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Democratising Knowledge Representation with BioCypher

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

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Market Basket Analysis Using Apriori and FP-Growth for Analy...

2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

Remote health monitoring in the time of COVID-19

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arXiv 2020年

作者： Behar, Joachim A. Liu, Chengyu Tsutsui, Kenta Corino, Valentina D.A. Singh, Janmajay Pimentel, Marco A.F. Karlen, Walter Warrick, Philip Zaunseder, Sebastian Andreotti, Fernando Osipov1, Maxim McSharry, Patrick E. Kotzen, Kevin Karmakar, Chandan Clifford, Gari D. Faculty of Biomedical Engineering Technion-IIT Haifa Israel State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing Department of Cardiovascular Medicine Saitama International Medical Center Saitama Department of Electronics Information and Bioengineering Milano Italy Independent Researcher Department of Engineering Science University of Oxford Oxford United Kingdom Mobile Health Systems Lab Department of Health Sciences and Technology ETH Zurich Zurich Switzerland PeriGen CaryNC United States Faculty of Information Technology FH Dortmund Dortmund Germany Sensyne Health Oxford United Kingdom Babylon Health London United Kingdom Carnegie Mellon University Africa Kigali Rwanda African Centre of Excellence in Data Science University of Rwanda Kigali Rwanda Oxford Man Institute of Quantitative Finance Oxford University Oxford United Kingdom School of Information Technology Deakin University Geelong Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study

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Journal of multimorbidity and comorbidity

Journal of multimorbidity and comorbidity 2021年 11卷

作者： Syed Mustafa Ali David A Selby Kazi Khalid Katherine Dempsey Elaine Mackey Nicola Small Sabine N van der Veer Brian Mcmillan Peter Bower Benjamin Brown John McBeth William G Dixon Centre for Epidemiology Versus Arthritis Division of Musculoskeletal and Dermatological Sciences Manchester Academic Health Science Centre (MAHSC) University of Manchester Manchester UK. Centre for Health Informatics Division of Informatics Imaging and Data Sciences Manchester Academic Health Sciences Centre University of Manchester Manchester UK. NIHR School for Primary Care Research Centre for Primary Care and Health Services Research Division of Population Health Health Services Research and Primary Care School of Health Sciences Faculty of Biology Medicine and health Manchester Academic Health Science Centre Manchester University of Manchester Manchester UK. NIHR Policy Research Unit for Older People and Frailty Centre for Primary Care and Health Services Research Division of Population Health Health Services Research and Primary Care School of Health Sciences Faculty of Biology Medicine and health Manchester Academic Health Science Centre Manchester University of Manchester Manchester UK. NIHR Manchester Biomedical Research Centre Manchester NHS Foundation Trust Manchester UK. Salford Royal NHS Foundation Trust Salford UK.

INTRODUCTION:People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices. AIM:The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M. METHODS:' was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per *** completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type. RESULTS:Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background ( = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23-67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders. CONCLUSION:It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

关键词： Multiple long-term conditions (multimorbidity) patient-generated health data smartwatch user engagement

Overview of the JEM-EUSO Program 7

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Overview of the JEM-EUSO Program

