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arXiv 2017年

作者： Yu, Weiren Lin, Xuemin Zhang, Wenjie McCann, Julie A. School of Engineering and Applied Science Aston University School of Computer Science and Engineering University of New South Wales Department of Computing Imperial College London

SimRank is an appealing pair-wise similarity measure based on graph structure. It iteratively follows the intuition that two nodes are assessed as similar if they are pointed to by similar nodes. Many real graphs are large, and links are constantly subject to minor changes. In this article, we study the efficient dynamical computation of all-pairs SimRanks on timevarying graphs. Existing methods for the dynamical SimRank computation (e.g., L-TSF [20] and READS [28]) mainly focus on top-k search with respect to a given query. For all-pairs dynamical SimRank search, Li et al.'s approach [13] was proposed for this problem. It first factorizes the graph via a singular value decomposition (SVD), and then incrementally maintains such a factorization in response to link updates at the expense of exactness. As a result, all pairs of SimRanks are updated approximately, yielding O(r4n2) time and O(r2n2) memory in a graph with n nodes, where r is the target rank of the low-rank SVD. Our solution to the dynamical computation of Sim- Rank comprises of five ingredients: (1) We first consider edge update that does not accompany new node insertions. We show that the SimRank update ΔS in response to every link update is expressible as a rank-one Sylvester matrix equation. This provides an incremental method requiring O(Kn2) time and O(n2) memory in the worst case to update n2pairs of similarities for K iterations. (2) To speed up the computation further, we propose a lossless pruning strategy that captures the "affected areas" of ΔS to eliminate unnecessary retrieval. This reduces the time of the incremental Sim-Rank to O(K(m + |AFF|)), where m is the number of edges in the old graph, and |AFF| (≤ n2) is the size of "affected areas" in ΔS, and in practice, |AFF| n2. (3) We also consider edge updates that accompany node insertions, and categorize them into three cases, according to which end of the inserted edge is a new node. For each case, we devise an efficient incremental algorit

关键词： Singular value decomposition

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Astronomy potential of KM3NeT/ARCA230 38

Astronomy potential of KM3NeT/ARCA230

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： van Eeden, Thijs Juan Aiello, S. Albert, A. Alves Garre, S. Aly, Z. Ambrosone, A. Ameli, F. Andre, M. Androutsou, E. Anguita, M. Aphecetche, L. Ardid, M. Ardid, S. Atmani, H. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Basegmez du Pree, S. Becherini, Y. Bendahman, M. Benfenati, F. Benhassi, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chadolias, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Cruz, A. Cuttone, G. Dallier, R. Darras, Y. De Benedittis, A. De Martino, B. Decoene, V. Del Burgo, R. Di Cerbo, U.M. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diaz, C. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Dörr, M. Drakopoulou, E. Drouhin, D. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gabriel, J. Gagliardini, S. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geißelbrecht, N. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goupilliere, D. Gozzini, S.R. Gracia, R. Graf, K. Guidi, C. Guillon, B. Gutiérrez, M. van Haren, H. Heijboer, A. Hekalo, A. Hennig, L. Hernández-Rey, J.J. Huang, F. Idrissi Ibnsalih, W. Illuminati, G. James, C.W. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kalekin, O. Katz, U.F. Khan Chowdhury, N.R. Khatun, A. Kistauri, G. Kopper, C. Kouchner, A. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Larosa, G. Lastoria, C. Lazo, A. Le Stum, S. Lehaut, G. Leonora, E. Nikhef Science Park 105 Amsterdam Netherlands INFN Sezione di Catania Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain NCSR Demokritos Institute of Nuclear and Particle Physics Ag. Paraskevi Attikis Athens15310 Greece University of Granada Dept. of Computer Architecture and Technology/CITIC Granada18071 Spain Subatech IMT Atlantique IN2P3-CNRS Université de Nantes 4 rue Alfred Kastler - La Chantrerie BP 20722 Nantes44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Nikhef National Institute for Subatomic Physics PO Box 41882 Amsterdam1009 DB Netherlands INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Diparti

The KM3NeT/ARCA neutrino detector is currently under construction at the bottom of the Mediterranean Sea. The main science objective is the detection of high-energy cosmic neutrinos and the discovery of their sources. This is achieved by instrumenting a cubic kilometre of seawater with photo-multiplier tubes that detect Cherenkov radiation from neutrino interaction products. In recent years, there have been advancements in the reconstruction algorithms for muon neutrinos, along with the development of techniques for identifying and accurately reconstructing electron and tau neutrinos. This contribution will present the updated prospects for cosmic sources using the track and cascade detection signatures for the full detector. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutrons

