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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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Surrogate model of hybridized numerical relativity binary black hole waveforms

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Physical Review D 2019年第6期99卷 064045-064045页

作者： Vijay Varma Scott E. Field Mark A. Scheel Jonathan Blackman Lawrence E. Kidder Harald P. Pfeiffer Theoretical Astrophysics California Institute of Technology Pasadena California 91125 USA Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Radiophysics and Space Research Cornell University Ithaca New York 14853 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany

Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms’ length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one-body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at 20 Hz) for stellar mass binaries with total masses as low as 2.25 M⊙. We include the ℓ≤4 and (5, 5) spin-weighted spherical harmonic modes but not the (4, 1) or (4, 0) modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios q≤8, and |χ1z|,|χ2z|≤0.8, where χ1z (χ2z) is the spin of the heavier (lighter) black hole in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches ≲3×10−4 over the mass range 2.25 M⊙≤M≤300 M⊙. At high masses (M≳40 M⊙), where the merger and ringdown are more prominent, we show roughly 2 orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal.

关键词： Gravitational waves Numerical relativity

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Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories

arXiv

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arXiv 2021年

作者： Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Vivanco, FranciscoHernandez Field, Scott E. Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram Thrane, Eric Töyrä, Daniel Vitale, Salvatore School of Physics and Astronomy Monash University VIC3800 Australia OzGrav The ARC Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia Institute for Gravitation and the Cosmos Department of Physics Pennsylvania State University University ParkPA16802 United States Department of Astronomy and Astrophysics Penn State University University ParkPA16802 United States School of Physics and Astronomy Cardiff University CardiffCF24 3AA United Kingdom Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States OzGrav University of Adelaide AdelaideSA5005 Australia OzGrav ANU Centre for Gravitational Astrophysics Research Schools of Physics and Astronomy and Astrophysics The Australian National University ACT2601 Australia LIGO Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for ∼ 90 minute long gravitational-wave signals, covering the observing band (5−2048 Hz), speeding up inference by a factor of ∼ 1.3 × 104 compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravitational effects

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Visual computing for archaeological artifacts with integral invariant filters in 3D

Visual computing for archaeological artifacts with integral ...

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2017 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2017

作者： Mara, H. Krmker, S. Heidelberg University IWR Interdisciplinary Center for Scientific Computing Germany FCGL Forensic Computational Geometry Laboratory VNGG - Visualization and Numerical Geometry Group Mathematikon Neuenheimer Feld 205 Heidelberg69120 Germany

ISBN: (纸本)9783038680376

3D-artifacts from ancient civilizations contain many different kinds of information in form of forensic trace evidence, e.g., tool marks from styli or fingerprints on wax sealings. These very fine structures are increasingly captured by various 3D-acquisiton techniques and stored as irregular meshes. We introduce filter algorithms for the processing of these datasets to finally extract meaningful information at predefined scales. Therefore, Multiscale-Integral Invariants (MSII) are introduced as robust filter methods with their four different variants, using volume, patch, surface and line integrals for their specific sensitivity on mean curvature, Gaussian curvature or noise detection. Smoothing as known from 2D-raster image processing cannot be applied directly. It needs adaptation to the irregular structure of the triangular grids describing 2D-manifolds in *** introduce a fast 1-ring smoothing with a skillful weighting by distance and area of the neighboring points and triangles. Finally, we apply our technique to the various motivating examples for showing the results as false color images with isolines, indicating the respective field of function values, e.g., curvature in various norms or correlations in the feature space. Smooth isolines are indicators for the successful removal of noise. We finally compare the fully automated results with a manual graphic rendering of a faded handwriting found in the tomb of the empress Gisela of Swabia. © 2017 GCH 2017 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Image processing

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Surrogate models for precessing binary black hole simulations with unequal masses

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Physical Review Research 2019年第3期1卷 033015-033015页

作者： Vijay Varma Scott E. Field Mark A. Scheel Jonathan Blackman Davide Gerosa Leo C. Stein Lawrence E. Kidder Harald P. Pfeiffer TAPIR 350-17 California Institute of Technology 1200 East California Boulevard Pasadena California 91125 USA Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA School of Physics and Astronomy and Institute for Gravitational Wave Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Department of Physics and Astronomy University of Mississippi University Mississippi 38677 USA Center for Radiophysics and Space Research Cornell University Ithaca New York 14853 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany

