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Bottomonium production in heavy-ion collisions using quantum trajectories: Differential observables and momentum anisotropy

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Physical Review D 2021年第9期104卷 094049-094049页

作者： Nora Brambilla Miguel Ángel Escobedo Michael Strickland Antonio Vairo Peter Vander Griend Johannes Heinrich Weber Physik-Department Technische Universität München James-Franck-Strasse 1 85748 Garching Germany Institute for Advanced Study Technische Universität München Lichtenbergstrasse 2 a 85748 Garching Germany Munich Data Science Institute Technische Universität München Walther-von-Dyck-Strasse 10 85748 Garching Germany Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela E-15782 Galicia Spain Department of Physics Kent State University Kent Ohio 44242 USA Department of Computational Mathematics Science and Engineering and Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA Institut für Physik Humboldt-Universität zu Berlin & IRIS Adlershof D-12489 Berlin Germany

We report predictions for the suppression and elliptic flow of the ϒ(1S), ϒ(2S), and ϒ(3S) as a function of centrality and transverse momentum in ultrarelativistic heavy-ion collisions. We obtain our predictions by numerically solving a Lindblad equation for the evolution of the heavy-quarkonium reduced density matrix derived using potential nonrelativistic QCD and the formalism of open quantum systems. To numerically solve the Lindblad equation, we make use of a stochastic unraveling called the quantum trajectories algorithm. This unraveling allows us to solve the Lindblad evolution equation efficiently on large lattices with no angular momentum cutoff. The resulting evolution describes the full 3D quantum and non-Abelian evolution of the reduced density matrix for bottomonium states. We expand upon our previous work by treating differential observables and elliptic flow; this is made possible by a newly implemented Monte Carlo sampling of physical trajectories. Our final results are compared to experimental data collected in sNN=5.02 TeV Pb-Pb collisions by the ALICE, ATLAS, and CMS collaborations.

关键词： Particle & resonance production Quark-gluon plasma Relativistic heavy-ion collisions

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Author Correction: Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task

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Scientific reports 2020年第1期10卷 11482页

作者： Pavel Kharyuk Dmitry Nazarenko Ivan Oseledets Igor Rodin Oleg Shpigun Andrey Tsitsilin Mikhail Lavrentyev Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Moscow 143026 Russia. kharyuk.pavel@***. Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow 119991 Russia. kharyuk.pavel@***. Lomonosov Moscow State University Faculty of Chemistry Moscow 119991 Russia. dmitro.nazarenko@***. Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Moscow 143026 Russia. Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow 119991 Russia. Lomonosov Moscow State University Faculty of Chemistry Moscow 119991 Russia. All-Russian Research Institute of Medicinal and Aromatic Plants (VILAR) Moscow 117216 Russia. Saratov State University Department of Botanics and Ecology Saratov 410012 Russia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Automatic Variationally Stable Analysis for Finite Element Computations: Transient Convection-Diffusion Problems

arXiv

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arXiv 2020年

作者： Valseth, Eirik Behnoudfar, Pouria Dawson, Clint Romkes, Albert Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States The Department of Data Science The Norwegian University of Life Science Drøbakveien 31 Ås1433 Norway Department of Scientific Computing and Numerical Analysis Simula Research Laboratory Kristian Augusts gate 23 Oslo0164 Norway PerthWA6152 Australia Department of Mechanical Engineering South Dakota School of Mines & Technology Rapid CitySD57701 United States

