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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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First Measurement of Antideuteron Number Fluctuations at Energies Available at the Large Hadron Collider

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Physical Review Letters 2023年第4期131卷 041901-041901页

作者： S. Acharya D. Adamová A. Adler G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali Y. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt I. Altsybeev M. N. Anaam C. Andrei A. Andronic V. Anguelov F. Antinori P. Antonioli C. Anuj N. Apadula L. Aphecetche H. Appelshäuser S. Arcelli R. Arnaldi I. C. Arsene M. Arslandok A. Augustinus R. Averbeck S. Aziz M. D. Azmi A. Badalà Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch F. Baruffaldi N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin I. R. Bhat M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. Bilandzic G. Biro S. Biswas J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov H. Bossi E. Botta L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi J. B. Butt A. Bylinkin S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho R. S. Camacho P. Camerini F. D. M. Canedo M. Carabas F. Carnesecchi R. Caron J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. G. Chavez T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. S. Chizzali S. Cho P. Chochula P. Christakoglou C. H. Christensen P. Christiansen T. Chujo M. Ciacco C. Cicalo L. C Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna 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 Affiliated with an institute covered by a cooperation agreement with CERN 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 COMSATS University Islamabad Islamabad Pakistan Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy University of Houston Houston Texas USA 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 Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Westfälische Wilhelms-Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National Laboratory Berkeley California USA SUBATECH IMT Atlantique Nantes Université CNRS-IN2P3 Nantes France Department of Physics University of Oslo Oslo Norway Yale University New Haven Connecticut USA L

The first measurement of event-by-event antideuteron number fluctuations in high energy heavy-ion collisions is presented. The measurements are carried out at midrapidity (|η|<0.8) as a function of collision centrality in Pb-Pb collisions at sNN=5.02 TeV using the ALICE detector. A significant negative correlation between the produced antiprotons and antideuterons is observed in all collision centralities. The results are compared with a state-of-the-art coalescence calculation. While it describes the ratio of higher order cumulants of the antideuteron multiplicity distribution, it fails to describe quantitatively the magnitude of the correlation between antiproton and antideuteron production. On the other hand, thermal-statistical model calculations describe all the measured observables within uncertainties only for correlation volumes that are different with respect to those describing proton yields and a similar measurement of net-proton number fluctuations.

关键词： Hadron-hadron interactions

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Electroweak, QCD and flavour physics studies with ATLAS data from Run 2 of the LHC

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Physics Reports 2025年 1116卷 57-126页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N.J. Abidi, S.H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B.S. Ackermann, A. Adam Bourdarios, C. Adamczyk, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Aktas, S. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Alegria, Z.L. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alsolami, Z.M.K. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M.G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amirie, K.J. Amor Dos Santos, S.P. Amos, K.R. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S.J. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Ashby Pickering, R.A. Assamagan, K. Astalos, R. Astrand, K.S.V. Atashi, S. Atkin, R.J. Atkinson, M. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Baer, T.M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J.T. Baines, L. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balakrishnan, V. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. CPPM Aix-Marseille Université CNRS IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK United States New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton NY United States Physics Department Southern Methodist University Dallas TX United States Faculté des sciences Université Mohammed V Rabat Morocco Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel Department of Physics New York University New York NY United States INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King's College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany LAPP Université Savoie Mont Blanc CNRS IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Brandeis University Waltham MA United States School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb IL United States Department of Physics Istanbul University Istanbul Turkey Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz CA United States Enrico Fermi Institute University of Chicago Chicago IL United States Institut de Física d'Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Italy Dipartimento di Fisica Uni

A summary of precision measurements sensitive to electroweak, QCD and quark-flavour effects performed by the ATLAS Collaboration at the Large Hadron Collider is reported. The measurements are predominantly performed on proton–proton (pp) collision data recorded at a centre-of-mass energy of 13 TeV taken from 2015 to 2018, with an integrated luminosity of up to 140 fb⁻¹, with some results based on pp and Pb+Pb data recorded at lower nucleon centre-of-mass energies. The results cover a wide range of topics, from strong production of particles at low energies and the spectroscopy of hadrons to perturbative QCD with hadronic jets and electroweak and strong production of single and multiple vector bosons. They provide precise measurements of fundamental constants and stringent tests of the Standard Model with unprecedented precision and in energy ranges never explored before. They are also used to explore the proton structure and to perform model-independent searches for new physics. © 2024 CERN for the benefit of the ATLAS Collaboration