7th International Symposium on Ultra-High-Energy Cosmic Rays, UHECR 2024

作者： Caruso, Rossella Abdullahi, M. Abe, S. Abrate, M. Adams, J.H. Allard, D. Alldredge, P. Aloisio, R. Ammendola, R. Anchordoqui, L. Andreoli, V. Anzalone, A. Arnone, E. Badoni, D. Bagheri, M. Baret, B. Barghini, D. Battisti, M. Bekele, N. Bellotti, R. Belov, A.A. Bertaina, M. Betts, M. Biermann, P. Bisconti, F. Blaksley, C. Blin-Bondil, S. Boezio, M. Bowaire, A.N. Burton, J. Cafagna, F. Campana, D. Capel, F. Caruso, R. Casolino, M. Cassardo, C. Castellina, A. Černý, K. Conti, L. Coretti, A.G. Cremonini, R. Creusot, A. Cummings, A. De Santis, C. de la Taille, C. Di Giovanni, A. Diesing, R. Ebisuzaki, T. Engel, R. Eser, J. Fenu, F. Ferrarese, S. Filippatos, G. Finch, W.W. Fornaro, C. Fuglesang, C. Fukushima, M. Garg, D. Gardiol, D. Garipov, G.K. Gazda, E. Golzio, A. Guépin, C. Haroon, Q. Haungs, A. Heibges, T. Isgrò, F. Judd, E.G. Kajino, F. Kaneko, I. Kupari, L. Kim, S.-W. Klimov, P.A. Kreykenbohm, I. Krizmanic, J.F. Kungel, V. Kuznetsov, E. Liberatori, F. Lima, H.P. Luppa, R. Mandát, D. Manfrin, M. Marcelli, A. Marcelli, L. Marszal, W. Masciantonio, G. Matthews, J.N. Mayotte, E. Mese, M. Meyer, S.S. Mignone, M. Miller, M. Mimouni, J. Miyamoto, H. Mizumoto, Y. Moses, J. Munini, R. Nagataki, S. Nathan, C. Neronov, A. Nicolaidis, R. Nonaka, T. Nozzoli, F. M’sihid, E. Ogawa, T. Ogio, S. Ohmori, H. Olinto, A.V. Onel, Y. Osteria, G. Otte, A.N. Panico, B. Parizot, E. Park, I.H. Paul, T. Pech, M. Perfetto, F. Perrone, L. Petta, C. Picozza, P. Piotrowski, L.W. Pitu, R. Plebaniak, Z. Potts, M. Prévôt, G. Przybylak, M. Reali, E. Reardon, P. Reno, M.H. Reynaud, F. Ricci, E. Ricci, M. Matamala, O.F. Romero Sagawa, H. Sakaki, N. Sarazin, F. Sato, M. Schovánek, P. Scotti, V. Sharakin, S.A. Scherini, V. Schieler, H. Shinozaki, K. Sotgiu, A. Sparvoli, R. Castro, R. Spinola Stillwell, B. Szabelski, J. Tajima, N. Tajima, T. Takahashi, Y. Takeda, M. Takizawa, Y. Thomas, S.B. Department of Physics and Astronomy "E.Majorana" University of Catania INFN-Catania Via Santa Sofia 64 Catania95123 Italy Algiers Algeria Univ. Constantine I Constantine Algeria Joint Laboratory of Optics Faculty of Science Palacký University Olomouc Czech Republic Institute of Physics Czech Academy of Sciences Prague Czech Republic Omega Ecole Polytechnique CNRS IN2P3 Palaiseau France Université de Paris CNRS ParisF-75013 France France Laboratoire Univers et Particules de Montpellier Universit Montpellier CNRS IN2P3 CC72 place Eugne Bataillon Montpellier34095 Cedex 5 France Germany Max Planck Institute for Physics Munich Germany University of Erlangen-Nuremberg Erlangen Germany Istituto Nazionale di Fisica Nucleare Sezione di Bari Italy Università degli Studi di Bari Aldo Moro Italy Dipartimento di Fisica e Astronomia "Ettore Majorana" Università di Catania Italy Istituto Nazionale di Fisica Nucleare Sezione di Catania Italy Istituto Nazionale di Fisica Nucleare Laboratori Nazionali di Frascati Italy Istituto Nazionale di Fisica Nucleare Sezione di Napoli Italy Università di Napoli Federico II Dipartimento di Fisica "Ettore Pancini" Italy INAF - Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo Italy Istituto Nazionale di Fisica Nucleare Sezione di Roma Tor Vergata Italy Università di Roma Tor Vergata Dipartimento di Fisica Roma Italy Istituto Nazionale di Fisica Nucleare Sezione di Torino Italy Dipartimento di Fisica Università di Torino Italy Osservatorio Astrofisico di Torino Istituto Nazionale di Astrofisica Italy Uninettuno University Rome Italy Agenzia Spaziale Italiana Via del Politecnico Roma00133 Italy Gran Sasso Science Institute L’Aquila Italy INFN Gran Sasso National Laboratory AQ AssergiI-67100 Italy University of Salento via per Arnesano Lecce73100 Italy INFN Section of Lecce via per Arnesano Lecce73100 Italy Centro Universitario di Monte Sant’Angelo Via Cintia Naples80126 Italy Arpa Piemonte

