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arXiv 2022年

作者： Harel, Re'em Rusanovsky, Matan Wagner, Ron Levin, Harel Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Scientific Computing Center Nuclear Research Center Negev Israel Department of Physics Nuclear Research Center Negev Israel Israel Atomic Energy Commission Israel Department of Computer Science Technion Israel Institute of Technology Israel School of Physics and Astronomy Tel Aviv University Israel

Supercomputers worldwide provide the necessary infrastructure for groundbreaking research. As the demand for enhancing their performance constantly increases, new systems are built worldwide to supply this demand at an increased pace, volume, and power. However, most supercomputers are not designed equally due to different desired figure of merit, which is derived from the computational bounds of the targeted scientific applications’ portfolio. In turn, the design of such computers becomes a budget-constrained optimization process that strives to achieve the best performances possible in a multi-parameters search space, including heterogeneous hardware, middleware, and software. Therefore, verifying and evaluating whether a supercomputer can achieve its desired goal becomes a tedious and complex task. For this purpose, many full, mini, proxy, and benchmark applications have been introduced in the attempt to represent scientific applications partially. Nevertheless, as these benchmarks are hard to expand, update technologically, and most importantly, are over-simplified compared to scientific applications that tend to couple multiple scientific domains, they fail to represent the true scaling capabilities. We suggest a new physical scalable benchmark framework, namely SCALSALE, based on the well-known SALE scheme. SCALSALE’s main goal is to provide a simple, flexible, scalable infrastructure that can be easily expanded to include multi-physical schemes while maintaining scalable and efficient execution times. By expanding SCALSALE, the gap between the over-simplified benchmarks and scientific applications can be bridged. To achieve this goal, SCALSALE is implemented in Modern Fortran with simple object-oriented design patterns and supported by transparent MPI-3 blocking and non-blocking communication that allows such a scalable framework. In this manner, SCALSALE easily achieves almost identical performances in similar settings to common original benchmark suites of

关键词： Object oriented programming

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Unpolarized transverse momentum dependent parton distributions of the nucleon from lattice QCD

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Physical Review D 2024年第11期109卷 114513-114513页

作者： Jin-Chen He Min-Huan Chu Jun Hua Xiangdong Ji Andreas Schäfer Yushan Su Wei Wang Yi-Bo Yang Jian-Hui Zhang Qi-An Zhang Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Department of Physics 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 Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Institut für Theoretische Physik Southern Center for Nuclear-Science Theory (SCNT) CAS Key Laboratory of Theoretical Physics School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Beijing/Hangzhou China School of Physical Sciences School of Science and Engineering Center of Advanced Quantum Studies Department of Physics School of Physics

We present a first lattice QCD calculation of the unpolarized nucleon’s isovector transverse-momentum-dependent parton distribution functions (TMDPDFs), which are essential to predict observables of multiscale, semi-inclusive processes in the standard model. We use a Nf=2+1+1 MILC ensemble with valence clover fermions on a highly improved staggered quark (HISQ) sea to compute the quark momentum distributions in a large-momentum nucleon on the lattice. The state-of-the-art techniques in renormalization and extrapolation in the correlation distance on the lattice are adopted. The perturbative kernel up to next-to-next-to-leading order is taken into account, and the dependence on the pion mass and the hadron momentum is explored. Our results are qualitatively comparable with phenomenological TMDPDFs, which provide an opportunity to predict high energy scatterings from first principles.

关键词： Lattice QCD Nucleon distribution Parton distribution functions

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GWSurrogate: A Python package for gravitational wave surrogate models

arXiv

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arXiv 2025年

作者： Field, Scott E. Varma, Vijay Blackman, Jonathan Gadre, Bhooshan Galley, Chad R. Islam, Tousif Mitman, Keefe Pürrer, Michael Ravichandran, Adhrit Scheel, Mark A. Stein, Leo C. Yoo, Jooheon Department of Mathematics Center for Scientific Computing & Data Science Research University of Massachusetts DartmouthMA02747 United States Theoretical Astrophysics 350-17 California Institute of Technology PasadenaCA91125 United States Utrecht University Utrecht3584 CC Netherlands Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics Center for Computational Research East Hall University of Rhode Island KingstonRI02881 United States Department of Physics and Astronomy The University of Mississippi UniversityMS38677 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States

