasets show that: (1) MICoL significantly outperforms strong zero-shot text classification and contrastive learning baselines; (2) MICoL is on par with the state-of-the-art supervised metadata-aware LMTC method trained on 10K-200K labeled documents; and (3) MICoL tends to predict more infrequent labels than supervised methods, thus alleviates the deteriorated performance on long-tailed labels., Comment: 12 pages; Accepted to WWW 2022

Published
2022

11. Heavy Flavour Physics and CP Violation at LHCb: a Ten-Year Review

Author

Chen, Shanzhen, Li, Yiming, Qian, Wenbin, Shen, Zhihong, Xie, Yuehong, Yang, Zhenwei, Zhang, Liming, and Zhang, Yanxi

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

Heavy flavour physics provides excellent opportunities to indirectly search for new physics at very high energy scales and to study hadron properties for deep understanding of the strong interaction. The LHCb experiment has been playing a leading role in the study of heavy flavour physics since the start of the LHC operations about ten years ago, and made a range of high-precision measurements and unexpected discoveries, which may have far-reaching implications on the field of particle physics. This review highlights a selection of the most influential physics results on CP violation, rare decays, and heavy flavour production and spectroscopy obtained by LHCb using the data collected during the first two operation periods of the LHC. The upgrade plan of LHCb and the physics prospects are also briefly discussed., Comment: Invited review for Frontiers of Physics

Published
2021
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13. PandaDB: Understanding Unstructured Data in Graph Database

Author

Zhao, Zihao, Shen, Zhihong, Tang, Mingjie, Hu, Chuan, Wang, Huajin, and Zhou, Yuanchun

Subjects
Computer Science - Databases
Abstract

Unstructured data(e.g., images, videos, PDF files, etc.) contain semantic information, for example, the facial feature of a person and the plate number of a vehicle. There could be semantic relationships between data items which are not explicitly represented. For example, a person's face may appear in two irrelevant photos. Also, much information is represented as structured data(e.g., the person's name and age). End-users prefer to query the semantic information from unstructured data together with structured data based on the potential relationships among them. However, due to the lack of a unified database system for structured and unstructured data, developers have to comprise multiple systems and runtime together to answer these queries. In this work, we build an open-source graph database named PandaDB to consistently manage and query structured and unstructured data. We first introduce a graph data model to manage structured and unstructured data, then propose a new query language to understand the semantics of the unstructured data in the graph. Next, we develop a new cost model and related query optimization techniques to speed up the unstructured data processing pipeline. Finally, we optimize the unstructured data storage and provide the index to speed up the query processing over unstructured data. PandaDB is widely used in industrial applications like FinTech, Knowledge Graph, and Recommendation System. The results show PandaDB can support a large scale of unstructured data query processing in a graph., Comment: 17 pages

Published
2021

14. Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

Author

Wang, Yu, Li, Jinchao, Naumann, Tristan, Xiong, Chenyan, Cheng, Hao, Tinn, Robert, Wong, Cliff, Usuyama, Naoto, Rogahn, Richard, Shen, Zhihong, Qin, Yang, Horvitz, Eric, Bennett, Paul N., Gao, Jianfeng, and Poon, Hoifung

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language, Computer Science - Digital Libraries
Abstract

Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months. In general, biomedical literature expands by two papers every minute, totalling over a million new papers every year. Search in the biomedical realm, and many other vertical domains is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as a promising direction to overcome the annotation bottleneck. We propose a general approach for vertical search based on domain-specific pretraining and present a case study for the biomedical domain. Despite being substantially simpler and not using any relevance labels for training or development, our method performs comparably or better than the best systems in the official TREC-COVID evaluation, a COVID-related biomedical search competition. Using distributed computing in modern cloud infrastructure, our system can scale to tens of millions of articles on PubMed and has been deployed as Microsoft Biomedical Search, a new search experience for biomedical literature: https://aka.ms/biomedsearch., Comment: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021 Applied Data Science Track

Published
2021
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16. Is preprint the future of science? A thirty year journey of online preprint services

Author

Xie, Boya, Shen, Zhihong, and Wang, Kuansan

Subjects
Computer Science - Digital Libraries, Computer Science - Computers and Society
Abstract

Preprint is a version of a scientific paper that is publicly distributed preceding formal peer review. Since the launch of arXiv in 1991, preprints have been increasingly distributed over the Internet as opposed to paper copies. It allows open online access to disseminate the original research within a few days, often at a very low operating cost. This work overviews how preprint has been evolving and impacting the research community over the past thirty years alongside the growth of the Web. In this work, we first report that the number of preprints has exponentially increased 63 times in 30 years, although it only accounts for 4% of research articles. Second, we quantify the benefits that preprints bring to authors: preprints reach an audience 14 months earlier on average and associate with five times more citations compared with a non-preprint counterpart. Last, to address the quality concern of preprints, we discover that 41% of preprints are ultimately published at a peer-reviewed destination, and the published venues are as influential as papers without a preprint version. Additionally, we discuss the unprecedented role of preprints in communicating the latest research data during recent public health emergencies. In conclusion, we provide quantitative evidence to unveil the positive impact of preprints on individual researchers and the community. Preprints make scholarly communication more efficient by disseminating scientific discoveries more rapidly and widely with the aid of Web technologies. The measurements we present in this study can help researchers and policymakers make informed decisions about how to effectively use and responsibly embrace a preprint culture.

Published
2021

17. MATCH: Metadata-Aware Text Classification in A Large Hierarchy

Author

Zhang, Yu, Shen, Zhihong, Dong, Yuxiao, Wang, Kuansan, and Han, Jiawei

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set. Commonly, the metadata of the given documents and the hierarchy of the labels are available in real-world applications. However, most existing studies focus on only modeling the text information, with a few attempts to utilize either metadata or hierarchy signals, but not both of them. In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels). To address this problem, we present the MATCH solution -- an end-to-end framework that leverages both metadata and hierarchy information. To incorporate metadata, we pre-train the embeddings of text and metadata in the same space and also leverage the fully-connected attentions to capture the interrelations between them. To leverage the label hierarchy, we propose different ways to regularize the parameters and output probability of each child label by its parents. Extensive experiments on two massive text datasets with large-scale label hierarchies demonstrate the effectiveness of MATCH over state-of-the-art deep learning baselines., Comment: 12 pages; Accepted to WWW 2021; Some typos fixed

Published
2021

18. A Key-Value Based Approach to Scalable Graph Database

Author

Zhao, Zihao, Hu, Chuan, Shen, Zhihong, Mao, Along, Ren, Hao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Amagasa, Toshiyuki, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2023
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19. PandaDB: An AI-Native Graph Database for Unified Managing Structured and Unstructured Data

Author

Zhao, Zihao, Shen, Zhihong, Mao, Along, Wang, Huajin, Hu, Chuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Sapino, Maria Luisa, editor, Han, Wook-Shin, editor, El Abbadi, Amr, editor, Dobbie, Gill, editor, Feng, Zhiyong, editor, Shao, Yingxiao, editor, and Yin, Hongzhi, editor

Published
2023
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20. CORD-19: The COVID-19 Open Research Dataset

Author

Wang, Lucy Lu, Lo, Kyle, Chandrasekhar, Yoganand, Reas, Russell, Yang, Jiangjiang, Burdick, Doug, Eide, Darrin, Funk, Kathryn, Katsis, Yannis, Kinney, Rodney, Li, Yunyao, Liu, Ziyang, Merrill, William, Mooney, Paul, Murdick, Dewey, Rishi, Devvret, Sheehan, Jerry, Shen, Zhihong, Stilson, Brandon, Wade, Alex, Wang, Kuansan, Wang, Nancy Xin Ru, Wilhelm, Chris, Xie, Boya, Raymond, Douglas, Weld, Daniel S., Etzioni, Oren, and Kohlmeier, Sebastian

Subjects
Computer Science - Digital Libraries, Computer Science - Computation and Language
Abstract

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19., Comment: ACL NLP-COVID Workshop 2020

Published
2020

21. TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Author

Shen, Jiaming, Shen, Zhihong, Xiong, Chenyan, Wang, Chi, Wang, Kuansan, and Han, Jiawei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion., Comment: WWW 2020

Published
2020
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22. A Scalable Hybrid Research Paper Recommender System for Microsoft Academic

Author

Kanakia, Anshul, Shen, Zhihong, Eide, Darrin, and Wang, Kuansan

Subjects
Computer Science - Digital Libraries, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