Only numerical relativity simulations can capture the full complexities of binary black hole mergers. These simulations, however, are prohibitively expensive for direct data analysis applications such as parameter estimation. We present two fast and accurate surrogate models for the outputs of these simulations: the first model, NRSur7dq4, predicts the gravitational waveform and the second model, NRSur7dq4Remnant, predicts the properties of the remnant black hole. These models extend previous seven-dimensional, noneccentric precessing models to higher mass ratios and have been trained against 1528 simulations with mass ratios q≤4 and spin magnitudes χ1,χ2≤0.8, with generic spin directions. The waveform model, NRSur7dq4, which begins about 20 orbits before merger, includes all ℓ≤4 spin-weighted spherical harmonic modes, as well as the precession frame dynamics and spin evolution of the black holes. The final black hole model, NRSur7dq4Remnant, models the mass, spin, and recoil kick velocity of the remnant black hole. In their training parameter range, both models are shown to be more accurate than existing models by at least an order of magnitude, with errors comparable to the estimated errors in the numerical relativity simulations. We also show that the surrogate models work well even when extrapolated outside their training parameter space range, up to mass ratios q=6.

关键词： Gravitational waves

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Surrogate models for precessing binary black hole simulations with unequal masses

arXiv

引用

arXiv 2019年

作者： Varma, Vijay Field, Scott E. Scheel, Mark A. Blackman, Jonathan Gerosa, Davide Stein, Leo C. Kidder, Lawrence E. Pfeiffer, Harald P. TAPIR 350-17 California Institute of Technology 1200 E California Boulevard PasadenaCA91125 United States Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States School of Physics and Astronomy Institute for Gravitational Wave Astronomy University of Birmingham BirminghamB15 2TT United Kingdom Department of Physics and Astronomy University of Mississippi UniversityMS38677 United States Center for Radiophysics and Space Research Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Only numerical relativity simulations can capture the full complexities of binary black hole mergers. These simulations, however, are prohibitively expensive for direct data analysis applications such as parameter estimation. We present two new fast and accurate surrogate models for the outputs of these simulations: the first model, NRSur7dq4, predicts the gravitational waveform and the second model, NRSur7dq4Remnant, predicts the properties of the remnant black hole. These models extend previous 7-dimensional, non-eccentric precessing models to higher mass ratios, and have been trained against 1528 simulations with mass ratios q ≤ 4 and spin magnitudes χ1, χ2 ≤ 0.8, with generic spin directions. The waveform model, NRSur7dq4, which begins about 20 orbits before merger, includes all ` ≤ 4 spin-weighted spherical harmonic modes, as well as the precession frame dynamics and spin evolution of the black holes. The final black hole model, NRSur7dq4Remnant, models the mass, spin, and recoil kick velocity of the remnant black hole. In their training parameter range, both models are shown to be more accurate than existing models by at least an order of magnitude, with errors comparable to the estimated errors in the numerical relativity simulations. We also show that the surrogate models work well even when extrapolated outside their training parameter space range, up to mass ratios q = 6. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

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Correction: Completing the view - histologic insights from circular AAA specimen including 3D imaging : A methodologic approach towards histologic analysis of circumferential AAA samples

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Diagnostic pathology 2023年第1期18卷 130页

作者： Anna-Leonie Menges Maja Nackenhorst Johannes R Müller Marie-Luise Engl Renate Hegenloh Jaroslav Pelisek Ellen Geibelt Anja Hofmann Christian Reeps Gabor Biro Hans-Henning Eckstein Alexander Zimmermann Derek Magee Martin Falk Nadja Sachs Albert Busch Department for Vascular Surgery University Hospital Zurich Zurich Switzerland. Department of Pathology Medical University of Vienna Vienna Austria. DFG Cluster of Excellence ?Physics of Life? TU Dresden Dresden Germany. Department for Vascular and Endovascular Surgery Klinikum Rechts der Isar Technical University Munich Munich Germany. Light Microscopy Facility Center for Molecular and Cellular Bioengineering (CMCB) Technische Universit?t Dresden Dresden Germany. Department for Visceral- Thoracic and Vascular Surgery Medical Faculty and University Hospital Carl Gustav Carus TUD Dresden University of Technology Fetscherstrasse 74 Dresden Germany. German Center for Cardiovascular Research (DZHK) Munich Heart Alliance Berlin Germany. HeteroGenius Limited Leeds UK. School of Computing University of Leeds Leeds UK. Scientific Visualization Group Department of Science and Technology (ITN) Linköping University Linköping Sweden. Department for Vascular and Endovascular Surgery Klinikum Rechts der Isar Technical University Munich Munich Germany. albert.busch@uniklinikum-dresden.de. Department for Visceral- Thoracic and Vascular Surgery Medical Faculty and University Hospital Carl Gustav Carus TUD Dresden University of Technology Fetscherstrasse 74 Dresden Germany. albert.busch@uniklinikum-dresden.de.