Gravitational waves are ripples in space-time caused by the motion of massive objects. One of the most astrophysically important sources of gravitational radiation is caused by two orbiting compact objects, such as black holes and neutron stars, that slowly inspiral and merge. The motion of these massive objects generates gravitational waves that radiate to the far field where gravitational-wave detectors can observe them. Complicated partial or ordinary differential equations govern the entire process. Traditionally, the dynamics of compact binary systems and the emitted gravitational waves have been computed by expensive simulation codes that can take days to months to run. A key simulation output is the gravitational wave signal for a particular set of parameter values describing the system, such as the black holes’ masses and spins. The computed signal is required for a diverse range of multiple-query applications, such as template bank generation for searches, parameter estimation, mock data analysis, studies of model bias, and tests of general relativity, to name a few. In such settings, the high-fidelity signal computed from differential equations is often too slow to be directly used. Surrogate models offer a practical way to dramatically accelerate model evaluation while retaining the high-fidelity accuracy of the expensive simulation code;an example is shown in Fig. 1. Surrogate models can be constructed in various ways, but what separates these models from other modeling frameworks is that they are primarily data-driven. Given a training set of gravitational waveform data sampling the parameter space, a model is built by following three steps: 1. Feature extraction: the waveform is decomposed into data pieces that are simple to model, 2. Dimensionality reduction: each data piece is approximated by a low-dimensional vector space, which reduces the degrees of freedom we need to model, and 3. Regression: fitting and regression techniques are applied to the l

关键词： Gravitational effects

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Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI 15th

Gaussian Process Emulators for Few-Shot Segmentation in Ca...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Viti, Bruno Thaler, Franz Kapper, Kathrin Lisa Urschler, Martin Holler, Martin Karabelas, Elias Department of Mathematics and Scientific Computing University of Graz Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783031877551

Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot learning has the capability of reducing data dependency on labeled data. In this work, we introduce a new method that merges few-shot learning with a U-Net architecture and Gaussian Process Emulators (GPEs), enhancing data integration from a support set for improved performance. GPEs are trained to learn the relation between the support images and the corresponding masks in latent space, facilitating the segmentation of unseen query images given only a small labeled support set at inference. We test our model with the M&Ms-2 public dataset to assess its ability to segment the heart in cardiac magnetic resonance imaging from different orientations, and compare it with state-of-the-art unsupervised and few-shot methods. Our architecture shows higher DICE coefficients compared to these methods, especially in the more challenging setups where the size of the support set is considerably small. The code is available on GitLab (https://***/bruno_viti/gpe_4_cardiac_fss). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gaussian distribution

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Searching for Asymmetric and Heavily Precessing Binary Black Holes in the Gravitational Wave Data from the LIGO Third Observing Run

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Physical Review Letters 2024年第20期133卷 201401-201401页

作者： Stefano Schmidt Sarah Caudill Jolien D. E. Creighton Leo Tsukada Anushka Doke Manvi Jain Anarya Ray Shomik Adhicary Pratyusava Baral Nikhef Science Park 105 1098 XG Amsterdam The Netherlands Institute for Gravitational and Subatomic Physics (GRASP) Utrecht University Princetonplein 1 3584 CC Utrecht The Netherlands 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 Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA 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 International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India

Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily misaligned spins in LIGO and Virgo data taken during the third observing run. Our target systems show strong imprints of precession whereas current searches have nonoptimal sensitivity in detecting them. After measuring the sensitivity improvement brought by our search over standard spin-aligned searches, we report the detection of 30 gravitational wave events already discovered in the latest version of the Gravitational Wave Transient Catalog. However, we do not find any additional significant gravitational wave candidates. Our results allow us to place an upper limit of R90%=0.28−0.04+0.33 Gpc−3 yr−1 on the merger rate of a hypothetical subpopulation of asymmetric, heavily precessing signals, not identified by other searches. Since our upper limit is consistent with the latest rate estimates from the LIGO-Virgo-KAGRA Collaboration, our findings rule out the existence of a yet-to-be-discovered population of precessing binaries.