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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Gaussian variant of freivalds' algorithm for efficient and reliable matrix product verification

arXiv

引用

arXiv 2017年

作者： Ji, Hao Mascagni, Michael Li, Yaohang Department of Computer Science Old Dominion University Departments of Computer Science Mathematics and Scientific Computing Florida State University Applied and Computational Mathematics Division National Institute of Standards and Technology

In this article, we consider the general problem of checking the correctness of matrix multiplication. Given three n × n matrices A, B, and C, the goal is to verify that A × B = C without carrying out the computationally costly operations of matrix multiplication and comparing the product A×B with C, term by term. This is especially important when some or all of these matrices are very large, and when the computing environment is prone to soft errors. Here we extend Freivalds' algorithm to a Gaussian Variant of Freivalds' Algorithm (GVFA) by projecting the product A × B as well as C onto a Gaussian random vector and then comparing the resulting vectors. The computational complexity of GVFA is consistent with that of Freivalds' algorithm, which is O(n2). However, unlike Freivalds' algorithm, whose probability of a false positive is 2-k, where k is the number of iterations. Our theoretical analysis shows that when A × B 6= C, GVFA produces a false positive on set of inputs of measure zero with exact arithmetic. When we introduce round-off error and floating point arithmetic into our analysis, we can show that the larger this error, the higher the probability that GVFA avoids false positives. Moreover, by iterating GVFA k times, the probability of a false positive decreases as pk, where p is a very small value depending on the nature of the fault on the result matrix and the arithmetic system's floating-point precision. Unlike deterministic algorithms, there do not exist any fault patterns that are completely undetectable with GVFA. Thus GVFA can be used to provide efficient fault tolerance in numerical linear algebra, and it can be efficiently implemented on modern computing architectures. In particular, GVFA can be very efficiently implemented on architectures with hardware support for fused multiply-add operations. Copyright © 2017, The Authors. All rights reserved.

关键词： Matrix algebra

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ForceTab: Visuo-haptic interaction with a force-sensitive actuated tablet

ForceTab: Visuo-haptic interaction with a force-sensitive ac...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jens Maiero Ernst Kruijff André Hinkenjann Gheorghita Ghinea Institute of Visual Computing Bonn-Rhein-Sieg University of Applied Sciences Germany Department of Computer Science Brunel University United Kingdom

ISBN: (纸本)9781509060689

Enhancing touch screen interfaces through non-visual cues has been shown to improve performance. In this paper we report on a novel system that explores the usage of a forcesensitive motion-platform enhanced tablet interface to improve multi-modal interaction based on visuo-haptic instead of tactile feedback. Extending mobile touch screen with force-sensitive haptic feedback has potential to enhance performance interacting with GUIs and to improve perception of understanding relations. A user study was performed to determine the perceived recognition of different 3D shapes and the perception of different heights. Furthermore, two application scenarios are proposed to explore our proposed visuo-haptic system. The studies show the positive stance towards the feedback, as well as the found limitations related to perception of feedback.

关键词： Shape Force Touch sensitive screens Haptic interfaces Servomotors Hardware Graphical user interfaces

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Sharp transitions in rotating turbulent convection: Lagrangian acceleration statistics reveal a second critical Rossby number

arXiv

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arXiv 2019年

作者： Alards, Kim M.J. Kunnen, Rudie P.J. Stevens, Richard J.A.M. Lohse, Detlef Toschi, Federico Clercx, Herman J.H. Fluid Dynamics Laboratory and J.M. Burgers Center for Fluid Dynamics Department of Applied Physics Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Physics of Fluids Group Max Planck Center for Complex Fluid Dynamics J.M. Burgers Center for Fluid Dynamics and MESA+ Research Institute Department of Science and Technology University of Twente P.O. Box 217 Enschede7500 AE Netherlands Max Planck Institute for Dynamics and Self-Organization Am Fassberg 17 Göttingen37077 Germany Centre of Analysis Scientific Computing and Applications W&I Department of Mathematics and Computer Science Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Istituto per le Applicazioni del Calcolo Consiglio Nazionale delle Ricerche Via dei Taurini 19 Rome00185 Italy

