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Light cone distribution amplitude for the Λ baryon from lattice QCD

Physical Review D 2025年第3期111卷 034510-034510页

作者： Min-Huan Chu Haoyang Bai Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Yushan Su Wei Wang Xiaonu Xiong INPAC Key Laboratory for Particle Astrophysics and Cosmology (MOE) Shanghai Key Laboratory for Particle Physics and Cosmology School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Faculty of Physics Adam Mickiewicz University ulica Uniwersytetu Poznańskiego 2 61-614 Poznań Poland Institute of High Energy Physics CAS Beijing 100049 China School of Physics University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Department of Physics University of Maryland College Park Maryland 20742 USA Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China School of Physics Central South University Changsha 418003 China

We calculate the leading-twist light cone distribution amplitudes of the Λ(uds) baryon using lattice QCD methods within the framework of large-momentum effective theory. Our numerical computations are carried out with Nf=2+1 stout smeared clover fermions and a Symanzik gauge action on a lattice with spacing a=0.077 fm and a pion mass of 303 MeV. To approach the large-momentum region, we simulate the equal-time correlations with the hadron momentum Pz={2.52,3.02,3.52} GeV. We investigate the analytic properties of the baryon quasidistribution amplitude in coordinate space and validate them through our lattice calculations. After renormalization and extrapolation, we present results for the three-dimensional distribution in momentum fractions of the two light quarks. Based on these findings, we briefly discuss the phenomenological impact on weak decays of Λb and outline potential systematic uncertainties that can be improved in the future. This work lays the theoretical foundation for accessing baryon light cone distribution amplitudes from lattice QCD.

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Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data using Deep Learning

arXiv

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arXiv 2023年

作者： Meijer, Quirijn Lopez, Melissa Tsuna, Daichi Caudill, Sarah Department of Physics Utrecht University Princetonplein 1 Utrecht3584CC Netherlands Nikhef Science Park 105 Amsterdam1098XG Netherlands TAPIR Mailcode 350-17 California Institute of Technology PasadenaCA91125 United States School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States

Gravitational-wave searches for cosmic strings are currently hindered by the presence of detector glitches, some classes of which strongly resemble cosmic string signals. This confusion greatly reduces the efficiency of searches. A deep-learning model is proposed for the task of distinguishing between gravitational-wave signals from cosmic string cusps and simulated blip glitches in design sensitivity data from the future Einstein Telescope. The model is an ensemble consisting of three convolutional neural networks, achieving an accuracy of 79%, a true positive rate of 76%, and a false positive rate of 18%. This marks the first time convolutional neural networks have been trained on a realistic population of Einstein Telescope glitches. On a dataset consisting of signals and glitches, the model is shown to outperform matched filtering, specifically being better at rejecting glitches. The behaviour of the model is interpreted through the application of several methods, including a novel technique called waveform surgery, used to quantify the importance of waveform sections to a classification model. In addition, a method to visualise convolutional neural network activations for one-dimensional time series is proposed and used. These analyses help further the understanding of the morphological differences between cosmic string cusp signals and blip glitches. Because of its classification speed in the order of magnitude of milliseconds, the deep-learning model is suitable for future use as part of a real-time detection pipeline. The deep-learning model is transverse and can therefore potentially be applied to other transient searches. Copyright © 2023, The Authors. All rights reserved.

关键词： Gravity waves

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Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

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Physical Review D 2020年第12期101卷 124054-124054页

作者： Feroz H. Shaik Jacob Lange Scott E. Field Richard O’Shaughnessy Vijay Varma Lawrence E. Kidder Harald P. Pfeiffer Daniel Wysocki Department of Physics Department of Mathematics and the Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA Center for Computational Relativity and Gravitation Rochester Institute of Technology Rochester New York 14623 USA Department of Mathematics and the Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA Theoretical Astrophysics California Institute of Technology Pasadena California 91125 USA Cornell Center for Astrophysics and Planetary Science Cornell University Ithaca New York 14853 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 14476 Potsdam Germany

Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with a detector-frame mass set to 120 M⊙ and with signal amplitude values that are consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Our work can be used to better understand asymmetric binary black hole merger events, such as GW190412. Higher modes are critical for such systems, and their omission usually produces substantial parameter biases.

