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arXiv 2023年

作者： Zhu, Hengrui Siegel, Harrison Mitman, Keefe Isi, Maximiliano Farr, Will M. Boyle, Michael Deppe, Nils Kidder, Lawrence E. Ma, Sizheng Moxon, Jordan Nelli, Kyle C. Pfeiffer, Harald P. Scheel, Mark A. Teukolsky, Saul A. Throwe, William Varma, Vijay Vu, Nils L. Department of Physics Princeton University Jadwin Hall Washington Road NJ08544 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Department of Physics Columbia University 704 Pupin Hall 538 West 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA91125 United States Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Physics Cornell University IthacaNY14853 United States Perimeter Institute for Theoretical Physics 31 Caroline Streeth North WaterlooONNSL 2Y5 Canada Am Mühlenberg 1 PotsdamD-14476 Germany Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States

The spectroscopic study of black hole quasinormal modes in gravitational-wave ringdown observations is hindered by our ignorance of which modes should dominate astrophysical signals for different binary configurations, limiting tests of general relativity and astrophysics. In this work, we present a description of the quasinormal modes that are excited in the ringdowns of comparable mass, quasi-circular precessing binary black hole coalescences—a key region of parameter space that has yet to be fully explored within the framework of black hole spectroscopy. We suggest that the remnant perturbation for precessing and non-precessing systems is approximately the same up to a rotation, which implies that the relative amplitudes of the quasinormal modes in both systems are also related by a rotation. We present evidence for this by analyzing an extensive catalog of numerical relativity simulations. Additional structure in the amplitudes is connected to the system’s kick velocity and other asymmetries in the orbital dynamics. We find that the ringdowns of precessing systems need not be dominated by the (, m) = (2, ±2) quasinormal modes, and that instead the (2, ±1) or (2, 0) quasinormal modes can dominate. Our results are consistent with a ringdown analysis of the LIGO-Virgo gravitational wave signal GW190521, and may also help in understanding phenomenological inspiral-merger-ringdown waveform model systematics. © 2023, CC BY.

关键词： Relativity

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Searching for asymmetric and heavily precessing Binary Black Holes in the gravitational wave data from the LIGO third Observing Run

arXiv

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arXiv 2024年

作者： Schmidt, Stefano Caudill, Sarah Creighton, Jolien D.E. Tsukada, Leo Doke, Anushka Jain, Manvi Ray, Anarya Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Cousins, Bryce Ewing, Becca Fong, Heather George, Richard N. Godwin, Patrick Hanna, Chad Harada, Reiko Huang, Yun-Jing Huxford, Rachael Joshi, Prathamesh Kennington, James Kuwahara, Soichiro Li, Alvin K.Y. Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Niu, Wanting Pace, Alex Posnansky, Cort Sachdev, Surabhi Sakon, Shio Singh, Divya Shah, Urja Tapia, Ron Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Nikhef Science Park 105 Amsterdam1098 XG Netherlands Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru560089 India RESCEU The University of Tokyo Tokyo113-0033 Japan Department of Physics University of Illinois UrbanaIL61801 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology MS 100-36 PasadenaCA91125 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United S

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 non-optimal 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+0−0.3304 Gpc−3yr−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. © 2024, CC BY.

关键词： Matched filters

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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

arXiv

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

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Reflection-asymmetric wormholes and their double shadows

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Physical Review D 2020年第8期102卷 084044-084044页

作者： Maciek Wielgus Jiří Horák Frederic Vincent Marek Abramowicz Nicolaus Copernicus Astronomical Centre Polish Academy of Sciences Bartycka 18 00-716 Warsaw Poland Research Center for Computational Physics and Data Processing Institute of Physics Silesian University in Opava Bezručovo náměstí 1150/13746 01 Opava Czech Republic and Department of Physics Göteborg University 412-96 Göteborg Sweden

We discuss construction and observational properties of wormholes obtained by connecting two Reissner-Nordström spacetimes with distinct mass and charge parameters. These objects are spherically symmetric, but not reflection symmetric, as the connected spacetimes differ. The reflection-asymmetric wormholes may reflect a significant fraction of the infalling radiation back to the spacetime of its origin. We interpret this effect in a simple framework of the effective photon potential. Depending on the model parameters, image of such a wormhole seen by a distant observer (its “shadow”) may contain a photon ring formed on the observer’s side, photon ring formed on the other side of the wormhole, or both photon rings. These unique topological features would allow us to firmly distinguish this class of objects from Kerr black holes using radioastronomical observations.