Since 2010, the international JEM-EUSO (Joint Exploratory Missions for Extreme Universe Space Observatory) Collaboration has been developing an ambitious program with the support of major International and National Space Agencies and research funding institutions, to enable UltraHigh-Energy Cosmic Ray (UHECR) and High-Energy (HE) neutrino observations from space. Its main objective is to develop a large mission with dedicated instrumentation looking down on the Earth atmosphere from space, both towards nadir and/or towards the limb, to detect the Extensive Air Showers (EAS) initiated by such particles in the atmosphere. This strategy is intended to complement the observations made with ground-based observatories, by allowing a significant increase in the exposure at the highest energies, and achieving near-uniform full sky coverage, as part of the global effort to better characterize the phenomenology of UHECRs, discover their sources and understand their acceleration mechanism. In the last decade, the JEM-EUSO Collaboration successfully developed five intermediate missions: one ground based (EUSO-TA), three balloon-borne (EUSO-Balloon, EUSO-SPB1, EUSO-SPB2) and one space-based (MiniEUSO), and also benefited from the experience gained by the space mission TUS. Important studies for a full-scale mission have also been carried out, namely K-EUSO and the stereo double telescope Probe Of Extreme Multi-Messenger Astrophysics (POEMMA), to be considered for a NASA Probe Mission in the next decade. The technical and scientific achievements of this rich and manifold program are reported on, as well as the scientific objectives updated in the light of the knowledge gained from the previous missions, and additional scientific objectives accessible to such unique technology. Finally, the near-future developments of the JEM-EUSO Program are briefly presented, with the continuation of the Mini-EUSO observations and the new balloon-borne mission PBR. © Copyright owned by the autho

关键词： Clouds

Photometric and spectroscopic study of the burst-like brightening of two Gaia-alerted young stellar objects

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arXiv 2022年

作者： Nagy, Zsófia Ábrahám, Péter Kóspál, Ágnes Park, Sunkyung Siwak, Michal de Miera, Fernando Cruz-Sáenz Fiorellino, Eleonora García-Álvarez, David Szabó, Zsófia Marianna Antoniucci, Simone Giannini, Teresa Giunta, Alessio Kriskovics, Levente Kun, Mária Marton, Gábor Moór, Attila Nisini, Brunella Pál, Andras Szabados, László Zieliński, Pawel Wyrzykowski, Lukasz Konkoly Thege Miklós út 15-17 BudapestH-1121 Hungary CSFK MTA Centre of Excellence Konkoly Thege Miklós út 15-17 BudapestH-1121 Hungary ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A BudapestH-1117 Hungary Max Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany INAF-Osservatorio Astronomico di Roma via di Frascati 33 Monte Porzio Catone 00078 Italy Instituto de Astrofísica de Canarias Avenida Vía Láctea Tenerife La LagunaE-38205 Spain Grantecan S.A. Centro de Astrofísica de La Palma Cuesta de San José La Palma Breña BajaE-38712 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany School of Physics and Astronomy University of St Andrews North Haugh St AndrewsKY16 9SS United Kingdom Space Science Data Center Italian Space Agency Via del Politecnico Roma00133 Italy Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń ul. Grudziądzka 5 Toruń87-100 Poland Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warsaw00-478 Poland

Young stars show variability on different time-scales from hours to decades, with a range of amplitudes. We studied two young stars, which triggered the Gaia science Alerts system due to brightenings on a time-scale of a year. Gaia20bwa brightened by about half a magnitude, whereas Gaia20fgx brightened by about two and half magnitudes. We analyzed the Gaia light curves, additional photometry, and spectra taken with the Telescopio Nazionale Galileo and the Gran Telescopio Canarias. Several emission lines were detected toward Gaia20bwa, including hydrogen lines from Hα to Hδ, Paβ, Brγ, and lines of Ca ii, O i, and Na i. The Hα and Brγ lines were detected toward Gaia20fgx in emission in its bright state, with additional CO lines in absorption, and the Paβ line with an inverse P Cygni profile during its fading. Based on the Brγ lines the accretion rate was (2.4 − 3.1) × 10−8 M yr−1 for Gaia20bwa and (4.5 − 6.6) × 10−8 M yr−1 for Gaia20fgx during their bright state. The accretion rate of Gaia20fgx dropped by almost a factor of 10 on a time-scale of half a year. The accretion parameters of both stars were found to be similar to those of classical T Tauri stars, lower than those of young eruptive stars. However, the amplitude and time-scale of these brightenings place these stars to a region of the parameter space, which is rarely populated by young stars. This suggests a new class of young stars, which produce outbursts on a time-scale similar to young eruptive stars, but with smaller amplitudes. © 2022, CC BY.