In Rayleigh–Bénard convection (RBC) for fluids with Prandtl number Pr 1, rotation beyond a critical (small) rotation rate is known to cause a sudden enhancement of heat transfer which can be explained by a change in the character of the boundary layer (BL) dynamics near the top and bottom plates of the convection cell. Namely, with increasing rotation rate, the BL signature suddenly changes from Prandtl–Blasius type to Ekman type. The transition from a constant heat transfer to an almost linearly increasing heat transfer with increasing rotation rate is known to be sharp and the critical Rossby number Roc occurs typically in the range 2.3 Roc 2.9 (for Rayleigh number Ra = 1.3×109, Pr = 6.7, and a convection cell with aspect ratio Γ = HD = 1, with D the diameter and H the height of the cell). The explanation of the sharp transition in the heat transfer points to the change in the dominant flow structure. At 1/Ro . 1/Roc (slow rotation), the well-known large-scale circulation (LSC) is found: a single domain-filling convection roll made up of many individual thermal plumes. At 1/Ro 1/Roc (rapid rotation), the LSC vanishes and is replaced with a collection of swirling plumes that align with the rotation axis. In this paper, by numerically studying Lagrangian acceleration statistics, related to the small-scale properties of the flow structures, we reveal that this transition between these different dominant flow structures happens at a second critical Rossby number, Roc2 ≈ 2.25 (different from Roc1 ≈ 2.7 for the sharp transition in the Nusselt number Nu;both values for the parameter settings of our present numerical study). When statistical data of Lagrangian tracers near the top plate are collected, it is found that the root-mean-square (rms) values and the kurtosis of the horizontal acceleration of these tracers show a sudden increase at Roc2. To better understand the nature of this transition we compute joint statistics of the Lagrangian velocity and acceleration of f

关键词： Flow structure

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Analyzing feature space via pauli decomposition for quantum classifier

arXiv

引用

arXiv 2019年

作者： Suzuki, Yudai Yano, Hiroshi Gao, Qi Uno, Shumpei Tanaka, Tomoki Akiyama, Manato Yamamoto, Naoki Department of Mechanical Engineering Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan Department of Information and Computer Science Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan Department of Biosciences and Informatics Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan Mitsubishi Chemical Corporation Science & Innovation Center 1000 Kamoshida-cho Aoba-ku Yokohama227-8502 Japan Mizuho Information & Research Institute Inc. 2-3 Kanda-Nishikicho Chiyoda-ku Tokyo101-8443 Japan Mitsubishi UFJ Financial Group Inc. and MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo100-8388 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Kohoku Hiyoshi Yokohama223- 8522 Japan

A method for analyzing the feature space used in the quantum classifier on the basis of Pauli decomposition is developed. In particular, for 2-dimensional input datasets, the method boils down to a general formula that easily computes a lower bound of the exact training accuracy, which eventually helps us to see whether the feature space constructed with the selected feature map is suitable for linearly separating the dataset. The effectiveness of this formula is demonstrated, with the special type of five feature maps and four 2-dimensional non-linear datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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PETSc/TS: A modern scalable ODE/DAE solver library

arXiv

引用

arXiv 2018年

作者： Abhyankar, Shrirang Brown, Jed Constantinescu, Emil M. Ghosh, Debojyoti Smith, Barry F. Zhang, Hong Energy Systems Division Argonne National Laboratory 9700 South Cass Avenue LemontIL60439-5844 United States Department of Computer Science University of Colorado Boulder 430 UCB BoulderCO80309 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 South Cass Avenue LemontIL60439-4844 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94550 United States

High-quality ordinary differential equation (ODE) solver libraries have a long history, going back to the 1970s. Over the past several years we have implemented, on top of the PETSc linear and nonlinear solver package, a new general-purpose, extensive, extensible library for solving ODEs and differential algebraic equations (DAEs). Package includes support for both forward and adjoint sensitivities that can be easily utilized by the TAO optimization package, which is also part of PETSc. The ODE/DAE integrator library strives to be highly scalable but also to deliver high efficiency for modest-sized problems. The library includes explicit solvers, implicit solvers, and a collection of implicit-explicit solvers, all with a common user interface and runtime selection of solver types, adaptive error control, and monitoring of solution progress. The library also offers enormous exibility in selection of nonlinear and linear solvers, including the entire suite of PETSc iterative solvers, as well as several parallel direct solvers. Copyright © 2018, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Measurement of the atmospheric muon neutrino flux with KM3NeT/ORCA6 38

Measurement of the atmospheric muon neutrino flux with KM3Ne...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Stavropoulos, Dimitris Tzamariudaki, Ekaterini Drakopoulou, Evangelia Markou, Christos Aiello, S. Albert, A. Alves Garre, S. Aly, Z. Ambrosone, A. Ameli, F. Andre, M. Androutsou, E. Anguita, M. Aphecetche, L. Ardid, M. Ardid, S. Atmani, H. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Basegmez du Pree, S. Becherini, Y. Bendahman, M. Benfenati, F. Benhassi, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chadolias, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Cruz, A. Cuttone, G. Dallier, R. Darras, Y. De Benedittis, A. De Martino, B. Decoene, V. Del Burgo, R. Di Cerbo, U.M. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diaz, C. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Dörr, M. Drakopoulou, E. Drouhin, D. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gabriel, J. Gagliardini, S. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geißelbrecht, N. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goupilliere, D. Gozzini, S.R. Gracia, R. Graf, K. Guidi, C. Guillon, B. Gutiérrez, M. van Haren, H. Heijboer, A. Hekalo, A. Hennig, L. Hernández-Rey, J.J. Huang, F. Idrissi Ibnsalih, W. Illuminati, G. James, C.W. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kalekin, O. Katz, U.F. Khan Chowdhury, N.R. Khatun, A. Kistauri, G. Kopper, C. Kouchner, A. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Larosa, G. National Technical University of Athens School of Applied Mathematical and Physical Sciences Zografou Campus 9 Iroon Polytechniou str Zografou Athens15780 Greece INFN Sezione di Catania Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain NCSR Demokritos Institute of Nuclear and Particle Physics Ag. Paraskevi Attikis Athens15310 Greece University of Granada Dept. of Computer Architecture and Technology/CITIC Granada18071 Spain Subatech IMT Atlantique IN2P3-CNRS Université de Nantes 4 rue Alfred Kastler - La Chantrerie BP 20722 Nantes44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Nikhef National Institute for Subatomic Physics PO Box 41882 Amsterdam1009