We present an application of stable finite element (FE) approximations of convection-diffusion initial boundary value problems (IBVPs) using a weighted least squares FE method, the automatic variationally stable finite element (AVS-FE) method [1]. The transient convection-diffusion problem leads to issues in classical FE methods as the differential operator can be considered a singular perturbation in both space and time. The stability property of the AVS-FE method, allows us significant flexibility in the construction of FE approximations in both space and time. Thus, in this paper, we take two distinct approaches to the FE discretization of the convection-diffusion problem: i) considering a space-time approach in which the temporal discretization is established using finite elements, and ii) a method of lines approach in which we employ the AVS-FE method in space whereas the temporal domain is discretized using the generalized-α method. We also consider another space-time technique in which the temporal direction is partitioned, thereby leading to finite space-time"slices" in an attempt to reduce the computational cost of the space-time discretizations. We present numerical verifications for these approaches, including numerical asymptotic convergence studies highlighting optimal convergence properties. Furthermore, in the spirit of the discontinuous Petrov-Galerkin (DPG) method by Demkowicz and Gopalakrishnan [2–6], the AVS-FE method also leads to readily available a posteriori error estimates through a Riesz representer of the residual of the AVS-FE approximations. Hence, the norm of the resulting local restrictions of these estimates serve as error indicators in both space and time for which we present multiple numerical verifications in mesh adaptive *** Codes 65M60 65M12 65M20 65M50 Copyright © 2020, The Authors. All rights reserved.

关键词： Finite element method

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Continuous black-box optimization with quantum annealing and random subspace coding

arXiv

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arXiv 2021年

作者： Izawa, Syun Kitai, Koki Tanaka, Shu Tamura, Ryo Tsuda, Koji Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo Kashiwa277-8561 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama223-8522 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply quantum annealing (QA) to overcome the difficulty in the continuous black-box optimization. As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back. Our method has the following three parts: 1) Random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector. 2) A quadratic unconstrained binary optimization (QUBO) defined by acquisition function based on nonnegative-weighted linear regression model which is solved by QA. 3) A penalization scheme to ensure that the QA solution can be translated back. It is shown in benchmark tests that its performance using D-Wave AdvantageTM quantum annealer is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up a new possibility of quantum annealing and other QUBO solvers including quantum approximate optimization algorithm (QAOA) using a gated-quantum computers, and expand its range of application to continuous-valued problems. © 2021, CC BY.

关键词： Annealing

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Distributed Algorithms for Subgraph-Centric Graph Platforms

arXiv

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arXiv 2019年

作者： Kakwani, Diptanshu Simmhan, Yogesh Information Science and Engineering M. S. Ramaiah Institute of Technology Bangalore India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Graph analytics for large scale graphs has gained interest in recent years. Many graph algorithms have been designed for vertex-centric distributed graph processing frameworks to operate on large graphs with 100 M vertices and edges, using commodity clusters and Clouds. Subgraph-centric programming models have shown additional performance benefits than vertex-centric models. But direct mapping of vertex-centric and shared-memory algorithms to subgraph-centric frameworks are either not possible, or lead to inefficient algorithms. In this paper, we present three subgraph-centric distributed graph algorithms for triangle counting, clustering and minimum spanning forest, using variations of shared and vertex-centric models. These augment existing subgraph-centric algorithms that exist in literature, and allow a broader evaluation of these three classes of graph processing algorithms and platforms. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Chimera states in hybrid coupled neuron populations

arXiv

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arXiv 2020年

作者： Calim, Ali Torres, Joaquin J. Ozer, Mahmut Uzuntarla, Muhammet Department of Biomedical Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Department of Electromagnetism and Physics of the Matter Institute Carlos I for Theoretical and Computational Physics University of Granada GranadaE-18071 Spain Ministry of National Education Ankara Turkey Center for Artificial Intelligence and Data Science Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

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DUALZ - Deep UNCOVER-ALMA Legacy High-Z Survey