We present the design and methodology for the large scale hybrid paper recommender system used by Microsoft Academic. The system provides recommendations for approximately 160 million English research papers and patents. Our approach handles incomplete citation information while also alleviating the cold-start problem that often affects other recommender systems. We use the Microsoft Academic Graph (MAG), titles, and available abstracts of research papers to build a recommendation list for all documents, thereby combining co-citation and content based approaches. Tuning system parameters also allows for blending and prioritization of each approach which, in turn, allows us to balance paper novelty versus authority in recommendation results. We evaluate the generated recommendations via a user study of 40 participants, with over 2400 recommendation pairs graded and discuss the quality of the results using P@10 and nDCG scores. We see that there is a strong correlation between participant scores and the similarity rankings produced by our system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations. The results of the user survey and associated analysis scripts are made available via GitHub and the recommendations produced by our system are available as part of the MAG on Azure to facilitate further research and light up novel research paper recommendation applications., Comment: 7 pages, 7 figures. Short paper at The Web Conference 2019, San Francisco, USA

Published
2019
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24. Huge mucinous cystic neoplasms with adhesion to the left colon: A case report and literature review

Author

Tang Haijun, Shen Zhihong, and Lu Baochun

Subjects
mucinous cystic neoplasms, precancerous conditions, tissue adhesions, colon, Medicine
Abstract

Mucinous cystic neoplasms (MCN) are rare premalignant neoplasms of the pancreas typically found as single lesions in the pancreatic body and tail of women in the fifth and sixth decade of life, constituting 2–5% of pancreatic neoplasms. We present a 50-year-old female patient with a large tail mass of the pancreatic body (size of 15 cm × 12 cm) with elevated tumor indicators. Computed tomography and magnetic resonance imaging revealed a large cystic lesion with septa and wall nodules. During the operation, the tumor capsule wall adhered to the left half colon and mesentery and could not be detached. Splenectomy and left hemicolectomy were performed. The postoperative pathological examination of the specimens confirmed a premalignant pancreatic mucous cystic tumor with moderate heterocytosis. The preoperative diagnosis of pancreatic MCN and MCN with invasive carcinoma is discussed, considering the characteristics of this case. Age, tumor size, texture, tumor marker elevation, and cystic wall condition are important characteristics of malignant MCN. Nevertheless, it is still very difficult to determine accurately whether an MCN is malignant or not before an actual pathological examination of the resected specimen.

Published
2022
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27. A Web-scale system for scientific knowledge exploration

Author

Shen, Zhihong, Ma, Hao, and Wang, Kuansan

Subjects
Computer Science - Computation and Language, Computer Science - Digital Libraries
Abstract

To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships., Comment: 6 pages, accepted for ACL 2018 demo paper

Published
2018

28. Reducing the Risk of Adverse Events in Patients with Type 2 Diabetes Who are Poorly Treated with Metformin Combined with Acarbose：Dipeptidyl Peptidase-4 Inhibitor is Better Than Insulin

Author

SU Peng, LIU Yukun, LIANG Xiaohua, LIU Xin, YU Yaohui, HUANG Pengfei, BAI Yuru, HE Xiaoyan, SHEN Zhihong, MA Dong

Subjects
diabetes mellitus, type 2, dipeptidyl-peptidaseⅳ inhibitors, metformin, acarbose, drug-related side effects and adverse reactions, cohort studies, retrospective studies, Medicine
Abstract

BackgroundClinically, when metformin (Met) combined with acarbose (Aca) cannot achieve the ideal hypoglycemic effect, a third drug will be usually added, such as insulin (Ins) or dipeptidyl peptidase-4 inhibitor (DPP-4i) etc., but there are few reports on the effect of triple therapy on complications related to type 2 diabetes (T2DM) .ObjectiveTo explore the risk of adverse events of DPP-4i combined with Met and Aca, Ins combined with Met and Aca in the treatment of T2DM patients, in order to provide help for the choice of drugs for clinical T2DM treatment.MethodsIn the retrospective cohort study, patients diagnosed with T2DM and treated with Met+Aca+ DPP-4i or Met+Aca+Ins in Shijiazhuang Second Hospital from November 1, 2017 to August 1, 2020 were selected as the study subject. Telephone follow-up was conducted from November 20, 2017 to August 4, 2020, the follow-up wasn't terminated until a preset outcome occurred, then that was recorded. The three prespecified outcome events were non-fatal cardiovascular disease, death from all causes, and severe hypoglycemic events. The comprehensive outcome events including all-cause death, or composite non-fatal cardiovascular events, or severe hypoglycemic events. Propensity score matching (1∶1 ratio for data matching, caliper value set to 0.02) was used, and multivariate Cox proportional hazards regression model was used to analyze the risk of comprehensive outcome events in T2DM patients after drug treatment. Stratified analysis was performed on the effect of each covariate on the risk of comprehensive outcome events in patients treated with different drugs.ResultsFinally, 1 570 patients with T2DM were enrolled, including 1 089 patients who received Met+Aca+Ins treatment (Met+Aca+Ins group) and 481 patients who received Met+Aca+DPP-4i treatment (Met+Aca+DPP-4i group) . There were 434 cases in both groups after propensity score matching. Compared with the Met+Aca+Ins group, the incidences of comprehensive outcome events (6.53/100 person-per year) , non-fatal cardiovascular disease (5.03/100 person-years) , all-cause death (0.73/100 person-per year) , and severe hypoglycemic (0.73/100 person-er year) were lower in the Met+Aca+ DPP-4i group. The multivariate Cox proportional hazards regression model analysis showed that the risk of comprehensive outcome events in the Met+Aca+DPP-4i group was 67% lower than the Met+Aca+Ins group 〔HR=0.34, 95%CI (0.23, 0.50) , P7 h/d) , non-smoking, and no family history of cardiovascular disease, Met+Aca+DPP-4i treatment reduced the incidence of comprehensive outcome events in T2DM patients compared with Met+Aca+Ins treatment (P values were 0.008, 0.031, and 0.042, respectively) .ConclusionAfter failure treatment of Met and Aca in T2DM patients, the supplementation of DPP-4i was associated with a lower risk of comprehensive outcome events, cardiovascular disease, all-cause mortality, and severe hypoglycemia compared with the Ins addition, particularly in patients with adequate sleep, no smoking, and without family history of cardiovascular disease.

Published
2022
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29. Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach

Author

Wang, Huajin, Li, Jianhui, Shen, Zhihong, and Zhou, Yuanchun

Subjects
Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture, Statistics - Applications
Abstract

Compared to basic fork-join queues, a job in (n, k) fork-join queues only needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. In this paper, we developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k., Comment: 10 pages

Published
2017
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30. A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Author

Dong, Yuxiao, Ma, Hao, Shen, Zhihong, and Wang, Kuansan

Subjects
Computer Science - Digital Libraries, Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies., Comment: KDD'17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Published
2017

35. SimbaQL: A Query Language for Multi-source Heterogeneous Data

Author

Li, Yuepeng, Shen, Zhihong, Li, Jianhui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Jianhui, editor, Meng, Xiaofeng, editor, Zhang, Ying, editor, Cui, Wenjuan, editor, and Du, Zhihui, editor

Published
2019
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45. Evidence for the decays $B^{0}\rightarrow\bar{D}^{(*)0}\phi$ and updated measurements of the branching fractions of the $B^{0}_{s}\rightarrow\bar{D}^{(*)0}\phi$ decays