The KM3NeT/ORCA detector (Oscillation Research with Cosmics in the Abyss) is an array of Digital Optical Modules, spheres that host 31 photomultiplier tubes, tied together in vertical structures, the Detection Units, which are anchored on the seabed. Such an array configuration can detect neutrino events from the Cherenkov radiation emitted by the secondary particles induced by neutrino interactions in the abyssal depths of the Mediterranean Sea. The KM3NeT/ORCA detector is being deployed at a depth of 2450 m offshore Toulon, France with the determination of the Neutrino Mass Ordering being the main physics goal of the detector. The aim of this work is the study of atmospheric neutrinos with energies at the 1-100 GeV range, in order to obtain information on the atmospheric muon neutrino flux in this energy range, in which only few measurements exist by other experiments. An analysis of data collected with the 6-Detection Unit configuration of KM3NeT/ORCA (KM3NeT/ORCA6) is being presented in this contribution. The data analyzed corresponds to a time period of one and a half year. The procedure for the selection of a high-purity atmospheric neutrino sample, using a Machine Learning classifier (Boosted Decision Tree), is described. Subsequently, an unfolding scheme is used to obtain an estimation of the atmospheric muon neutrino flux in bins of energy in the region of interest. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutron flux

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Search for a diffuse astrophysical neutrino flux with KM3NeT/ARCA using data of 2021-2022 38

Search for a diffuse astrophysical neutrino flux with KM3NeT...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Tsourapis, Vasileios Drakopoulou, Evangelia Markou, Christos Sinopoulou, Anna Tzamariudaki, Ekaterini Aiello, S. Albert, A. Alves Garre, S. Aly, Z. Ambrosone, A. Ameli, F. Andre, M. Androutsou, E. Anguita, M. Aphecetche, L. Ardid, M. Ardid, S. Atmani, H. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Basegmez du Pree, S. Becherini, Y. Bendahman, M. Benfenati, F. Benhassi, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chadolias, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Cruz, A. Cuttone, G. Dallier, R. Darras, Y. De Benedittis, A. De Martino, B. Decoene, V. Del Burgo, R. Di Cerbo, U.M. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diaz, C. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Dörr, M. Drakopoulou, E. Drouhin, D. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gabriel, J. Gagliardini, S. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geißelbrecht, N. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goupilliere, D. Gozzini, S.R. Gracia, R. Graf, K. Guidi, C. Guillon, B. Gutiérrez, M. van Haren, H. Heijboer, A. Hekalo, A. Hennig, L. Hernández-Rey, J.J. Huang, F. Idrissi Ibnsalih, W. Illuminati, G. James, C.W. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kalekin, O. Katz, U.F. Khan Chowdhury, N.R. Khatun, A. Kistauri, G. Kopper, C. Kouchner, A. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Larosa, G. NCSR "Demokritos" Institute of Nuclear and Particle Physics Ag. Paraskevi Attikis Athens15310 Greece National Technical University of Athens School of Applied Mathematical and Physical Sciences Zografou Campus 9 Iroon Polytechniou str Zografou Athens15780 Greece INFN Sezione di Catania Via Santa Sofia Catania64 - 95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain University of Granada Dept. of Computer Architecture and Technology/CITIC Granada18071 Spain Subatech IMT Atlantique IN2P3-CNRS Université de Nantes 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Nikhef National Institute for Subatomic Physics PO Box 41882 Amsterdam1

KM3NeT is a research infrastructure hosting two neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector focuses on the detection of high energy neutrinos (>TeV) from astrophysical sources. In this contribution, an analysis of the data obtained during 2021-2022, using machine learning techniques, is presented. The potential of KM3NeT/ARCA to detect a diffuse flux of astrophysical neutrinos at the early stages of the detector construction is reported. The results of this analysis are presented. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Astrophysics

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