关键词： Classical black holes General relativity General relativity equations & solutions Radio, microwave, & sub-mm astronomy

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Evidence of large recoil velocity from a black hole merger signal

arXiv

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arXiv 2022年

作者： Varma, Vijay Biscoveanu, Sylvia Islam, Tousif Shaik, Feroz H. Haster, Carl-Johan Isi, Maximiliano Farr, Will M. Field, Scott E. Vitale, Salvatore Am Mühlenberg 1 Potsdam14476 Germany 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 Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Physics and Astronomy Stony Brook University Stony Brook NY11794 United States

The final black hole left behind after a binary black hole merger can attain a recoil velocity, or a "kick", reaching values up to 5000 km/s. This phenomenon has important implications for gravitational wave astronomy, black hole formation scenarios, testing general relativity, and galaxy evolution. We consider the gravitational wave signal from the binary black hole merger GW200129_065458 (henceforth referred to as GW200129), which has been shown to exhibit strong evidence of orbital precession. Using numerical relativity surrogate models, we constrain the kick velocity of GW200129 to vf (Equation presented) km/s or vf 698 km/s (one-sided limit), at 90% credibility. This marks the first identification of a large kick velocity for an individual gravitational wave event. Given the kick velocity of GW200129, we estimate that there is a less than 0.48% (7.7%) probability that the remnant black hole after the merger would be retained by globular (nuclear star) clusters. Finally, we show that kick effects are not expected to cause biases in ringdown tests of general relativity for this event, although this may change in the future with improved detectors. © 2022, CC BY.

关键词： Merging

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Detection of Hepatocellular Carcinoma Using Machine Learning: A Case for the Filipino Cohort

SSRN

引用

SSRN 2023年

作者： Manzanillo, Mojhune Gabriel Tripon, Edhel Junio, Dannah Zemirah Jamindang, Marc Daniel Nuique, Ramonito Roxas-Villanueva, Ranzivelle Marianne Tiangco, Beatrice Albia, Jason Data-Driven Research Laboratory Institute of Mathematical Sciences and Physics University of the Philippines Los Baños Laguna Los Baños4030 Philippines Computational Interdisciplinary Research Laboratory University of the Philippines Los Baños Laguna Los Baños4030 Philippines Domingo AI Research Center Pasig City Metro Manila1606 Philippines Center for Liver Health and Transplantation The Medical City Ortigas Pasig City Metro Manila1600 Philippines Augusto P. Sarmiento Cancer Institute The Medical City Ortigas Pasig City Metro Manila1600 Philippines Cancer CARE Registry and Research Philippines Foundation Inc. Pasig City Metro Manila1600 Philippines National Institute of Health University of the Philippines Manila Manila City Metro Manila1000 Philippines Graduate School University of the Philippines Los Baños Laguna Los Baños4030 Philippines

Hepatocellular Carcinoma (HCC) is a global health concern, ranking sixth in prevalence and third in cancer-related mortality. The lack of cost-effective diagnostic tests poses a challenge in improving clinical prognosis and overall patient survival. Baseline clinical data from a cohort of 731 Filipino patients were used to develop HCC detection models. Three machine learning algorithms, logistic regression (LR), random forest (RF), and gradient boosting (GR), were tested and optimized to evaluate their detection capability by adopting a traditional train-validation strategy. The sensitivity (Sn) and specificity (Sp) [Sn%: Sp%] of LR, RF, and GR are [93.33, 92.42], [93.33, 92.42], [93.33, 90.31], respectively. Furthermore, logistic regression demonstrated the highest detection capability, with an area under the receiver operating characteristic curve (AUROC) of 0.97. Age, albumin, alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and des-gamma-carboxy prothrombin (DCP) were also identified as significant predictors in detecting HCC. In addition, we illustrate that logistic regression has a robust capability to detect HCC irrespective of the clinical risk factors. To a large extent, this study demonstrates the capability of machine learning-based detection models as an alternative and cheaper route for HCC detection and screening in resource-limited and remote settings. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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High precision calculation of the hadronic vacuum polarisation contribution to the muon anomaly