Author

Aaij, Roel, Abdelmotteleb, Ahmed Sameh Wagih, Abellan Beteta, Carlos, Abudinén, Fernando Jesus, Ackernley, Thomas, Adeva, Bernardo, Adinolfi, Marco, Adlarson, Patrik Harri, Afsharnia, Hossein, Agapopoulou, Christina, Aidala, Christine Angela, Ajaltouni, Ziad, Akar, Simon, Akiba, Kazuyoshi, Albicocco, Pietro, Albrecht, Johannes, Alessio, Federico, Alexander, Michael, Alfonso Albero, Alejandro, Aliouche, Zakariya, Alvarez Cartelle, Paula, Amalric, Renaud, Amato, Sandra, Amey, Jake Lewis, Amhis, Yasmine, An, Liupan, Anderlini, Lucio, Andersson, Martin, Andreianov, Aleksei, Andreotti, Mirco, Andreou, Dimitra, Ao, Dong, Archilli, Flavio, Artamonov, Alexander, Artuso, Marina, Aslanides, Elie, Atzeni, Michele, Audurier, Benjamin, Bachiller Perea, Irene, Bachmann, Sebastian, Bachmayer, Marie, Back, John, Bailly-Reyre, Aurelien, Baladron Rodriguez, Pablo, Balagura, Vladislav, Baldini, Wander, Baptista de Souza Leite, Juan, Barbetti, Matteo, Barbosa, Ivanildo Rui, Barlow, Roger, Barsuk, Sergey, Barter, William, Bartolini, Matteo, Baryshnikov, Fedor, Basels, Jan-Marc, Bassi, Giovanni, Batsukh, Baasansuren, Battig, Alexander, Bay, Aurelio, Beck, Anja, Becker, Maik, Bedeschi, Franco, Bediaga, Ignacio, Beiter, Andrew, Belin, Samuel, Bellee, Violaine, Belous, Konstantin, Belov, Ilia, Belyaev, Ivan, Benane, Gaya, Bencivenni, Giovanni, Ben-Haim, Eli, Berezhnoy, Alexander, Bernet, Roland, Bernet Andres, Sergi, Berninghoff, Daniel, Bernstein, Harris Conan, Bertella, Claudia, Bertolin, Alessandro, Betancourt, Christopher, Betti, Federico, Bezshyiko, Iaroslava, Bhom, Jihyun, Bian, Lingzhu, Bieker, Martin Stefan, Biesuz, Nicolo Vladi, Billoir, Pierre, Biolchini, Alice, Birch, Matthew, Bishop, Fionn Caitlin Ros, Bitadze, Alexander, Bizzeti, Andrea, Blago, Michele Piero, Blake, Thomas, Blanc, Frederic, Blank, Jonah Evan, Blusk, Steven, Bobulska, Dana, Bocharnikov, Vladimir, Boelhauve, Julian Alexander, Boente Garcia, Oscar, Boettcher, Thomas, Boldyrev, Alexey, Bolognani, Carolina, Bolzonella, Riccardo, Bondar, Nikolay, Borgato, Federica, Borghi, Silvia, Borsato, Martino, Borsuk, Jozef Tomasz, Bouchiba, Sonia Amina, Bowcock, Themistocles, Boyer, Alexandre, Bozzi, Concezio, Bradley, Matthew John, Braun, Svende, Brea Rodriguez, Alexandre, Breer, Nils, Brodzicka, Jolanta, Brossa Gonzalo, Arnau, Brown, James David, Brundu, Davide, Buonaura, Annarita, Buonincontri, Laura, Burke, Aodhan Tomas, Burr, Christopher, Bursche, Albert, Butkevich, Anatoly, Butter, Jordy Sebastiaan, Buytaert, Jan, Byczynski, Wiktor, Cadeddu, Sandro, Cai, Hao, Calabrese, Roberto, Calefice, Lukas, Cali, Stefano, Calvi, Marta, Calvo Gomez, Miriam, Campana, Pierluigi, Campora Perez, Daniel Hugo, Campoverde Quezada, Angel Fernando, Capelli, Simone, Capriotti, Lorenzo, Carbone, Angelo, Cardinale, Roberta, Cardini, Alessandro, Carniti, Paolo, Carus, Leon David, Casais Vidal, Adrian, Caspary, Rowina, Casse, Gianluigi, Cattaneo, Marco, Cavallero, Giovanni, Cavallini, Viola, Celani, Sara, Cerasoli, Jacopo, Cervenkov, Daniel, Chadwick, Abbie Jane, Chahrour, Ibrahim, Chapman, Matthew George, Charles, Matthew, Charpentier, Philippe, Chavez Barajas, Carlos Alberto, Chefdeville, Maximilien, Chen, Chen, Chen, Shanzhen, Chernov, Aleksei, Chernyshenko, Serhii, Chobanova, Veronika, Cholak, Serhii, Chrzaszcz, Marcin, Chubykin, Aleksei, Chulikov, Vladimir, Ciambrone, Paolo, Cicala, Maria Flavia, Cid Vidal, Xabier, Ciezarek, Gregory, Cifra, Pierfrancesco, Ciullo, Giuseppe, Clarke, Peter, Clemencic, Marco, Cliff, Harry, Closier, Joel, Cobbledick, John Leslie, Coco, Victor, Cogan, Julien, Cogneras, Eric, Cojocariu, Lucian, Collins, Paula, Colombo, Tommaso, Congedo, Liliana, Contu, Andrea, Cooke, Naomi, Corredoira, Imanol, Corti, Gloria, Couturier, Benjamin, Craik, Daniel Charles, Cruz Torres, Melissa Maria, Currie, Robert, da Silva, Cesar Luiz, Dadabaev, Shakhzod, Dai, Lingyun, Dai, Xinchen, Dall'Occo, Elena, Dalseno, Jeremy, d'Ambrosio, Carmelo, Daniel, Jessy, Danilina, Anna, d'Argent, Philippe, Davies, Jonathan Edward, Davis, Adam, de Aguiar Francisco, Oscar, de Boer, Jan, de Bruyn, Kristof, de Capua, Stefano, de Cian, Michel, de Freitas Carneiro da Graca, Ulisses, de Lucia, Erika, de Miranda, Jussara, de Paula, Leandro, de Serio, Marilisa, de Simone, Dario, de Simone, Patrizia, de Vellis, Fabio, de Vries, Jacco, Dean, Cameron Thomas, Debernardis, Francesco, Decamp, Daniel, Dedu, Vlad-George, del Buono, Luigi, Delaney, Blaise, Dembinski, Hans Peter, Denysenko, Vadym, Deschamps, Olivier, Dettori, Francesco, Dey, Biplab, Di Nezza, Pasquale, Diachkov, Igor, Didenko, Sergey, Dieste Maronas, Lorena, Ding, Shuchong, Dobishuk, Vasyl, Dolmatov, Aleksandr, Dong, Chenzhi, Donohoe, Amanda May, Dordei, Francesca, dos Reis, Alberto, Douglas, Lauren, Downes, Anthony Gavin, Duda, Piotr Jakub, Dudek, Maciej Wojciech, Dufour, Laurent, Duk, Viacheslav, Durante, Paolo, Duras, Maciej, Durham, John Matthew, Dutta, Deepanwita, Dziurda, Agnieszka, Dzyuba, Alexey, Easo, Sajan, Egede, Ulrik, Egorychev, Artem, Egorychev, Victor, Eirea Orro, Caetano, Eisenhardt, Stephan, Ejopu, Enoch, Ek-In, Surapat, Eklund, Lars, Elashri, Mohamed, Ellbracht, Jan Hendrik, Ely, Scott, Ene, Alexandru, Epple, Eliane, Escher, Stephan, Eschle, Jonas Nathanael, Esen, Sevda, Evans, Timothy, Fabiano, Federica, Falcao, Lucas, Fan, Yanting, Fang, Bo, Fantini, Lisa, Faria, Maria, Farry, Stephen, Fazzini, Davide, Felkowski, Lukasz Jerzy, Feo, Mauricio, Fernandez Gomez, Miguel, Fernez, Alex Daniel, Ferrari, Fabio, Ferreira Lopes, Lino, Ferreira Rodrigues, Fernando, Ferreres Sole, Silvia, Ferrillo, Martina, Ferro-Luzzi, Massimiliano, Filippov, Sergey, Fini, Rosa Anna, Fiorini, Massimiliano, Firlej, Miroslaw, Fischer, Kamil Leszek, Fitzgerald, Dillon Scott, Fitzpatrick, Conor, Fiutowski, Tomasz, Fleuret, Frederic, Fontana, Marianna, Fontanelli, Flavio, Forty, Roger, Foulds-Holt, Daniel, Franco Lima, Vinicius, Franco Sevilla, Manuel, Frank, Markus, Franzoso, Edoardo, Frau, Giulia, Frei, Christoph, Friday, David Anthony, Frontini, Luca, Fu, Jinlin, Fuehring, Quentin, Fulghesu, Tommaso, Gabriel, Emmy, Galati, Giuliana, Galati, Maria Domenica, Gallas Torreira, Abraham, Galli, Domenico, Gambetta, Silvia, Gandelman, Miriam, Gandini, Paolo, Gao, Han, Gao, Rui, Gao, Yang, Gao, Yuanning, Garau, Michela, Garcia Martin, Luis Miguel, Garcia Moreno, Paula, García Pardiñas, Julián, Garcia Plana, Beatriz, Garcia Rosales, Felipe Andres, Garrido, Lluis, Gaspar, Clara, Geertsema, Robbert Erik, Gerick, David, Gerken, Louis Lenard, Gersabeck, Evelina, Gersabeck, Marco, Gershon, Timothy, Giambastiani, Luca, Gibson, Valerie, Giemza, Henryk