关键词： Gravitational waves Data analysis

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Modeling the BMS transformation induced by a binary black hole merger

arXiv

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arXiv 2025年

作者： Da Re, Guido Mitman, Keefe Stein, Leo C. Scheel, Mark A. Teukolsky, Saul A. Sun, Dongze Boyle, Michael Deppe, Nils Field, Scott E. Kidder, Lawrence E. Moxon, Jordan Nelli, Kyle C. Throwe, William Varma, Vijay Vu, Nils L. Theoretical Astrophysics 350-17 California Institute of Technology PasadenaCA91125 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Physics and Astronomy University of Mississippi UniversityMS38677 United States Laboratory for Elementary Particle Physics Cornell University IthacaNY14853 United States Department of Physics Cornell University IthacaNY14853 United States Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States

Understanding the characteristics of the remnant black hole formed in a binary black hole merger is crucial for conducting gravitational wave astronomy. Typically, models of remnant black holes provide information about their mass, spin, and kick velocity. However, other information related to the supertranslation symmetries of the BMS group, such as the memory effect, is also important for characterizing the final state of the system. In this work, we build a model of the BMS transformation that maps a binary black hole’s inspiral frame to the remnant black hole’s canonical rest frame. Training data for this model are created using high-precision numerical relativity simulations of quasi-circular systems with mass ratios q ≤ 8 and spins parallel to the orbital angular momentum with magnitudes χ1, χ2 ≤ 0.8. We use Gaussian Process Regression to model the BMS transformations over the three-dimensional parameter space (q, χz1, χz2). The physics captured by this model is strictly non-perturbative and cannot be obtained from post-Newtonian approximations alone, as it requires knowledge of the strong nonlinear effects that are sourced during the merger. Apart from providing the first model of the supertranslation induced by a binary black hole merger, we also find that the kick velocities predicted using Cauchy-characteristic evolution waveforms are, on average, ∼ 5% larger than the ones obtained from extrapolated waveforms. Our work has broad implications for improving gravitational wave models and studying the large-scale impact of memory, such as on the cosmological background. The fits produced in this work are available through the Python package surfinBH under the name NRSur3dq8BMSRemnant. © 2025, CC BY.

关键词： Relativity

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The Fundamental Limit of Jet Tagging

arXiv

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arXiv 2024年

作者： Geuskens, Joep Gite, Nishank Krämer, Michael Mikuni, Vinicius Mück, Alexander Nachman, Benjamin Reyes-González, Humberto Institute for Theoretical Particle Physics and Cosmology RWTH Aachen University Germany Department of Physics University of California BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Identifying the origin of high-energy hadronic jets (‘jet tagging’) has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence - are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics. © 2024, CC BY.

关键词： Hadrons

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Adding higher-order spherical harmonics in non-spinning eccentric binary black hole merger waveform models

arXiv

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arXiv 2024年

作者： Islam, Tousif Khanna, Gaurav Field, Scott E. Kavli Institute for Theoretical Physics University of California Santa Barbara Kohn Hall Lagoon Rd Santa BarbaraCA93106 United States Theoretical AstroPhysics Including Relativity and Cosmology California Institute of Technology PasadenaCA United States Department of Physics Center for Computational Research University of Rhode Island KingstonRI02881 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States

gwNRHME is a recently developed framework that seamlessly converts a multi-modal (i.e with several spherical harmonic modes) quasi-circular waveform into multi-modal eccentric waveform if the quadrupolar eccentric waveform is known. Here, we employ the gwNRHME framework to combine a multi-modal quasi-circular waveform model NRHybSur3dq8 and quadrupolar non-spinning eccentric waveform model EccentricIMR to construct multi-modal non-spinning eccentric model NRHybSur3dq8-gwNRHME. Using a total of 35 eccentric numerical relativity (NR) simulations obtained from the SXS and RIT catalogs, we demonstrate that NRHybSur3dq8-gwNRHME model predictions agree well with NR (with typical relative L2 errors of ∼ 0.01 for the dominant quadrupolar mode) for mass ratios 1 ≤ q ≤ 4 and eccentricities up to ∼ 0.2 measured about 10 cycles before the merger. To demonstrate the modularity of the gwNRHME framework, we further combine EccentricIMR with BHPTNRSur1dq1e4 model and develop a non-spinning eccentric models named BHPTNRSur1dq1e4-gwNRHME. Finally, we develop a different variant of these models by replacing EccentricIMR with EccentricTD. Both the gwNRHME framework and associated models are available through the gwModels package. Copyright © 2024, The Authors. All rights reserved.