arXiv

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arXiv 2023年

作者： Fujimoto, Seiji Bezanson, Rachel Labbe, Ivo Brammer, Gabriel Price, Sedona H. Wang, Bingjie Weaver, John R. Fudamoto, Yoshinobu Oesch, Pascal A. Williams, Christina C. Dayal, Pratika Feldmann, Robert Greene, Jenny E. Leja, Joel Whitaker, Katherine E. Zitrin, Adi Cutler, Sam E. Furtak, Lukas J. Pan, Richard Chemerynska, Iryna Kokorev, Vasily Miller, Tim B. Atek, Hakim Van Dokkum, Pieter Juneau, Stéphanie Kassin, Susan Khullar, Gourav Marchesini, Danilo Maseda, Michael Nelson, Erica J. Setton, David J. Smit, Renske Department of Astronomy The University of Texas at Austin AustinTX78712 United States Department of Physics and Astronomy Pitt Pacc University of Pittsburgh PittsburghPA15260 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Niels Bohr Institute University of Copenhagen Jagtvej 128 Kobenhavn N DK-2200 Denmark Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Astronomy University of Massachusetts AmherstMA01003 United States Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Tokyo Mitaka Japan Department of Astronomy University of Geneva Chemin Pegasi 51 Versoix1290 Switzerland NSF's National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Avenue TucsonAZ85719 United States Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands Institute for Computational Science University of Zurich ZurichCH-8057 Switzerland Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Physics Department Ben-Gurion University of the Negev P.O. Box 653 Beér-Sheva 84105 Israel Department of Physics and Astronomy Tufts University 574 Boston Ave. MedfordMA02155 United States Institut d'Astrophysique de Paris Cnrs Sorbonne Université 98bis Boulevard Arago Paris75014 France Department of Astronomy Yale University New HavenCT06511 United States D

We present the survey design and initial results of the ALMA Cycle 9 program of DUALZ, which aims to establish a joint ALMA and JWST public legacy field targeting the massive galaxy cluster Abell 2744. DUALZ features a contiguous 4′×6′ ALMA 30-GHz-wide mosaic in Band 6, covering areas of μ > 2 down to a sensitivity of σ = 32.7 μJy. Through a blind search, we identified 69 dust continuum sources at S/N 5.0 with median redshift and intrinsic 1.2-mm flux of z = 2.30 and S1.2mmint = 0.24 mJy. Of these, 27 have been spectroscopically confirmed, leveraged by the latest NIRSpec observations, while photometric redshifts are also constrained by the comprehensive HST, NIRCam, and ALMA data sets for the remaining sources. With priors, we further identify a [C ii]158 μm line emitter at z = 6.3254 ± 0.0004, confirmed by the latest NIRSpec spectroscopy. The NIRCam counterparts of the 1.2-mm continuum exhibit undisturbed morphologies, denoted either by disk or spheroid, implying the triggers for the faint mm emission are less catastrophic than mergers. We have identified 8 HST-dark galaxies (F150W>27 mag, F150W-F444W>2.3) and 2 JWST-dark (F444W>30 mag) galaxy candidates among the ALMA continuum sources. The former includes face-on disk galaxies, hinting that substantial dust obscuration does not always result from inclination. We also detect a marginal dust emission from an X-ray-detected galaxy at zspec = 10.07, suggesting an active co-evolution of the central black hole and its host. We assess the infrared luminosity function up to z ∼ 10 and find it consistent with predictions from galaxy formation models. To foster diverse scientific outcomes from the community, we publicly release reduced ALMA mosaic maps, cubes, and the source cataloga) © 2023, CC BY.

关键词： Galaxies

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EvAn: Neuromorphic event-based anomaly detection

arXiv

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arXiv 2019年

作者： Annamalai, Lakshmi Chakraborty, Anirban Thakur, Chetan Singh Defence Reserach and Development Organization Bangalore India Department of Computational Data Sciences at Indian Institute of Science Bangalore India Department of Electronic Systems Engineering Indian Institute of Science Bangalore India