Karol, Gilman, Alexander Leon, Giovannetti, Matteo, Gioventù, Alessandra, Gironella Gironell, Pere, Giugliano, Carmen, Giza, Maciej Artur, Gizdov, Konstantin, Gkougkousis, Evangelos Leonidas, Gligorov, Vladimir, Göbel, Carla, Golobardes, Elisabet, Golubkov, Dmitry, Golutvin, Andrey, Gomes, Alvaro, Gomez Fernandez, Sergio, Goncalves Abrantes, Fernanda, Goncerz, Mateusz, Gong, Guanghua, Gorelov, Igor Vladimirovich, Gotti, Claudio, Grabowski, Jascha Peter, Grammatico, Thomas, Granado Cardoso, Luis Alberto, Graugés, Eugeni, Graverini, Elena, Graziani, Giacomo, Grecu, Alexandru Tudor, Greeven, Lex Marinus, Grieser, Nathan Allen, Grillo, Lucia, Gromov, Sergey, Gu, Chenxi, Guarise, Marco, Guittiere, Manuel, Guliaeva, Vasilisa, Günther, Paul Andre, Guseinov, Abdul-Kerim, Gushchin, Evgeny, Guz, Yury, Gys, Thierry, Hadavizadeh, Thomas, Hadjivasiliou, Christos, Haefeli, Guido, Haen, Christophe, Haimberger, Jakob, Haines, Susan, Halewood-Leagas, Tabitha, Halvorsen, Marius Maehlum, Hamilton, Phoebe Meredith, Hammerich, Jan, Han, Qundong, Han, Xiaoxue, Hansmann-Menzemer, Stephanie, Hao, Lei, Harnew, Neville, Harrison, Thomas, Hasse, Christoph, Hatch, Mark, He, Jibo, Heijhoff, Kevin, Hemmer, Frederic, Henderson, Conor, Henderson, Riley Dylan Leslie, Hennequin, Arthur Marius, Hennessy, Karol, Henry, Louis, Herd, James Daniel, Heuel, Johannes, Hicheur, Adlène, Hill, Donal, Hilton, Martha, Hollitt, Sophie, Horswill, Joshua Ethan, Hou, Ruiwen, Hou, Yingrui, Hu, Jiangqiao, Hu, Jifeng, Hu, Wenhua, Hu, Xiaofan, Huang, Wenqian, Huang, Xiaotao, Hulsbergen, Wouter, Hunter, Ross John, Hushchyn, Mikhail, Hutchcroft, David, Ibis, Philipp, Idzik, Marek, Ilin, Dmitrii, Ilten, Philip, Inglessi, Alexander, Iniukhin, Aleksandr, Ishteev, Artur, Ivshin, Kuzma, Jacobsson, Richard, Jage, Hendrik, Jaimes Elles, Sergio Javier, Jakobsen, Sune, Jans, Eddy, Jashal, Brij Kishor, Jawahery, Abolhassan, Jevtic, Vukan, Jiang, Emily Kaiyin, Jiang, Xiaojie, Jiang, Yi, John, Malcolm, Johnson, Daniel, Jones, Christopher, Jones, Thomas Peter, Joshi, Salil, Jost, Beat, Jurik, Nathan, Juszczak, Izabela, Kandybei, Sergii, Kang, Youen, Karacson, Matthias, Karpenkov, Dmitrii, Karpov, Maksim, Kautz, Jacob William, Keizer, Floris, Keller, Dustin Michael, Kenzie, Matthew, Ketel, Tjeerd, Khanji, Basem, Kharisova, Anastasiia, Kholodenko, Sergei, Khreich, Gaelle, Kirn, Thomas, Kirsebom, Veronica Soelund, Kitouni, Ouail, Klaver, Suzanne, Kleijne, Nico, Klimaszewski, Konrad, Kmiec, Mateusz Rafal, Koliiev, Serhii, Kolk, Lars, Kondybayeva, Almagul, Konoplyannikov, Anatoly, Kopciewicz, Pawel, Kopecna, Renata, Koppenburg, Patrick, Korolev, Mikhail, Kostiuk, Igor, Kot, Oleksander, Kotriakhova, Sofia, Kozachuk, Anastasiia, Kravchenko, Polina, Kravchuk, Leonid, Kreps, Michal, Kretzschmar, Sophie Katharina, Krokovny, Pavel, Krupa, Wojciech, Krzemien, Wojciech, Kubat, Jakub, Kubis, Sebastian Antoni, Kucewicz, Wojciech, Kucharczyk, Marcin, Kudryavtsev, Vasily, Kulikova, Evgenia, Kupsc, Andrzej, Lacarrere, Daniel, Lafferty, George, Lai, Adriano, Lampis, Andrea, Lancierini, Davide, Landesa Gomez, Clara, Lane, John Jake, Lane, Richard, Langenbruch, Christoph, Langer, Jan, Lantwin, Oliver, Latham, Thomas, Lazzari, Federico, Lazzeroni, Cristina, Le Gac, Renaud, Lee, Sook Hyun, Lefèvre, Regis, Leflat, Alexander, Legotin, Sergey, Lenisa, Paolo, Leroy, Olivier, Lesiak, Tadeusz, Leverington, Blake, Li, Anfeng, Li, Hengne, Li, Kechen, Li, Peilian, Li, Pei-Rong, Li, Shiyang, Li, Tiange, Li, Tianqi, Li, Yiming, Li, Zhuoming, Lian, Zhengchen, Liang, Xixin, Lin, Chuangxin, Lin, Tai-Hua, Lindner, Rolf, Lisovskyi, Vitalii, Litvinov, Roman, Liu, Guoming, Liu, Huanhuan, Liu, Kai, Liu, Qian, Liu, Shuaiyi, Lobo Salvia, Aniol, Loi, Angelo, Lollini, Riccardo, Lomba Castro, Julian, Longstaff, Iain, Lopes, Jose, Lopez Huertas, Albert, López Soliño, Saúl, Lovell, George Holger, Lu, Yu, Lucarelli, Chiara, Lucchesi, Donatella, Luchuk, Stanislav, Lucio Martinez, Miriam, Lukashenko, Valeriia, Luo, Yiheng, Lupato, Anna, Luppi, Eleonora, Lynch, Kate, Lyu, Xiao-Rui, Ma, Ruiting, Maccolini, Serena, Machefert, Frederic, Maciuc, Florin, Mackay, Innes, Macko, Vladimir, Madhan Mohan, Lakshan Ram, Maevskiy, Artem, Maisuzenko, Dmitrii, Majewski, Maciej Witold, Malczewski, Jakub Jacek, Malde, Sneha, Malecki, Bartosz, Malinin, Alexander, Maltsev, Timofei, Manca, Giulia, Mancinelli, Giampiero, Mancuso, Chiara, Manera Escalero, Rafel, Manuzzi, Daniele, Manzari, Claudio Andrea, Marangotto, Daniele, Marchand, Jean François, Marconi, Umberto, Mariani, Saverio, Marin Benito, Carla, Marks, Jörg, Marshall, Alexander Mclean, Marshall, Phillip John, Martelli, Gabriele, Martellotti, Giuseppe, Martinazzoli, Loris, Martinelli, Maurizio, Martinez Santos, Diego, Martinez Vidal, Fernando, Massafferri, André, Materok, Marcel, Matev, Rosen, Mathad, Abhijit, Matiunin, Viacheslav, Matteuzzi, Clara, Mattioli, Kara Renee, Mauri, Andrea, Maurice, Emilie, Mauricio, Joan, Mazurek, Michal, Mccann, Michael, Mcconnell, Lucas, Mcgrath, Tamaki Holly, Mchugh, Niall Thomas, Mcnab, Andrew, Mcnulty, Ronan, Meadows, Brian, Meier, Gerwin, Melnychuk, Dmytro, Meloni, Simone, Merk, Marcel, Merli, Andrea, Meyer Garcia, Lucas, Miao, Dexing, Miao, Han, Mikhasenko, Mikhail, Milanes, Diego Alejandro, Milovanovic, Marko, Minard, Marie-Noelle, Minotti, Alessandro, Minucci, Elisa, Miralles, Tristan, Mitchell, Sara Elizabeth, Mitreska, Biljana, Mitzel, Dominik Stefan, Modak, Atanu, Mödden, Antje, Mohammed, Rizwaan Adeeb, Moise, Razvan-Daniel, Mokhnenko, Sergei, Mombächer, Titus, Monk, Matthew David, Monroy, Igancio Alberto, Monteil, Stephane, Morello, Gianfranco, Morello, Michael Joseph, Morgenthaler, Maurice Pierre, Moron, Jakub, Morris, Adam Benjamin, Morris, Andrew George, Mountain, Raymond, Mu, Hongjie, Muhammad, Emir, Muheim, Franz, Mulder, Mick, Müller, Katharina, Murray, Donal, Murta, Rebecca, Muzzetto, Piera, Naik, Paras, Nakada, Tatsuya, Nandakumar, Raja, Nanut, Tara, Nasteva, Irina, Needham, Matthew, Neri, Nicola, Neubert, Sebastian, Neufeld, Niko, Neustroev, Petr, Newcombe, Ryan, Nicolini, Janina, Nicotra, Davide, Niel, Elisabeth Maria, Nieswand, Simon, Nikitin, Nikolay, Nolte, Niklas Stefan, Normand, Camille, Novoa Fernandez, Julio, Nowak, Gabriel Matthew, Nunez, Cynthia, Oblakowska-Mucha, Agnieszka, Obraztsov, Vladimir, Oeser, Thomas, Okamura, Shinichi, Oldeman, Rudolf, Oliva, Federica, Olocco, Micol, Onderwater, Gerco, O'Neil, Ryunosuke Hugo, Otalora Goicochea, Juan Martin, Ovsiannikova, Tatiana, Owen, Patrick, Oyanguren, Maria Aranzazu, Ozcelik, Ozlem, Padeken, Klaas Ole, Pagare, Bhagyashree, Pais, Preema