关键词： Gravitational waves

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Survey of gravitational wave memory in intermediate mass ratio binaries

arXiv

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arXiv 2021年

作者： Islam, Tousif Field, Scott E. Khanna, Gaurav Warburton, Niels Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics University of Rhode Island KingstonRI02881 United States School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland

The non-linear gravitational wave (GW) memory effect is a distinct prediction in general relativity. While the effect has been well studied for comparable mass binaries, it has mostly been overlooked for intermediate mass ratio inspirals (IMRIs). We offer a comprehensive analysis of the phenomenology and detectability of memory effects, including contributions from subdominant harmonic modes, in heavy IMRIs consisting of a stellar mass black hole and an intermediate mass black hole. When formed through hierarchical mergers, for example when a GW190521-like remnant captures a stellar mass black hole, IMRI systems have a large total mass, large spin on the primary, and possibly residual eccentricity;features that potentially raise the prospect for memory detection. We compute both the displacement and spin non-linear GW memory from the m 6= 0 gravitational waveforms computed within a black hole perturbation theory framework that is partially calibrated to numerical relativity waveforms. We probe the dependence of memory effects on mass ratio, spin, and eccentricity and consider the detectability of a memory signal from IMRIs using current and future GW detectors. We find that (i) while eccentricity introduces additional features in both displacement and spin memory, it does not appreciatively change the prospects of detectability, (ii) including higher modes into the memory computation can increase signal-to-noise (SNR) values by about 7% in some cases, (iii) the SNR from displacement memory dramatically increases as the spin approaches large, positive values, (iv) spin memory from heavy IMRIs would, however, be difficult to detect with future generation detectors even from highly spinning systems. Our results suggest that hierarchical binary black hole mergers may be a promising source for detecting memory and could favorably impact memory forecasts. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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Simulating dirty bosons on a quantum computer

arXiv

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arXiv 2022年

作者： Oftelie, Lindsay Bassman Van Beeumen, Roel Camps, Daan de Jong, Wibe A. Dupont, Maxime Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The physics of dirty bosons highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum computer allowed us to study the effect of quantum hardware noise on the physical properties of the simulated system. Our results suggest that scaling laws control how noise modifies observables versus its strength, the circuit depth, and the number of qubits. Moreover, we observe that noise impacts the delocalized and localized phases differently. A better understanding of how noise alters the genuine properties of the simulated system is essential for leveraging noisy intermediate-scale quantum devices for simulation of dirty bosons, and indeed for condensed matter systems in general. Copyright © 2022, The Authors. All rights reserved.

关键词： Bosons

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Dynamics of stratified-convected Eyring-Powell nanoliquid featuring chemically reactive species and Ohmic dissipation: Application of Levenberg-Marquardt artificial neural networks(ALM-ANNs)

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International Journal of Heat and Fluid Flow 2024年 108卷

作者： Shah, Zahoor Waqas, M. Asif Zahoor Raja, Muhammad Shahzad, Faisal Zamri, Nurnadiah Juraev, Nizomiddin Alanazi, Meznah M. Department of Mathematics COMSATS University Islamabad Islamabad Campus Pakistan Department of Computer Science National University of Technology Islamabad44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Mohi-Ud-Din Islamic University Nerian Sharif AJK Pakistan Faculty of Chemical Engineering New Uzbekistan University Tashkent Uzbekistan Scientific and Innovation Department Tashkent State Pedagogical University Tashkent Uzbekistan Department of Physics College of Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut Malaysia

Magnetic nanoliquids are pondered cutting-edge heat transference liquids, representing a modern generation because of their aptitude to function as smart liquids. The deployed magnetism externally exerts a readily manageable impact, escalating their adaptableness and practicality in various applications. In this analysis, electro magnetized Powell-Eyring rheological nanoliquid is modeled considering stretching cylinder based buoyancy-driven flow. Transport expressions include thermal radiation, Brownian diffusion, chemical reaction, thermophoresis, and thermal source effect. Prandtl theory of boundary-layer (BL) assists to develop the governing problems. Appropriate transformations are deployed to obtain the dimensionless problems which are then computed numerically through the novel Levenberg-Marquardt artificial neural networks(ALM-ANNs). It is worth to mention that the close agreement between the error analysis of the reference dataset and the proposed datasets roughly from E−10 to E−03, further justifies the approaches utilized in this study. Besides the excellent measures of performance in terms of MSE are achieved at level 5.77E−11, 4.41E−11, 2.09E−12, 2.16E−11, 1.03E−11, 1.65E−11, 2.00E−11, and 1.91E−11against 272, 416, 266, 391, 142, 366, 425, and 508 epochs for first instance of all eight various scenarios. The acquired outcomes are compared with alternate analytical (homotopy) scheme and good agreement is witnessed. Furthermore it is noticed that as the values of Pr,RandS1 increase in size, so does the EPFM temperature profile θ, and the evaluation of AE is between E-04 to E-10. © 2024

关键词： Boundary layers

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A semi-adaptive finite difference method for simulating two-sided fractional convection–diffusion quenching problems

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Journal of Computational and Applied Mathematics 2026年 472卷

作者： Rumin Dong Lin Zhu Qin Sheng Bingxin Zhao School of Mathematics and Statistics Ningxia Key Laboratory of Interdisciplinary Mechanics and Scientific Computing Ningxia Basic Science Research Center of Mathematics Ningxia University Yinchuan 750021 China Department of Mathematics and Center for Astrophysics Space Physics and Engineering Research Baylor University Waco TX 76798-7328 USA