关键词： Stars

Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm

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arXiv 2018年

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a 23-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a 54-approximation algorithm. Our analysis is tight in all cases except *** codes 68Q25 Copyright © 2018, The Authors. All rights reserved.

关键词： Vectors

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

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Diagnostic and prognostic research 2020年第1期4卷 9页

作者： Lucy M Bull Mark Lunt Glen P Martin Kimme Hyrich Jamie C Sergeant Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research Manchester Academic Health Science Centre University of Manchester Manchester UK. Centre for Biostatistics Manchester Academic Health Science Centre University of Manchester Manchester UK. Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester UK. National Institute for Health Research Manchester Biomedical Research Centre Manchester University NHS Foundation Trust Manchester Academic Health Science Centre Manchester UK.

BACKGROUND:Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. METHODS:MEDLINE, Embase and Web of science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. RESULTS:The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. CONCLUSIONS:The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

关键词： Clinical risk prediction Dynamic prediction Electronic health records Joint models Longitudinal data Personalised medicine Prediction models Repeated observations Survival analysis Time-dependent covariates

Measurement of ω meson production in pp collisions at = 13 TeV

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Journal of High Energy Physics 2025年第4期2025卷 1-30页

作者： Acharya, S. Adamová, D. Agarwal, A. Aglieri Rinella, G. Aglietta, L. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S. U. Ahuja, I. Akindinov, A. Akishina, V. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H. M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altamura, A. R. Altsybeev, I. Alvarado, J. R. Anaam, M. N. Andrei, C. Andreou, N. Andronic, A. Andronov, E. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J. G. M. C. A. Arsene, I. C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M. D. Baba, H. Badalà, A. Bae, J. Baek, Y. W. Bai, X. Bailhache, R. Bailung, Y. Bala, R. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbasova, V. Barile, F. Barioglio, L. Barlou, M. Barman, B. Barnaföldi, G. G. Barnby, L. S. Barreau, E. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J. L. Bearden, I. G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A. D. C. Bellini, F. Bellwied, R. Belokurova, S. Beltran, L. G. E. Beltran, Y. A. V. Bencedi, G. Bensaoula, A. Beole, S. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M. G. Betev, L. Bhaduri, P. P. Bhasin, A. Bhat, M. A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčík, J. Bielčíková, J. Bigot, A. P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J. T. Blau, D. Blidaru, M. B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bok, J. Boldizsár, L. Bombara, M. Bond, P. M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A. G. Bossi, H. Botta, E. Bouziani, Y. E. M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G. E. Buckland, M. D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Burmasov, N. Buthelezi, Z. Bylinkin, A. Bysiak, S. A. Cabanillas Noris, J. C. Cabrera, M. F. T. Cai, M. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J. M. M. Camerini, P. Canedo, F. D. M. Cantway, S. L. Carabas, M. Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento di Fisica dell’Università and Sezione INFN Turin Italy Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Frankfurt Institute for Advanced Studies Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico University of Houston Houston United States Sungkyunkwan University Suwon City Republic of Korea Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany INFN Sezione di Bari Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Universidad Autónoma de Puebla Puebla Mexico Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania University of Derby Derby United Kingdom Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy INFN Sezione di Bologna Bologna Italy Lawrence Berkeley National Laboratory Berkeley United Sta

The pT-differential cross section of ω meson production in pp collisions at $$ \sqrt{s} $$ = 13 TeV at midrapidity (|y| < 0.5) was measured with the ALICE detector at the LHC, covering an unprecedented transverse-momentum range of 1.6 < pT < 50 GeV/c. The meson is reconstructed via the ω → π+π−π0 decay channel. The results are compared with various theoretical calculations: PYTHIA8.2 with the Monash 2013 tune overestimates the data by up to 50%, whereas good agreement is observed with Next-to-Leading Order (NLO) calculations incorporating ω fragmentation using a broken SU(3) model. The ω/π0 ratio is presented and compared with theoretical calculations and the available measurements at lower collision energies. The presented data triples the pT ranges of previously available measurements. A constant ratio of Cω/π0 = 0.578 ± 0.006 (stat.) ± 0.013 (syst.) is found above a transverse momentum of 4 GeV/c, which is in agreement with previous findings at lower collision energies within the systematic and statistical uncertainties.

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