Rennee, Pajero, Tommaso, Palano, Antimo, Palutan, Matteo, Panshin, Gennady, Paolucci, Lorenzo, Papanestis, Antonios, Pappagallo, Marco, Pappalardo, Luciano, Pappenheimer, Cheryl, Parker, William, Parkes, Christopher, Passalacqua, Barbara, Passaleva, Giovanni, Pastore, Alessandra, Patel, Mitesh, Patrignani, Claudia, Pawley, Christopher James, Pellegrino, Antonio, Pepe Altarelli, Monica, Perazzini, Stefano, Pereima, Dmitrii, Pereiro Castro, Asier, Perret, Pascal, Petridis, Konstantinos, Petrolini, Alessandro, Petrucci, Stefano, Petruzzo, Marco, Pham, Hang, Philippov, Anton, Piandani, Roberto, Pica, Lorenzo, Piccini, Mauro, Pietrzyk, Boleslaw, Pietrzyk, Guillaume, Pinci, Davide, Pisani, Flavio, Pizzichemi, Marco, Placinta, Vlad-Mihai, Plews, Jonathan, Plo Casasus, Maximo, Polci, Francesco, Poli Lener, Marco, Poluektov, Anton, Polukhina, Natalia, Polyakov, Ivan, Polycarpo, Erica, Ponce, Sebastien, Popov, Dmitry, Poslavskii, Stanislav, Prasanth, Kodassery, Promberger, Laura, Prouve, Claire, Pugatch, Valery, Puill, Veronique, Punzi, Giovanni, Qi, Hongrong, Qian, Wenbin, Qin, Ning, Qu, Sanqiang, Quagliani, Renato, Raab, Naomi Veronika, Rachwal, Bartlomiej, Rademacker, Jonas, Rajagopalan, Rohan, Rama, Matteo, Ramos Pernas, Miguel, Rangel, Murilo, Ratnikov, Fedor, Raven, Gerhard, Rebollo de Miguel, Miguel, Redi, Federico, Reich, Jake, Reiss, Florian, Ren, Zan, Resmi, P.K, Ribatti, Roberto, Ricci, Alessandro Maria, Ricciardi, Stefania, Richardson, Kate Abigail, Richardson-Slipper, Mary, Rinnert, Kurt, Robbe, Patrick, Robertson, Gary, Rodrigues, Eduardo, Rodriguez Fernandez, Emilio Xose, Rodriguez Lopez, Jairo Alexis, Rodriguez Rodriguez, Efren, Rolf, David Leonhard, Rollings, Alexandra Paige, Roloff, Philipp, Romanovskiy, Vladimir, Romero Lamas, Marcos, Romero Vidal, Antonio, Rotondo, Marcello, Rudolph, Matthew Scott, Ruf, Thomas, Ruiz Fernandez, Ramon Angel, Ruiz Vidal, Joan, Ryzhikov, Artem, Ryzka, Jakub, Saborido Silva, Juan Jose, Sagidova, Naylya, Sahoo, Niladribihari, Saitta, Biagio, Salomoni, Matteo, Sanchez Gras, Cristina, Sanderswood, Izaac, Santacesaria, Roberta, Santamarina Rios, Cibran, Santimaria, Marco, Santoro, Leonardo, Santovetti, Emanuele, Saranin, Danila, Sarpis, Gediminas, Sarpis, Mindaugas, Sarti, Alessio, Satriano, Celestina, Satta, Alessia, Saur, Miroslav, Savrina, Darya, Sazak, Halime, Scantlebury Smead, Luke George, Scarabotto, Alessandro, Schael, Stefan, Scherl, Sigrid, Schertz, Amy Marie, Schiller, Manuel, Schindler, Heinrich, Schmelling, Michael, Schmidt, Burkhard, Schmitt, Sebastian, Schneider, Olivier, Schopper, Andreas, Schubiger, Maxime, Schulte, Nicole, Schulte, Sebastian, Schune, Marie Helene, Schwemmer, Rainer, Schwering, Georg, Sciascia, Barbara, Sciuccati, Augusto, Sellam, Sara, Semennikov, Alexander, Senghi Soares, Mara, Sergi, Antonino, Serra, Nicola, Sestini, Lorenzo, Seuthe, Alex, Shang, Yiduo, Shangase, Desmond Mzamo, Shapkin, Mikhail, Shchemerov, Ivan, Shchutska, Lesya, Shears, Tara, Shekhtman, Lev, Shen, Zhihong, Sheng, Shuqi, Shevchenko, Vladimir, Shi, Boan, Shields, Edward Brendan, Shimizu, Yuya, Shmanin, Evgenii, Shorkin, Roman, Shupperd, Joseph David, Siddi, Benedetto Gianluca, Silva Coutinho, Rafael, Simi, Gabriele, Simone, Saverio, Singla, Minni, Skidmore, Nicola, Skuza, Raphael, Skwarnicki, Tomasz, Slater, Mark, Smallwood, Jennifer Clare, Smeaton, John Gordon, Smith, Eluned, Smith, Krista Lizbeth, Smith, Mark, Snoch, Aleksandra, Soares Lavra, Lais, Sokoloff, Michael, Soler, Paul, Solomin, Anatoly, Solovev, Aleksandr, Solovyev, Ivan, Song, Rongrong, Souza de Almeida, Felipe Luan, Souza de Paula, Bruno, Spadaro Norella, Elisabetta, Spedicato, Eugenia, Speer, Jannis Guido, Spiridenkov, Eduard, Spradlin, Patrick, Sriskaran, Viros, Stagni, Federico, Stahl, Marian, Stahl, Sascha, Stanislaus, Seophine, Stein, Eloise Noelle, Steinkamp, Olaf, Stenyakin, Oleg, Stevens, Holger, Strekalina, Daria, Su, Yangjie, Suljik, Fidan, Sun, Jiayin, Sun, Liang, Sun, Yipeng, Swallow, Paul Nathaniel, Swientek, Krzysztof, Szabelski, Adam, Szumlak, Tomasz, Szymanski, Maciej Pawel, Tan, Yinghua, Taneja, Shantam, Tat, Martin Duy, Terentev, Aleksandr, Teubert, Frederic, Thomas, Eric, Thompson, Daniel James David, Tilquin, Hanae, Tisserand, Vincent, t'Jampens, Stephane, Tobin, Mark, Tomassetti, Luca, Tonani, Giorgia, Tong, Xingyu, Torres Machado, Diego, Toscano, Luca, Tou, da Yu, Trippl, Carina, Tuci, Giulia, Tuning, Niels, Ukleja, Artur, Unverzagt, Daniel Joachim, Usachov, Andrii, Ustyuzhanin, Andrey, Uwer, Ulrich, Vagnoni, Vincenzo, Valassi, Andrea, Valenti, Giovanni, Valls Canudas, Nuria, van Dijk, Maarten, van Hecke, Hubert, van Herwijnen, Eric, van Hulse, Charlotte Barbara, van Veghel, Maarten, Vazquez Gomez, Ricardo, Vazquez Regueiro, Pablo, Vázquez Sierra, Carlos, Vecchi, Stefania, Velthuis, Jaap, Veltri, Michele, Venkateswaran, Aravindhan, Vesterinen, Mika, Vieira, Daniel, Vieites Diaz, Maria, Vilasis-Cardona, Xavier, Vilella Figueras, Eva, Villa, Andrea, Vincent, Pascal, Volle, Felicia Carolin, Vom Bruch, Dorothea, Vorobyev, Vitaly, Voropaev, Nikolai, Vos, Kimberley, Vrahas, Constantinos, Walsh, John, Walton, Eliot Jane, Wan, Guanyue, Wang, Chishuai, Wang, Ganrong, Wang, Jialu, Wang, Jianchun, Wang, Jianqiao, Wang, Jike, Wang, Mengzhen, Wang, Rui, Wang, Xiaolin, Wang, Yilong, Wang, Zhenzi, Wang, Zirui, Wang, Ziyi, Ward, Jake Alexander, Watson, Nigel, Websdale, David, Wei, Yajing, Westhenry, Benedict Donald C, White, Dylan Jaide, Whitehead, Mark, Wiederhold, Aidan Richard, Wiedner, Dirk, Wilkinson, Guy, Wilkinson, Michael K, Williams, Ifan, Williams, Mike, Williams, Mark Richard James, Williams, Richard Morgan, Wilson, Fergus, Wislicki, Wojciech, Witek, Mariusz, Witola, Lukas, Wong, Cheuk Ping, Wormser, Guy, Wotton, Stephen, Wu, Hangyi, Wu, Jie, Wu, Yanxi, Wyllie, Kenneth, Xiang, Zhiyu, Xie, Yuehong, Xu, Ao, Xu, Jingyi, Xu, Li, Xu, Liangjun, Xu, Menglin, Xu, Qingnian, Xu, Zehua, Xu, Zhihao, Xu, Zijun, Yang, Di, Yang, Shuangli, Yang, Xueting, Yang, Youhua, Yang, Zhenwei, Yang, Zishuo, Yeroshenko, Vsevolod, Yeung, Ho Yin Dereck, Yin, Hang, Yu, Jiesheng, Yuan, Xuhao, Zaffaroni, Ettore, Zavertyaev, Mikhail, Zdybal, Milosz, Zeng, Ming, Zhang, Chenjia, Zhang, Dongliang, Zhang, Jianyu, Zhang, Liming, Zhang, Shulei, Zhang, Shunan, Zhang, Yanxi, Zhang, Yu, Zhao, Ya, Zharkova, Alina, Zhelezov, Alexey, Zheng, Yangheng, Zhou, Tianwen, Zhou, Xiaokang, Zhou, Yixiong, Zhovkovska, Valeriia, Zhu, Xianglei, Zhu, Xiaoyu, Zhu, Zhanwen, Zhukov, Valery, Zhuo, Jiahui, Zou, Quan, Zucchelli, Stefano, Zuliani, Davide, Zunica, Gianluca, Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and LHCb