This paper investigates quenching solutions of an one-dimensional, two-sided Riemann–Liouville fractional order convection–diffusion problem. Fractional order spatial derivatives are discretized using weighted averaging approximations in conjunction with standard and shifted Grünwald formulas. The advective term is handled utilizing a straightforward Euler formula, resulting in a semi-discretized system of nonlinear ordinary differential equations. The conservativeness of the proposed scheme is rigorously proved and validated through simulation experiments. The study is further advanced to a fully discretized, semi-adaptive finite difference method. Detailed analysis is implemented for the monotonicity, positivity and stability of the scheme. Investigations are carried out to assess the potential impacts of the fractional order on quenching location, quenching time, and critical length. The computational results are thoroughly discussed and analyzed, providing a more comprehensive understanding of the quenching phenomena modeled through two-sided fractional order convection–diffusion problems.

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Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA’s fourth observing run

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Physical Review D 2024年第4期109卷 042008-042008页

作者： Becca Ewing Rachael Huxford Divya Singh Leo Tsukada Chad Hanna Yun-Jing Huang Prathamesh Joshi Alvin K. Y. Li Ryan Magee Cody Messick Alex Pace Anarya Ray Surabhi Sachdev Shio Sakon Ron Tapia Shomik Adhicary Pratyusava Baral Amanda Baylor Kipp Cannon Sarah Caudill Sushant Sharma Chaudhary Michael W. Coughlin Bryce Cousins Jolien D. E. Creighton Reed Essick Heather Fong Richard N. George Patrick Godwin Reiko Harada James Kennington Soichiro Kuwahara Duncan Meacher Soichiro Morisaki Debnandini Mukherjee Wanting Niu Cort Posnansky Andrew Toivonen Takuya Tsutsui Koh Ueno Aaron Viets Leslie Wade Madeline Wade Gaurav Waratkar Department of Physics The Pennsylvania State University University Park Pennsylvania 16802 USA Institute for Gravitation and the Cosmos The Pennsylvania State University University Park Pennsylvania 16802 USA Department of Astronomy and Astrophysics The Pennsylvania State University University Park Pennsylvania 16802 USA Institute for Computational and Data Sciences The Pennsylvania State University University Park Pennsylvania 16802 USA LIGO Laboratory California Institute of Technology Pasadena California 91125 USA MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA School of Physics Georgia Institute of Technology Atlanta 30332 USA RESCEU The University of Tokyo Tokyo 113-0033 Japan Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Institute of Multimessenger Astrophysics and Cosmology Missouri University of Science and Technology Physics Building 1315 N. Pine St. Rolla Missouri 65409 USA School of Physics and Astronomy University of Minnesota Minneapolis Minnesota 55455 USA Department of Physics University of Illinois Urbana Illinois 61801 USA Canadian Institute for Theoretical Astrophysics 60 St George St Toronto Ontario M5S 3H8 Canada Graduate School of Science The University of Tokyo Tokyo 113-0033 Japan Center for Gravitational Physics University of Texas at Austin Austin Texas 78712 USA LIGO Laboratory California Institute of Technology MS 100-36 Pasadena California 91125 USA Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8582 Japan NASA Marshall Space Flight Center Huntsville Alabama 35811 USA Center for Space Plasma and

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA Collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation.

关键词： Gravitational wave detection Astronomical black holes Binary stars

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A fully precessing higher-mode surrogate model of effective-one-body waveforms

arXiv

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arXiv 2022年

作者： Gadre, Bhooshan Pürrer, Michael Field, Scott E. Ossokine, Serguei Varma, Vijay Am Mühlenberg 1 Potsdam14476 Germany Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Department of Physics East Hall University of Rhode Island KingstonRI02881 United States URI Center for Computational Research Tyler Hall University of Rhode Island KingstonRI02881 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States

We present a surrogate model of SEOBNRv4PHM, a fully precessing time-domain effective-one-body (EOB) waveform model including subdominant modes. We follow an approach similar to that used to build recent numerical relativity surrogate models. Our surrogate is 5000M in duration, covers mass-ratios up to 1:20 and dimensionless spin magnitudes up to 0.8. Validating the surrogate against an independent test set we find that the median mismatch error is less than 10−3, which is typically smaller than the modeling error of SEOBNRv4PHM itself. At high total mass, a few percent of configurations can exceed mismatches of 10−2 if they are highly precessing and exceed a mass ratio of 1:4. This surrogate is nearly two orders of magnitude faster than the underlying time-domain SEOBNRv4PHM model and can be evaluated in ∼ 50 ms. Bayesian inference analyses with SEOBNRv4PHM are typically very computationally demanding and can take from weeks to months to complete. The two-order of magnitude speedup attained by our surrogate model enables practical parameter estimation analyses with this waveform family. This is crucial because Bayesian inference allows us to recover the masses and spins of binary black hole mergers given a model of the emitted gravitational waveform along with a description of the noise. © 2022, CC BY.

关键词： Relativity

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

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TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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