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Dynamical Inference from Polarized Light Curves of Sagittarius A*

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arXiv 2025年

作者： Ricarte, Angelo Conroy, Nicholas S. Wielgus, Maciek Palumbo, Daniel Emami, Razieh Chan, Chi-Kwan Black Hole Initiative at Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

Polarimetric light curves of Sagittarius A∗ (Sgr A∗) sometimes exhibit loops in the Stokes Q and U plane over time, often interpreted as orbiting hotspot motion. In this work, we apply the differential geometry of planar curves to develop a new technique for estimating polarimetric rotation rates. Applying this technique to 230 GHz light curves of Sgr A∗, we find evidence of clockwise motion not only during a post-flare period on 2017 April 11th, as previously discovered, but also during the quiescent days imaged by the Event Horizon Telescope (EHT). The data exhibit a clockwise fraction of 0.67±0.09 and an overall Q − U rotation rate of −3.7 ± 1.2 deg t−g1. We analyze a library of General Relativistic Magnetohydrodynamic (GRMHD) simulations and find that face-on, clockwise-rotating models with strong magnetic fields are most likely to be consistent with the observations. These results are consistent with EHT and GRAVITY Collaboration studies, and indirectly support an interpretation in which the polarized image of Sgr A∗ has been rotated by an external Faraday screen. This technique offers a novel probe of event horizon scale dynamics that complements dynamical reconstructions. © 2025, CC BY.

关键词： Rotation rate

Deep learning inter-atomic potential model for accurate irradiation damage simulationsa)

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arXiv 2019年

作者： Wang, Hao Guo, Xun Zhang, Linfeng Wang, Han Xue, Jianming State Key Laboratory of Nuclear Physics and Technology School of Physics CAPT HEDPS IFSA Collaborative Innovation Center of MoE College of Engineering Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100871 China State Key Laboratory of Nuclear Physics and Technology School of Physics CAPT HEDPS IFSA Collaborative Innovation Center of MoE College of Engineering Peking University Beijing100871 China

We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field. Copyright © 2019, The Authors. All rights reserved.

关键词： Potential energy

Active learning of uniformly accurate inter-atomic potentials for materials simulation

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arXiv 2018年

作者： Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy

Adaptive coupling of a deep neural network potential to a classical force field

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arXiv 2018年

作者： Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science and Beijing International Center for Mathematical Research Peking University China Beijing Institute of Big Data Research Beijing100871 China

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

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Physical Review Letters 2018年第14期120卷 143001-143001页

作者： Linfeng Zhang Jiequn Han Han Wang Roberto Car Weinan E Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing 100871 People’s Republic of China

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

关键词： Classical statistical mechanics Density functional theory First-principles calculations Interatomic & molecular potentials Learning Neuronal networks Potential energy surfaces Solid-state chemistry Thermal properties Van der Waals interaction Molecular dynamics

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models

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arXiv 2019年

作者： Whiteway, Matthew R. Socha, Karolina Bonin, Vincent Butts, Daniel A. Program in Applied Mathematics and Statistics and Scientific Computation University of Maryland College ParkMD United States Neuro-Electronics Research Flanders Leuven Belgium Department of Biology Leuven Brain Institute KU Leuven Leuven Belgium VIB Leuven Belgium Department of Biology and Program in Neuroscience and Cognitive Sciences University of Maryland College ParkMD United States

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the stimulus information contained in those responses. This information can in principle be preserved if variability is shared across many neurons, but depends on the structure of the shared variability and its relationship to sensory encoding at the population level. The structure of this shared variability in neural activity can be characterized by latent variable models, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we introduce two nonlinear latent variable models for analyzing large-scale neural *** first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron's stimulus selectivity and a set of latent variables that modulate these stimulus-driven responses both additively and multiplicatively. While these approaches can detect very general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an "affine model". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions. C

关键词： Neurons

The Nystrom Extension for Signals Defined on a Graph

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The Nystrom Extension for Signals Defined on a Graph

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ayelet Heimowitz Yonina C. Eldar The Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Technion Haifa Israel

ISBN: (纸本)9781538646595

In this paper we introduce a computationally efficient solution to the problem of graph signal interpolation. Our solution is derived using the Nyström extension and is due to the properties of the Markov matrix which we use as our graph shift operator, inspired by diffusion maps. We focus on graph signals that are smooth over the graph. This assumption cements the relationship between the graph and the graph signal. We experimentally verify our suggested framework on the MNIST data set of handwritten digits.

关键词： Markov processes Signal processing Interpolation Laplace equations Symmetric matrices Image edge detection Eigenvalues and eigenfunctions

Automating the Practice of Science – Opportunities, Challenges, and Implications

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Homogenization for Generalized Langevin Equations with Applications to Anomalous Diffusion

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arXiv 2019年

作者： Lim, Soon Hoe Wehr, Jan Lewenstein, Maciej Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-106 91 Sweden Department of Mathematics Program in Applied Mathematics University of Arizona TucsonAZ85721-0089 United States ICFO - Institut de Ciéncies Fotóniques Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels [Barcelona08860 Spain ICREA Pg. Lluis Companys 23 Barcelona08010 Spain

We study homogenization for a class of generalized Langevin equations (GLEs) with state-dependent coefficients and exhibiting multiple time scales. In addition to the small mass limit, we focus on homogenization limits, which involve taking to zero the inertial time scale and, possibly, some of the memory time scales and noise correlation time scales. The latter are meaningful limits for a class of GLEs modeling anomalous diffusion. We find that, in general, the limiting stochastic differential equations (SDEs) for the slow degrees of freedom contain non-trivial drift correction terms and are driven by non-Markov noise processes. These results follow from a general homogenization theorem stated and proven here. We illustrate them using stochastic models of particle diffusion. Copyright © 2019, The Authors. All rights reserved.

关键词： Time measurement