Subjects
CKM matrix, p p, scattering, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], mixing angle, TeV, branching ratio, statistical, LHC-B, Particle Physics - Experiment, High Energy Physics - Experiment
Abstract

Evidence for the decays $B^{0}\rightarrow\bar{D}^{0}\phi$ and $B^{0} \rightarrow \bar{D}^{*0}\phi$ is reported with a significance of 3.6$\,\sigma$ and 4.3$\,\sigma$, respectively. The analysis employs $pp$ collision data at centre-of-mass energies $\sqrt{s}=7$, 8 and 13 TeV collected by the LHCb detector and corresponding to an integrated luminosity of 9 $\rm{fb}^{-1}$. The branching fractions are measured to be $\mathcal{B}(B^{0}\rightarrow\bar{D}^{0}\phi) = (7.7\pm2.1\pm0.7\pm0.7)\times10^{-7}$, $\mathcal{B}(B^{0} \rightarrow \bar{D}^{*0}\phi)=(2.2\pm0.5\pm0.2\pm0.2)\times10^{-6}$. In these results, the first uncertainties are statistical, the second systematic, and the third is related to the branching fraction of the $B^{0}\rightarrow\bar{D}^{0}K^{+}K^{-}$ decay, used for normalisation. By combining the branching fractions of the decays $B^{0}\rightarrow\bar{D}^{(*)0}\phi$ and $B^{0}\rightarrow\bar{D}^{(*)0}\omega$, the $\omega$-$\phi$ mixing angle $\delta$ is constrained to be $\tan^2\delta = (3.6\pm0.7\pm0.4)\times10^{-3}$, where the first uncertainty is statistical and the second systematic. An updated measurement of the branching fractions of the $B^{0}_{s} \rightarrow \bar{D}^{(*)0}\phi$ decays, which can be used to determine the CKM angle $\gamma$, leads to $\mathcal{B}(B^{0}_{s}\rightarrow\bar{D}^{0}\phi)=(2.30\pm0.10 \pm 0.11\pm0.20)\times10^{-5}$, $\mathcal{B}(B^{0}_{s}\rightarrow\bar{D}^{*0}\phi) =(3.17\pm0.16 \pm 0.17 \pm 0.27)\times10^{-5}$., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-003.html (LHCb public pages)