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur. Moreover, such cameras, by design, encode only the relative motion between the scene and the sensor (and not the static background) to yield a very sparse data structure, which can be utilized for various motion analytics tasks. In this paper, for the first time in event data analytics community, we leverage these advantages of an event camera towards a critical vision application -video anomaly detection. We propose to model the motion dynamics in the event domain with dual discriminator conditional Generative adversarial Network (cGAN) built on state-of-the-art architectures. To adapt event data for using as input to cGAN, we also put forward a deep learning solution to learn a novel representation of event data, which retains the sparsity of the data as well as encode the temporal information readily available from these sensors. Since there is no existing dataset for anomaly detection in event domain, we also provide an anomaly detection event dataset with an exhaustive set of anomalies. We empirically validate different components of our architecture on this proposed dataset as well as validate the benefits of our event data representation over state-of-the-art event data representations on video anomaly detection application. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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A high black hole to host mass ratio in a lensed AGN in the early Universe

arXiv

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arXiv 2023年

作者： Furtak, Lukas J. Labbé, Ivo Zitrin, Adi Greene, Jenny E. Dayal, Pratika Chemerynska, Iryna Kokorev, Vasily Miller, Tim B. Goulding, Andy D. de Graaff, Anna Bezanson, Rachel Brammer, Gabriel B. Cutler, Sam E. Leja, Joel Pan, Richard Price, Sedona H. Wang, Bingjie Weaver, John R. Whitaker, Katherine E. Atek, Hakim Bogdán, Ákos Charlot, Stéphane Curtis-Lake, Emma van Dokkum, Pieter Endsley, Ryan Feldmann, Robert Fudamoto, Yoshinobu Fujimoto, Seiji Glazebrook, Karl Juneau, Stéphanie Marchesini, Danilo Maseda, Micheal V. Nelson, Erica Oesch, Pascal A. Plat, Adèle Setton, David J. Stark, Daniel P. Williams, Christina C. Physics Department Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Kapteyn Astronomical Institute University of Groningen P.O. Box 800 Groningen9700 AV Netherlands Institut d’Astrophysique de Paris CNRS Sorbonne Université 98bis Boulevard Arago Paris75014 France Department of Astronomy Yale University New HavenCT06511 United States Department of Physics and Astronomy Northwestern University 1800 Sherman Ave EvanstonIL60201 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany Department of Physics and Astronomy PITT PACC University of Pittsburgh PittsburghPA15260 United States Niels Bohr Institute University of Copenhagen Jagtvej 128 København NDK-2200 Denmark Department of Astronomy University of Massachusetts AmherstMA01003 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy Tufts University 574 Boston Ave. MedfordMA02155 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Centre for Astrophysics Research Department of Physics Astronomy and Mathematics University of Hertfordshire HatfieldAL10 9AB United Kingdom Department of Astronomy The University of Texas at Austin AustinTX78712 United States Institute for Computational Science University of Zurich ZurichCH-8057 Switzerland Waseda Research Institute for Science and Engineering Faculty of Science and Engineering W

Early JWST observations have uncovered a new population of red sources that might represent a previously overlooked phase of supermassive black hole growth [1–3]. One of the most intriguing examples is an extremely red, point-like object that was found to be triply-imaged by the strong lensing (SL) cluster Abell 2744 [4]. Here we present deep JWST/NIRSpec observations of this object, Abell2744-QSO1. The spectroscopy confirms that the three images are of the same object, and that it is a highly reddened (AV 3) broad emission line Active Galactic Nucleus (AGN) at a redshift of zspec = 7.0451 ± 0.0005. From the width of Hβ (FWHM = 2800 ± 250 kms ) we derive a black hole mass of MBH = 4+2−1 × 107 M. We infer a very high ratio of black hole to galaxy mass of at least 3 %, an order of magnitude more than is seen in local galaxies [5], and possibly as high as 100 %. The lack of strong metal lines in the spectrum together with the high bolometric luminosity (Lbol = (1.1 ±0.3) ×1045 ergs ) indicate that we are seeing the black hole in a phase of rapid growth, accreting at 30 % of the Eddington limit. The rapid growth and high black hole to galaxy mass ratio of A2744-QSO1 suggest that it may represent the missing link between black hole seeds [6] and the first luminous quasars [7]. Copyright © 2023, The Authors. All rights reserved.

关键词： Black holes

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An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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