arXiv

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arXiv 2024年

作者： Boccaletti, A. Borsanyi, Sz. Davier, M. Fodor, Z. Frech, F. Gérardin, A. Giusti, D. Kotov, A.Yu. Lellouch, L. Lippert, Th. Lupo, A. Malaescu, B. Mutzel, S. Portelli, A. Risch, A. Sjö, M. Stokes, F. Szabo, K.K. Toth, B.C. Wang, G. Zhang, Z. Department of Physics University of Wuppertal WuppertalD-42119 Germany Jülich Supercomputing Centre Forschungszentrum Jülich JülichD-52428 Germany IJCLab Université Paris-Saclay CNRS IN2P3 Orsay91405 France Physics Department Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences Pennsylvania State University University ParkPA16802 United States Institute for Theoretical Physics Eötvös University BudapestH-1117 Hungary University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Aix Marseille Univ Université de Toulon CNRS CPT IPhU Marseille France Fakultät für Physik Universität Regensburg Regensburg93040 Germany LPNHE Sorbonne Université Université Paris Cité CNRS IN2P3 Paris75252 France Laboratoire de Physique de l'Ecole Normale Supérieure Mines Paris - PSL CNRS Inria PSL Research University Paris France School of Physics and Astronomy University of Edinburgh EdinburghEH9 3JZ United Kingdom RIKEN Center for Computational Science Kobe650-0047 Japan Special Research Centre for the Subatomic Structure of Matter Department of Physics University of Adelaide SA5005 Australia

For fifty years, the standard model of particle physics has been hugely successful in describing subatomic phenomena. Recently, this statement appeared to be contradicted by the strong disagreement between measurements of the anomalous magnetic moment of the muon [1], aµ, and the reference standard-model prediction [2]. Such a large discrepancy should signal the discovery of interactions or particles not present in the standard model. However, two independent determinations [3, 4] of the most uncertain contribution to the standard-model prediction display significantly less tension with the measurement. Here we present a new first-principle calculation of this contribution. We reduce uncertainties compared to our earlier computation [3] by 40%. To reach this unprecedented level of precision we improve on many aspects of the calculation. Namely, we perform large-scale lattice QCD simulations on finer lattices than in Ref. [3], allowing for an even more accurate continuum extrapolation. We also include a small, long-distance contribution obtained using input from experiments in a low-energy regime where they all agree. Combined with the extensive calculations of other standard model contributions summarised in Ref. [2], our result leads to a prediction that differs from the measurement of aµ by only 0.9 standard deviations. This provides a remarkable validation of the standard model to 0.37ppm. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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On the design space between molecular mechanics and machine learning force fields

arXiv

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arXiv 2024年

作者： Wang, Yuanqing Takaba, Kenichiro Chen, Michael S. Wieder, Marcus Xu, Yuzhi Zhu, Tong Zhang, John Z.H. Nagle, Arnav Yu, Kuang Wang, Xinyan Cole, Daniel J. Rackers, Joshua A. Cho, Kyunghyun Greener, Joe G. Eastman, Peter Martiniani, Stefano Tuckerman, Mark E. Simons Center for Computational Physical Chemistry Department of Chemistry New York University New YorkNY10003 United States Center for Data Science New York University New YorkNY10004 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Asahi Kasei Pharma Corporation 632-1 Mifuku Shizuoka Izunokuni410-2321 Japan Open Molecular Software Foundation DavisCA95616 United States NYU-ECNU Center for Computational Chemistry Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU Shanghai Shanghai200062 China Faculty of Synthetic Biology Shenzhen University of Advanced Technology Shenzhen518055 China University of Washington SeattleWA98195 United States Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua Univ. Shenzhen518055 China DP Technology Beijing100089 China School of Natural and Environmental Sciences Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Prescient Design Genentech New YorkNY10004 United States Medical Research Council Laboratory of Molecular Biology CambridgeCB2 0QH United Kingdom Department of Chemistry Stanford University StanfordCA94305 United States Center for Soft Matter Research Department of Physics New York University New YorkNY10003 United States

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed (as well as stability and generalizability), as many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of 1 kcal/mol—the empirical threshold beyond which realistic chemical predictions are possible—though still magnitudes slower than MM. Hoping to kindle explorations and designs of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the design space (the speed-accuracy tradeoff) between MM and ML force fields. After a brief review of the building blocks of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, and envision what the next generation of MLFF might look like. © 2024, CC BY.

关键词： Molecular mechanics

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the 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 Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines 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 the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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