Published
2023

46. Precision measurement of $\it{CP} $ violation in the penguin-mediated decay $B_s^{0}\rightarrow\phi\phi$

Author

Aaij, Roel, Abdelmotteleb, Ahmed Sameh Wagih, Abellan Beteta, Carlos, Abudinén, Fernando Jesus, Ackernley, Thomas, Adeva, Bernardo, Adinolfi, Marco, Adlarson, Patrik Harri, Afsharnia, Hossein, Agapopoulou, Christina, Aidala, Christine Angela, Ajaltouni, Ziad, Akar, Simon, Akiba, Kazuyoshi, Albicocco, Pietro, Albrecht, Johannes, Alessio, Federico, Alexander, Michael, Alfonso Albero, Alejandro, Aliouche, Zakariya, Alvarez Cartelle, Paula, Amalric, Renaud, Amato, Sandra, Amey, Jake Lewis, Amhis, Yasmine, An, Liupan, Anderlini, Lucio, Andersson, Martin, Andreianov, Aleksei, Andreotti, Mirco, Andreou, Dimitra, Ao, Dong, Archilli, Flavio, Artamonov, Alexander, Artuso, Marina, Aslanides, Elie, Atzeni, Michele, Audurier, Benjamin, Bachiller Perea, Irene, Bachmann, Sebastian, Bachmayer, Marie, Back, John, Bailly-Reyre, Aurelien, Baladron Rodriguez, Pablo, Balagura, Vladislav, Baldini, Wander, Baptista de Souza Leite, Juan, Barbetti, Matteo, Barlow, Roger, Barsuk, Sergey, Barter, William, Bartolini, Matteo, Baryshnikov, Fedor, Basels, Jan-Marc, Bassi, Giovanni, Batozskaya, Varvara, Batsukh, Baasansuren, Battig, Alexander, Bay, Aurelio, Beck, Anja, Becker, Maik, Bedeschi, Franco, Bediaga, Ignacio, Beiter, Andrew, Belin, Samuel, Bellee, Violaine, Belous, Konstantin, Belov, Ilia, Belyaev, Ivan, Benane, Gaya, Bencivenni, Giovanni, Ben-Haim, Eli, Berezhnoy, Alexander, Bernet, Roland, Bernet Andres, Sergi, Berninghoff, Daniel, Bernstein, Harris Conan, Bertella, Claudia, Bertolin, Alessandro, Betancourt, Christopher, Betti, Federico, Bezshyiko, Iaroslava, Bhom, Jihyun, Bian, Lingzhu, Bieker, Martin Stefan, Biesuz, Nicolo Vladi, Billoir, Pierre, Biolchini, Alice, Birch, Matthew, Bishop, Fionn Caitlin Ros, Bitadze, Alexander, Bizzeti, Andrea, Blago, Michele Piero, Blake, Thomas, Blanc, Frederic, Blank, Jonah Evan, Blusk, Steven, Bobulska, Dana, Bocharnikov, Vladimir, Boelhauve, Julian Alexander, Boente Garcia, Oscar, Boettcher, Thomas, Boldyrev, Alexey, Bolognani, Carolina, Bolzonella, Riccardo, Bondar, Nikolay, Borgato, Federica, Borghi, Silvia, Borsato, Martino, Borsuk, Jozef Tomasz, Bouchiba, Sonia Amina, Bowcock, Themistocles, Boyer, Alexandre, Bozzi, Concezio, Bradley, Matthew John, Braun, Svende, Brea Rodriguez, Alexandre, Breer, Nils, Brodzicka, Jolanta, Brossa Gonzalo, Arnau, Brown, James David, Brundu, Davide, Buonaura, Annarita, Buonincontri, Laura, Burke, Aodhan Tomas, Burr, Christopher, Bursche, Albert, Butkevich, Anatoly, Butter, Jordy Sebastiaan, Buytaert, Jan, Byczynski, Wiktor, Cadeddu, Sandro, Cai, Hao, Calabrese, Roberto, Calefice, Lukas, Cali, Stefano, Calvi, Marta, Calvo Gomez, Miriam, Campana, Pierluigi, Campora Perez, Daniel Hugo, Campoverde Quezada, Angel Fernando, Capelli, Simone, Capriotti, Lorenzo, Carbone, Angelo, Cardinale, Roberta, Cardini, Alessandro, Carniti, Paolo, Carus, Leon David, Casais Vidal, Adrian, Caspary, Rowina, Casse, Gianluigi, Cattaneo, Marco, Cavallero, Giovanni, Cavallini, Viola, Celani, Sara, Cerasoli, Jacopo, Cervenkov, Daniel, Chadwick, Abbie Jane, Chahrour, Ibrahim, Chapman, Matthew George, Charles, Matthew, Charpentier, Philippe, Chavez Barajas, Carlos Alberto, Chefdeville, Maximilien, Chen, Chen, Chen, Shanzhen, Chernov, Aleksei, Chernyshenko, Serhii, Chobanova, Veronika, Cholak, Serhii, Chrzaszcz, Marcin, Chubykin, Aleksei, Chulikov, Vladimir, Ciambrone, Paolo, Cicala, Maria Flavia, Cid Vidal, Xabier, Ciezarek, Gregory, Cifra, Pierfrancesco, Clarke, Peter, Clemencic, Marco, Cliff, Harry, Closier, Joel, Cobbledick, John Leslie, Coco, Victor, Cogan, Julien, Cogneras, Eric, Cojocariu, Lucian, Collins, Paula, Colombo, Tommaso, Congedo, Liliana, Contu, Andrea, Cooke, Naomi, Corredoira, Imanol, Corti, Gloria, Couturier, Benjamin, Craik, Daniel Charles, Cruz Torres, Melissa Maria, Currie, Robert, da Silva, Cesar Luiz, Dadabaev, Shakhzod, Dai, Lingyun, Dai, Xinchen, Dall'Occo, Elena, Dalseno, Jeremy, d'Ambrosio, Carmelo, Daniel, Jessy, Danilina, Anna, d'Argent, Philippe, Davies, Jonathan Edward, Davis, Adam, de Aguiar Francisco, Oscar, de Boer, Jan, de Bruyn, Kristof, de Capua, Stefano, de Cian, Michel, de Freitas Carneiro da Graca, Ulisses, de Lucia, Erika, de Miranda, Jussara, de Paula, Leandro, de Serio, Marilisa, de Simone, Dario, de Simone, Patrizia, de Vellis, Fabio, de Vries, Jacco, Dean, Cameron Thomas, Debernardis, Francesco, Decamp, Daniel, Dedu, Vlad-George, del Buono, Luigi, Delaney, Blaise, Dembinski, Hans Peter, Denysenko, Vadym, Deschamps, Olivier, Dettori, Francesco, Dey, Biplab, Di Nezza, Pasquale, Diachkov, Igor, Didenko, Sergey, Dieste Maronas, Lorena, Ding, Shuchong, Dobishuk, Vasyl, Dolmatov, Aleksandr, Dong, Chenzhi, Donohoe, Amanda May, Dordei, Francesca, dos Reis, Alberto, Douglas, Lauren, Downes, Anthony Gavin, Duda, Piotr Jakub, Dudek, Maciej Wojciech, Dufour, Laurent, Duk, Viacheslav, Durante, Paolo, Duras, Maciej, Durham, John Matthew, Dutta, Deepanwita, Dziurda, Agnieszka, Dzyuba, Alexey, Easo, Sajan, Egede, Ulrik, Egorychev, Artem, Egorychev, Victor, Eirea Orro, Caetano, Eisenhardt, Stephan, Ejopu, Enoch, Ek-In, Surapat, Eklund, Lars, Elashri, Mohamed, Ellbracht, Jan Hendrik, Ely, Scott, Ene, Alexandru, Epple, Eliane, Escher, Stephan, Eschle, Jonas Nathanael, Esen, Sevda, Evans, Timothy, Fabiano, Federica, Falcao, Lucas, Fan, Yanting, Fang, Bo, Fantini, Lisa, Faria, Maria, Farry, Stephen, Fazzini, Davide, Felkowski, Lukasz Jerzy, Feo, Mauricio, Fernandez Gomez, Miguel, Fernez, Alex Daniel, Ferrari, Fabio, Ferreira Lopes, Lino, Ferreira Rodrigues, Fernando, Ferreres Sole, Silvia, Ferrillo, Martina, Ferro-Luzzi, Massimiliano, Filippov, Sergey, Fini, Rosa Anna, Fiorini, Massimiliano, Firlej, Miroslaw, Fischer, Kamil Leszek, Fitzgerald, Dillon Scott, Fitzpatrick, Conor, Fiutowski, Tomasz, Fleuret, Frederic, Fontana, Marianna, Fontanelli, Flavio, Forty, Roger, Foulds-Holt, Daniel, Franco Lima, Vinicius, Franco Sevilla, Manuel, Frank, Markus, Franzoso, Edoardo, Frau, Giulia, Frei, Christoph, Friday, David Anthony, Frontini, Luca, Fu, Jinlin, Fuehring, Quentin, Fulghesu, Tommaso, Gabriel, Emmy, Galati, Giuliana, Galati, Maria Domenica, Gallas Torreira, Abraham, Galli, Domenico, Gambetta, Silvia, Gandelman, Miriam, Gandini, Paolo, Gao, Han, Gao, Rui, Gao, Yang, Gao, Yuanning, Garau, Michela, Garcia Martin, Luis Miguel, Garcia Moreno, Paula, García Pardiñas, Julián, Garcia Plana, Beatriz, Garcia Rosales, Felipe Andres, Garrido, Lluis, Gaspar, Clara, Geertsema, Robbert Erik, Gerick, David, Gerken, Louis Lenard, Gersabeck, Evelina, Gersabeck, Marco, Gershon, Timothy, Giambastiani, Luca, Gibson, Valerie, Giemza, Henryk Karol, Gilman, Alexander Leon, Giovannetti, Matteo, Gioventù, Alessandra, Gironella Gironell, Pere, Giugliano, Carmen, Giza, Maciej Artur, Gizdov, Konstantin, Gkougkousis, Evangelos Leonidas, Gligorov, Vladimir, Göbel, Carla, Golobardes, Elisabet, Golubkov, Dmitry, Golutvin, Andrey, Gomes, Alvaro, Gomez Fernandez, Sergio, Goncalves Abrantes, Fernanda, Goncerz, Mateusz, Gong, Guanghua, Gorelov, Igor Vladimirovich, Gotti, Claudio, Grabowski, Jascha Peter, Grammatico, Thomas, Granado Cardoso, Luis Alberto, Graugés, Eugeni, Graverini, Elena, Graziani, Giacomo, Grecu, Alexandru Tudor, Greeven, Lex Marinus, Grieser, Nathan Allen, Grillo, Lucia, Gromov, Sergey, Gu, Chenxi, Guarise, Marco, Guittiere, Manuel, Guliaeva, Vasilisa, Günther, Paul Andre, Guseinov, Abdul-Kerim, Gushchin, Evgeny, Guz, Yury, Gys, Thierry, Hadavizadeh, Thomas, Hadjivasiliou, Christos, Haefeli, Guido, Haen, Christophe, Haimberger, Jakob, Haines, Susan, Halewood-Leagas, Tabitha, Halvorsen, Marius Maehlum, Hamilton, Phoebe Meredith, Hammerich, Jan, Han, Qundong, Han, Xiaoxue, Hansmann-Menzemer, Stephanie, Harnew, Neville, Harrison, Thomas, Hasse, Christoph, Hatch, Mark, He, Jibo, Heijhoff, Kevin, Hemmer, Frederic, Henderson, Conor, Henderson, Riley Dylan Leslie, Hennequin, Arthur Marius, Hennessy, Karol, Henry, Louis, Herd, James Daniel, Heuel, Johannes, Hicheur, Adlène, Hill, Donal, Hilton, Martha, Hollitt, Sophie, Horswill, Joshua Ethan, Hou, Ruiwen, Hou, Yingrui, Hu, Jiangqiao, Hu, Jifeng, Hu, Wenhua, Hu, Xiaofan, Huang, Wenqian, Huang, Xiaotao, Hulsbergen, Wouter, Hunter, Ross John, Hushchyn, Mikhail, Hutchcroft, David, Ibis, Philipp, Idzik, Marek, Ilin, Dmitrii, Ilten, Philip, Inglessi, Alexander, Iniukhin, Aleksandr, Ishteev, Artur, Ivshin, Kuzma, Jacobsson, Richard, Jage, Hendrik, Jaimes Elles, Sergio Javier, Jakobsen, Sune, Jans, Eddy, Jashal, Brij Kishor, Jawahery, Abolhassan, Jevtic, Vukan, Jiang, Emily Kaiyin, Jiang, Xiaojie, Jiang, Yi, John, Malcolm, Johnson, Daniel, Jones, Christopher, Jones, Thomas Peter, Joshi, Salil, Jost, Beat, Jurik, Nathan, Juszczak, Izabela, Kandybei, Sergii, Kang, Youen, Karacson, Matthias, Karpenkov, Dmitrii, Karpov, Maksim, Kautz, Jacob William, Keizer, Floris, Keller, Dustin Michael, Kenzie, Matthew, Ketel, Tjeerd, Khanji, Basem, Kharisova, Anastasiia, Kholodenko, Sergei, Khreich, Gaelle, Kirn, Thomas, Kirsebom, Veronica Soelund, Kitouni, Ouail, Klaver, Suzanne, Kleijne, Nico, Klimaszewski, Konrad, Kmiec, Mateusz Rafal, Koliiev, Serhii, Kolk, Lars, Kondybayeva, Almagul, Konoplyannikov, Anatoly, Kopciewicz, Pawel, Kopecna, Renata, Koppenburg, Patrick, Korolev, Mikhail, Kostiuk, Igor, Kot, Oleksander, Kotriakhova, Sofia, Kozachuk, Anastasiia, Kravchenko, Polina, Kravchuk, Leonid, Kreps, Michal, Kretzschmar, Sophie Katharina, Krokovny, Pavel, Krupa, Wojciech, Krzemien, Wojciech, Kubat, Jakub, Kubis, Sebastian Antoni, Kucewicz, Wojciech, Kucharczyk, Marcin, Kudryavtsev, Vasily, Kulikova, Evgenia, Kupsc, Andrzej, Lacarrere, Daniel, Lafferty, George, Lai, Adriano, Lampis, Andrea, Lancierini, Davide, Landesa Gomez, Clara, Lane, John Jake, Lane, Richard, Langenbruch, Christoph, Langer, Jan, Lantwin, Oliver, Latham, Thomas, Lazzari, Federico, Lazzeroni, Cristina, Le Gac, Renaud, Lee, Sook Hyun, Lefèvre, Regis, Leflat, Alexander, Legotin, Sergey, Leroy, Olivier, Lesiak, Tadeusz, Leverington, Blake, Li, Anfeng, Li, 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Daniele, Manzari, Claudio Andrea, Marangotto, Daniele, Maratas, Jan Mickelle, Marchand, Jean François, Marconi, Umberto, Mariani, Saverio, Marin Benito, Carla, Marks, Jörg, Marshall, Alexander Mclean, Marshall, Phillip John, Martelli, Gabriele, Martellotti, Giuseppe, Martinazzoli, Loris, Martinelli, Maurizio, Martinez Santos, Diego, Martinez Vidal, Fernando, Massafferri, André, Materok, Marcel, Matev, Rosen, Mathad, Abhijit, Matiunin, Viacheslav, Matteuzzi, Clara, Mattioli, Kara Renee, Mauri, Andrea, Maurice, Emilie, Mauricio, Joan, Mazurek, Michal, Mccann, Michael, Mcconnell, Lucas, Mcgrath, Tamaki Holly, Mchugh, Niall Thomas, Mcnab, Andrew, Mcnulty, Ronan, Meadows, Brian, Meier, Gerwin, Melnychuk, Dmytro, Meloni, Simone, Merk, Marcel, Merli, Andrea, Meyer Garcia, Lucas, Miao, Dexing, Miao, Han, Mikhasenko, Mikhail, Milanes, Diego Alejandro, Milovanovic, Marko, Minard, Marie-Noelle, Minotti, Alessandro, Minucci, Elisa, Miralles, Tristan, Mitchell, Sara Elizabeth, Mitreska, Biljana, Mitzel, Dominik Stefan, Modak, Atanu, Mödden, Antje, Mohammed, Rizwaan Adeeb, Moise, Razvan-Daniel, Mokhnenko, Sergei, Mombächer, Titus, Monk, Matthew David, Monroy, Igancio Alberto, Monteil, Stephane, Morello, Gianfranco, Morello, Michael Joseph, Morgenthaler, Maurice Pierre, Moron, Jakub, Morris, Adam Benjamin, Morris, Andrew George, Mountain, Raymond, Mu, Hongjie, Muhammad, Emir, Muheim, Franz, Mulder, Mick, Müller, Katharina, Murray, Donal, Murta, Rebecca, Muzzetto, Piera, Naik, Paras, Nakada, Tatsuya, Nandakumar, Raja, Nanut, Tara, Nasteva, Irina, Needham, Matthew, Neri, Nicola, Neubert, Sebastian, Neufeld, Niko, Neustroev, Petr, Newcombe, Ryan, Nicolini, Janina, Nicotra, Davide, Niel, Elisabeth Maria, Nieswand, Simon, Nikitin, Nikolay, Nolte, Niklas Stefan, Normand, Camille, Novoa Fernandez, Julio, Nowak, Gabriel Matthew, Nunez, Cynthia, Oblakowska-Mucha, Agnieszka, Obraztsov, Vladimir, Oeser, Thomas, Okamura, Shinichi, Oldeman, Rudolf, Oliva, Federica, Onderwater, Gerco, O'Neil, 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Michele, Venkateswaran, Aravindhan, Vesterinen, Mika, Vieira, Daniel, Vieites Diaz, Maria, Vilasis-Cardona, Xavier, Vilella Figueras, Eva, Villa, Andrea, Vincent, Pascal, Volle, Felicia Carolin, Vom Bruch, Dorothea, Vorobyev, Vitaly, Voropaev, Nikolai, Vos, Kimberley, Vrahas, Constantinos, Walsh, John, Walton, Eliot Jane, Wan, Guanyue, Wang, Chishuai, Wang, Ganrong, Wang, Jialu, Wang, Jianchun, Wang, Jianqiao, Wang, Jike, Wang, Mengzhen, Wang, Rui, Wang, Xiaolin, Wang, Yilong, Wang, Zhenzi, Wang, Zirui, Wang, Ziyi, Ward, Jake Alexander, Watson, Nigel, Websdale, David, Wei, Yajing, Westhenry, Benedict Donald C, White, Dylan Jaide, Whitehead, Mark, Wiederhold, Aidan Richard, Wiedner, Dirk, Wilkinson, Guy, Wilkinson, Michael K, Williams, Ifan, Williams, Mike, Williams, Mark Richard James, Williams, Richard Morgan, Wilson, Fergus, Wislicki, Wojciech, Witek, Mariusz, Witola, Lukas, Wong, Cheuk Ping, Wormser, Guy, Wotton, Stephen, Wu, Hangyi, Wu, Jie, Wu, Yanxi, Wyllie, Kenneth, Xiang, Zhiyu, Xie, Yuehong, Xu, Ao, Xu, Jingyi, Xu, Li, Xu, Liangjun, Xu, Menglin, Xu, Qingnian, Xu, Zehua, Xu, Zhihao, Xu, Zijun, Yang, Di, Yang, Shuangli, Yang, Xueting, Yang, Youhua, Yang, Zhenwei, Yang, Zishuo, Yeroshenko, Vsevolod, Yeung, Ho Yin Dereck, Yin, Hang, Yu, Jiesheng, Yuan, Xuhao, Zaffaroni, Ettore, Zavertyaev, Mikhail, Zdybal, Milosz, Zeng, Ming, Zhang, Chenjia, Zhang, Dongliang, Zhang, Jianyu, Zhang, Liming, Zhang, Shulei, Zhang, Shunan, Zhang, Yanxi, Zhang, Yu, Zhao, Ya, Zharkova, Alina, Zhelezov, Alexey, Zheng, Yangheng, Zhou, Tianwen, Zhou, Xiaokang, Zhou, Yixiong, Zhovkovska, Valeriia, Zhu, Xianglei, Zhu, Xiaoyu, Zhu, Zhanwen, Zhukov, Valery, Zhuo, Jiahui, Zou, Quan, Zucchelli, Stefano, Zuliani, Davide, Zunica, Gianluca, Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and LHCb