关键词： Spheres

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GW190521: Tracing imprints of spin-precession on the most massive black hole binary

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Physical Review D 2024年第2期109卷 024024-024024页

作者： Simona J. Miller Maximiliano Isi Katerina Chatziioannou Vijay Varma Ilya Mandel Department of Physics California Institute of Technology Pasadena California 91125 USA LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany School of Physics and Astronomy Monash University Clayton Victoria 3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery Clayton Victoria 3800 Australia

GW190521 is a remarkable gravitational-wave signal on multiple fronts: its source is the most massive black hole binary identified to date and could have spins misaligned with its orbit, leading to spin-induced precession; an astrophysically consequential property linked to the binary’s origin. However, due to its large mass, GW190521 was only observed during its final 3–4 cycles, making precession constraints puzzling and giving rise to alternative interpretations, such as eccentricity. Motivated by these complications, we trace the observational imprints of precession on GW190521 by dissecting the data with a novel time domain technique, allowing us to explore the morphology and interplay of the few observed cycles. We find that precession inference hinges on a quiet portion of the pre-merger data that is suppressed relative to the merger ringdown. Neither premerger nor postmerger data alone are the sole driver of inference, but rather their combination; in the quasicircular scenario, precession emerges as a mechanism to accommodate the lack of a stronger premerger signal in light of the observed postmerger. In terms of source dynamics, the premerger suppression arises from a tilting of the binary with respect to the observer. Establishing such a consistent picture between the source dynamics and the observed data is crucial for characterizing the growing number of massive binary observations and bolstering the robustness of ensuing astrophysical claims.

关键词： Gravitational wave detection Gravitational wave sources Gravitational waves Astronomical black holes Classical black holes Spin Numerical simulations in gravitation & astrophysics

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Defining eccentricity for gravitational wave astronomy

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Physical Review D 2023年第10期108卷 104007-104007页

作者： Md Arif Shaikh Vijay Varma Harald P. Pfeiffer Antoni Ramos-Buades Maarten van de Meent Department of Physics and Astronomy Seoul National University Seoul 08826 Korea International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore 560089 India Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Niels Bohr International Academy Niels Bohr Institute Blegdamsvej 17 2100 Copenhagen Denmark

Eccentric compact binary mergers are significant scientific targets for current and future gravitational wave observatories. To detect and analyze eccentric signals, there is an increasing effort to develop waveform models, numerical relativity simulations, and parameter estimation frameworks for eccentric binaries. Unfortunately, current models and simulations use different internal parametrizations of eccentricity in the absence of a unique natural definition of eccentricity in general relativity, which can result in incompatible eccentricity measurements. In this paper, we adopt a standardized definition of eccentricity and mean anomaly based solely on waveform quantities and make our implementation publicly available through an easy-to-use python package, gw_eccentricity. This definition is free of gauge ambiguities, has the correct Newtonian limit, and can be applied as a postprocessing step when comparing eccentricity measurements from different models. This standardization puts all models and simulations on the same footing and enables direct comparisons between eccentricity estimates from gravitational wave observations and astrophysical predictions. We demonstrate the applicability of this definition and the robustness of our implementation for waveforms of different origins, including post-Newtonian theory, effective-one-body, extreme mass ratio inspirals, and numerical relativity simulations. We focus on binaries without spin precession in this work, but possible generalizations to spin-precessing binaries are discussed.

关键词： Gravitational waves Classical black holes Gravitational wave detection Numerical relativity

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