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Multiscale modeling meets machine learning: What can we learn?

arXiv

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arXiv 2019年

作者： Peng, Grace C.Y. Alber, Mark Tepole, Adrian Buganza Cannon, William De, Suvranu Dura-Bernal, Salvador Garikipati, Krishna Karniadakis, George Lytton, William W. Perdikaris, Paris Petzold, Linda Kuhl, Ellen National Institute of Biomedical Imaging and Bioengineering National Institutes of Health BethesdaMD United States Department of Mathematics University of California Riverside United States Department of Mechanical Engineering Purdue University LafayetteIN United States Computational Biology Pacific Northwest National Laboratory RichlandWA United States Department of Mechanical Aerospace and Nuclear Engineering Troy New York United States Department of Physiology and Pharmacology State University of New York New York United States Departments of Mechanical Engineering and Mathematics MICDE University of Michigan Ann ArborMI United States Division of Applied Mathematics Brown University ProvidenceRI United States Department of Mechanical Engineering University of Pennsylvania PhiladelphiaPA United States Department of Computer Science and Mechanical Engineering University of California Santa BarbaraCA United States Department of Mechanical Engineering Stanford University StanfordCA United States

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data;multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models f

关键词： Machine learning

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

arXiv

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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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Tensors, differential geometry and statistical shading analysis

arXiv

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arXiv 2017年

作者： Holtmann-Rice, Daniel Niels Kunsberg, Benjamin S. Zucker, Steven W. Department of Computer Science Yale University United States Division of Applied Mathematics Brown University United States

We develop a linear algebraic framework for the shape-from-shading problem, because tensors arise when scalar (e.g. image) and vector (e.g. surface normal) fields are differentiated multiple times. Using this framework, we first investigate when image derivatives exhibit invariance to changing illumination by calculating the statistics of image derivatives under general distributions on the light source. Second, we apply that framework to develop Taylor-like expansions, and build a boot-strapping algorithm to find the polynomial surface solutions (under any light source) consistent with a given patch to arbitrary order. A generic constraint on the light source restricts these solutions to a 2-D subspace, plus an unknown rotation matrix. It is this unknown matrix that encapsulates the ambiguity in the problem. Finally, we use the framework to computationally validate the hypothesis that image orientations (derivatives) provide increased invariance to illumination by showing (for a Lambertian model) that a shape-from-shading algorithm matching gradients instead of intensities provides more accurate reconstructions when illumination is incorrectly estimated under a flatness prior. Copyright © 2017, The Authors. All rights reserved.

关键词： Light sources

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REGULARITY BOUNDS for A GEVREY CRITERION in A KERNEL-BASED REGULARIZATION of the CAUCHY PROBLEM of ELLIPTIC EQUATIONS

arXiv

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arXiv 2017年

作者： Khoa, Vo Anh Hung, Tran The Mathematics and Computer Science Division Gran Sasso Science Institute L-Aquila Italy Faculty of Applied Physics and Mathematics Gdasnk University of Technology Gdasnk Poland

This Note derives regularity bounds for a Gevrey criterion when the Cauchy problem of elliptic equations is solved by regularization. When utilizing the regularization, one knows that checking such criterion is basically problematic, albeit its importance to engineering circumstances. Therefore, coping with that impediment helps us improve the use of some regularization methods in real-world applications. This work also consider the presence of the power-law nonlinearities. Copyright © 2017, The Authors. All rights reserved.

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Deep Generative Models for Fast Photon Shower Simulation in ATLAS

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Computing and Software for Big science 2024年第1期8卷 1-40页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Albicocco, P. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Appelt, C. Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F. Baltes, L.M. Balunas, W.K. Balz, J. Banas Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France APC Université Paris Cité CNRS/IN2P3 Paris France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Department of Physics Istanbul University Istanbul Turkey Istinye University Sariyer Istanbul

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques. © The Author(s) 2024.