Subjects
p p: scattering, Phi(1020): pair production, precision measurement, 13000 GeV-cms, +2Phi%281020%29%22">B/s0 --> 2Phi(1020), amplitude analysis: penguin, asymmetry: time dependence, LHC-B, asymmetry: CP, scattering amplitude: interference, High Energy Physics - Experiment, polarization: dependence, B/s0 anti-B/s0: mixing, error: statistical, CERN LHC Coll, B/s0: hadronic decay, CP: violation, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], p p: colliding beams, B/s0: rare decay, experimental results
Abstract

A flavor-tagged time-dependent angular analysis of the decay $B_s^{0}\rightarrow\phi\phi$ is performed using $pp$ collision data collected by the LHCb experiment at $\sqrt{s}=13$ TeV, the center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb^{-1}. The $\it{CP}$-violating phase and direct $\it{CP}$-violation parameter are measured to be $\phi_{s\bar{s}s} = -0.042 \pm 0.075 \pm 0.009 $ rad and $|\lambda|=1.004\pm 0.030 \pm 0.009 $, respectively, assuming the same values for all polarization states of the $\phi\phi$ system. In these results, the first uncertainties are statistical and the second systematic. These parameters are also determined separately for each polarization state, showing no evidence for polarization dependence. The results are combined with previous LHCb measurements using $pp$ collisions at center-of-mass energies of 7 and 8 TeV, yielding $\phi_{s\bar{s}s} = -0.074 \pm 0.069 $ rad and $|\lambda|=1.009 \pm 0.030$. This is the most precise study of time-dependent $\it{CP} $ violation in a penguin-dominated $B$ meson decay. The results are consistent with $\it{CP} $ symmetry and with the Standard Model predictions., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-001.html (LHCb public pages)

Published
2023

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