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Surrogate model of hybridized numerical relativity binary black hole waveforms

arXiv

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arXiv 2018年

作者： Varma, Vijay Field, Scott E. Scheel, Mark A. Blackman, Jonathan Kidder, Lawrence E. Pfeiffer, Harald P. Theoretical Astrophysics California Institute of Technology PasadenaCA91125 United States Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Center for Radiophysics and Space Research Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms’ length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at 20 Hz) for stellar mass binaries with total masses as low as 2.25 M. We include the ` ≤ 4 and (5, 5) spin-weighted spherical harmonic modes but not the (4, 1) or (4, 0) modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios q ≤ 8, and |χ1z|, |χ2z| ≤ 0.8, where χ1z (χ2z) is the spin of the heavier (lighter) BH in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches . 3 ×10−4 over the mass range 2.25M ≤ M ≤ 300M. At high masses (M & 40M), where the merger and ringdown are more prominent, we show roughly two orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal. Copyright © 2018, The Authors. All rights reserved.

关键词： Merging

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Black hole spectroscopy: Systematic errors and ringdown energy estimates

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Physical Review D 2018年第4期97卷 044048-044048页

作者： Vishal Baibhav Emanuele Berti Vitor Cardoso Gaurav Khanna Department of Physics and Astronomy University of Mississippi University Mississippi 38677 USA CENTRA Departamento de Física Instituto Superior Técnico Universidade de Lisboa Avenida Rovisco Pais 1 1049 Lisboa Portugal Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Department of Physics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth North Dartmouth Massachusetts 02747 USA

The relaxation of a distorted black hole to its final state provides important tests of general relativity within the reach of current and upcoming gravitational wave facilities. In black hole perturbation theory, this phase consists of a simple linear superposition of exponentially damped sinusoids (the quasinormal modes) and of a power-law tail. How many quasinormal modes are necessary to describe waveforms with a prescribed precision? What error do we incur by only including quasinormal modes, and not tails? What other systematic effects are present in current state-of-the-art numerical waveforms? These issues, which are basic to testing fundamental physics with distorted black holes, have hardly been addressed in the literature. We use numerical relativity waveforms and accurate evolutions within black hole perturbation theory to provide some answers. We show that (i) a determination of the fundamental l=m=2 quasinormal frequencies and damping times to within 1% or better requires the inclusion of at least the first overtone, and preferably of the first two or three overtones; (ii) a determination of the black hole mass and spin with precision better than 1% requires the inclusion of at least two quasinormal modes for any given angular harmonic mode (ℓ, m). We also improve on previous estimates and fits for the ringdown energy radiated in the various multipoles. These results are important to quantify theoretical (as opposed to instrumental) limits in parameter estimation accuracy and tests of general relativity allowed by ringdown measurements with high signal-to-noise ratio gravitational wave detectors.

关键词： Classical black holes Astronomical black holes

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Evaluating the perception of semi-transparent structures in direct volume rendering techniques 16

Evaluating the perception of semi-transparent structures in ...

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2016 SIGGRAPH ASIA Symposium on visualization, SA 2016

作者： Englund, Rickard Ropinski, Timo Scientific Visualization Group Linköping University Sweden Visual Computing Group Ulm University Germany

ISBN: (纸本)9781450345477

Direct volume rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. A key benefit of DVR is that semi-transparency can be facilitated in order to convey the complexity of the visualized data. Unfortunately, semi-transparency introduces new challenges in spatial comprehension of the visualized data, as the ambiguities inherent to semi-transparent representations affect spatial comprehension. Accordingly, many visualization techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we conduct a user evaluation in which we compare standard DVR with five visualization techniques which have been proposed to enhance the spatial comprehension of DVR images. In our study, we investigate the perceptual performance of these techniques and compare them against each other to find out which technique is most suitable for different types of data and purposes. In order to do this, a large-scale user study was conducted with 300 participants who completed a number of micro-tasks designed such that the aggregated feedback gives us insight on how well these techniques aid the end user to perceive depth and shape of objects. Within this paper we discuss the tested techniques, present the conducted study and analyze the retrieved results. © 2016 Copyright held by the owner/author(s).

关键词： Volume rendering

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