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Lattice QCD and Particle Physics

arXiv

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arXiv 2022年

作者： Kronfeld, Andreas S. Bhattacharya, Tanmoy Blum, Thomas Christ, Norman H. DeTar, Carleton Detmold, William Edwards, Robert Hasenfratz, Anna Lin, Huey-Wen Mukherjee, Swagato Orginos, Konstantinos Brower, Richard Cirigliano, Vincenzo Davoudi, Zohreh Jóo, Bálint Jung, Chulwoo Lehner, Christoph Meinel, Stefan Neil, Ethan T. Petreczky, Peter Richards, David G. Bazavov, Alexei Catterall, Simon Dudek, Jozef J. El-Khadra, Aida X. Engelhardt, Michael Fleming, George T. Giedt, Joel Gupta, Rajan Hansen, Maxwell T. Izubuchi, Taku Karsch, Frithjof Laiho, Jack Liu, Keh-Fei Meyer, Aaron S. Rinaldi, Enrico Savage, Martin Schaich, David Shanahan, Phiala E. Sharpe, Stephen R. Sufian, Raza Syritsyn, Sergey Van de Water, Ruth S. Wagman, Michael L. Weinberg, Evan Witzel, Oliver Aubin, Christopher Boyle, Peter Chandrasekharan, Shailesh Cloët, Ian C. Constantinou, Martha Cushman, Kimmy DeGrand, Thomas Fodor, Zoltan Foreman, Sam Gottlieb, Steven Hoying, Daniel Jang, Yong-Chull Jay, William I. Jin, Xiao-Yong Kelly, Christopher Kuti, Julius Lamm, Henry Lin, Meifeng Lin, Yin Lytle, Andrew T. Mackenzie, Paul Mandula, Jeffrey Meurice, Yannick Monahan, Christopher Morningstar, Colin Osborn, James C. Park, Sungwoo Simone, James N. Strelchenko, Alexei Tomii, Masaaki Vaquero, Alejandro Vranas, Pavlos Wang, Bigeng Wilcox, Walter Yoon, Boram Zhao, Yong Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Group T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of Connecticut StorrsCT06269 United States RIKEN BNL Research Center Brookhaven National Laboratory UptonNY11973 United States Department of Physics Columbia University New YorkNY10027 United States Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF Institute for Artificial Intelligence and Fundamental Interactions Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Department of Physics College of William & Mary WilliamsburgVA23187 United States Department of Physics Center for Computational Science Boston University BostonMA02215 United States Institute of Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN37831 United States Fakultät für Physik Universität Regensburg RegensburgD-93040 Germany Department of Physics University of Arizona TucsonAZ85721 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics Syracuse University SyracuseNY13244 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics New Mexic

Lattice field theory provides a mathematically rigorous definition of quantum field theory, including gauge theories. The rigor provides a platform for computation of strongly-coupled gauge theories, not only quantum chromodynamics (QCD) at long-distances but also strongly-coupled sectors that might lie beyond the Standard Model (BSM) of particle physics. Indeed, the interpretation of many experiments in particle physics, nuclear physics, and astrophysics relies, often crucially, on results from lattice QCD or BSM. In quark-flavor physics, lattice QCD is essential for grounding theoretical predictions. Together with experimental measurements, lattice QCD is used to determine many of the fundamental parameters of the Standard Model's flavor sector: five out of six quark masses and the three mixing angles and CP-violating phase of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. Further Standard-Model predictions requiring lattice QCD are for processes that could reveal signatures of new physics. This area of lattice QCD has become a precision science, as has the determination of the strong coupling, αs, which is key in many areas, for example for understanding Higgs-boson decay to gluons. Lattice-QCD calculations of the hadronic contributions to the anomalous magnetic moment of the muon are another area for which precision is both mandatory and achievable. While these contributions can be estimated via a combination of certain measurements and general theoretical concepts, a direct ab initio calculation from QCD is desirable to confirm robustly the tantalizingly large discrepancy between experiment and the Standard Model. Other indirect searches for new physics are complementary to quark-flavor physics and the muon anomalous magnetic moment. Lattice QCD is an essential tool for understanding newly discovered hadrons with quark content different from the usual baryons and mesons. Lattice-QCD calculations are needed to interpret bounds on electric dipole moments, proton dec

关键词： Calculations

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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