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Squeezing information from radio surveys to probe the primordial Universe

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arXiv 2024年

作者： Karagiannis, Dionysios Maartens, Roy Saito, Shun Fonseca, José Camera, Stefano Clarkson, Chris Department of Physics & Astronomy University of the Western Cape Cape Town7535 South Africa Institute of Cosmology & Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom National Institute for Theoretical & Computational Sciences Cape Town7535 South Africa Institute for Multi-messenger Astrophysics & Cosmology Department of Physics Missouri University of Science & Technology RollaMO65409 United States Kavli Institute for the Physics & Mathematics of the Universe University of Tokyo Chiba Kashiwa227-8583 Japan Instituto de Astrofísica e Ciências do Espaço Universidade do Porto CAUP Porto4150-762 Portugal Departamento de Física e Astronomia Faculdade de Ciências Universidade do Porto Rua do Campo Alegre 687 PortoPT4169-007 Portugal Dipartimento di Fisica Università degli Studi di Torino Torino10125 Italy INFN – Istituto Nazionale di Fisica Nucleare Sezione di Torino Torino10125 Italy INAF – Istituto Nazionale di Astrofisica Osservatorio Astrofisico di Torino Pino Torinese10025 Italy Department of Physics & Astronomy Queen Mary University of London LondonE1 4NS United Kingdom Department of Mathematics & Applied Mathematics University of Cape Town Cape Town7701 South Africa

A major goal of cosmology is to understand the nature of the field(s) which drove primordial Inflation. Through future observations, the statistics of large-scale structure will allow us to probe primordial non-Gaussianity of the curvature perturbation at the end of Inflation. We show how a new correlation statistic can significantly improve these constraints over conventional methods. Next-generation radio telescope arrays are under construction which will map the density field of neutral hydrogen to high redshifts. These telescopes can operate as an interferometer, able to probe small scales, or as a collection of single dishes, combining signals to map the large scales. We show how to fuse these these operating modes in order to measure the squeezed bispectrum with higher precision and greater economy. This leads to constraints on primordial non-Gaussianity that will improve on measurements by Planck, and out-perform other surveys such as Euclid. We forecast that σ(fNLloc) ∼ 3, achieved by using a small subset, O(102 − 103), of the total number of accessible triangles. The proposed method can be used for any signal that peaks in squeezed configurations. © 2024, CC BY.

关键词： Radio telescopes

Convergence of deep fictitious play for stochastic differential games

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arXiv 2020年

作者： Han, Jiequn Hu, Ruimeng Long, Jihao Department of Mathematics Princeton University PrincetonNJ08544-1000 United States Department of Mathematics Department of Statistics and Applied Probability University of California Santa BarbaraCA93106-3080 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544-1000 United States

Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets. The recently proposed machine learning algorithm, deep fictitious play, provides a novel efficient tool for finding Markovian Nash equilibrium of large N-player asymmetric stochastic differential games [J. Han and R. Hu, Mathematical and Scientific Machine Learning Conference, pages 221-245, PMLR, 2020]. By incorporating the idea of fictitious play, the algorithm decouples the game into N sub-optimization problems, and identifies each player's optimal strategy with the deep backward stochastic differential equation (BSDE) method parallelly and repeatedly. In this paper, we prove the convergence of deep fictitious play (DFP) to the true Nash equilibrium. We can also show that the strategy based on DFP forms an ϵ-Nash equilibrium. We generalize the algorithm by proposing a new approach to decouple the games, and present numerical results of large population games showing the empirical convergence of the algorithm beyond the technical assumptions in the theorems. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

Neural network representation of electronic structure from ab initio molecular dynamics

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arXiv 2020年

作者： Gu, Qiangqiang Zhang, Linfeng Feng, Ji International Center for Quantum Materials School of Physics Peking University Beijing100871 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Collaborative Innovation Center of Quantum Matter Beijing100871 China

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving way to previously inaccessible or challenging avenues in materials modeling. © 2020, CC-BY.

关键词： Electronic structure

Realizable hyperuniform and nonhyperuniform particle configurations with targeted spectral functions via effective pair interactions

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Physical Review E 2020年第3期101卷 032124-032124页

作者： Ge Zhang Salvatore Torquato Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

The capacity to identify realizable many-body configurations associated with targeted functional forms for the pair correlation function g2(r) or its corresponding structure factor S(k) is of great fundamental and practical importance. While there are obvious necessary conditions that a prescribed structure factor at number density ρ must satisfy to be configurationally realizable, sufficient conditions are generally not known due to the infinite degeneracy of configurations with different higher-order correlation functions. A major aim of this paper is to expand our theoretical knowledge of the class of pair correlation functions or structure factors that are realizable by classical disordered ensembles of particle configurations, including exotic “hyperuniform” varieties. We first introduce a theoretical formalism that provides a means to draw classical particle configurations from canonical ensembles with certain pairwise-additive potentials that could correspond to targeted analytical functional forms for the structure factor. This formulation enables us to devise an improved algorithm to construct systematically canonical-ensemble particle configurations with such targeted pair statistics, whenever realizable. As a proof of concept, we test the algorithm by targeting several different structure factors across dimensions that are known to be realizable and one hyperuniform target that is known to be nontrivially unrealizable. Our algorithm succeeds for all realizable targets and appropriately fails for the unrealizable target, demonstrating the accuracy and power of the method to numerically investigate the realizability problem. Subsequently, we also target several families of structure-factor functions that meet the known necessary realizability conditions but are not known to be realizable by disordered hyperuniform point configurations, including d-dimensional Gaussian structure factors, d-dimensional generalizations of the two-dimensional one-component plas

关键词： Classical statistical mechanics

DeePN2: A deep learning-based non-Newtonian hydrodynamic model

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arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

Formation of intermediate-mass planets via magnetically-controlled disk fragmentation

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arXiv 2021年

作者： Deng, Hongping Mayer, Lucio Helled, Ravit Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Wilberforce Rd CambridgeCB3 0WA United Kingdom Center for Theoretical Astrophysics and Cosmology Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland

Intermediate mass planets, from Super-Earth to Neptune-sized bodies, are the most common type of planets in the galaxy 1. The prevailing theory of planet formation, core-accretion 2, predicts significantly fewer intermediate-mass giant planets than observed 3, 4. The competing mechanism for planet formation, disk instability, can produce massive gas giant planets on wide-orbits, such as HR 87995, by direct fragmentation of the protoplanetary disk6. Previously, fragmentation in magnetized protoplanetary disks has only been considered when the magneto-rotational instability is the driving mechanism for magnetic field growth7. Yet, this instability is naturally superseded by the spiral-driven dynamo when more realistic, non-ideal MHD conditions are considered 8, 9. Here we report on MHD simulations of disk fragmentation in the presence of a spiral-driven dynamo. Fragmentation leads to the formation of long-lived bound protoplanets with masses that are at least one order of magnitude smaller than in conventional disk instability models10, 11. These light clumps survive shear and do not grow further due to the shielding effect of the magnetic field, whereby magnetic pressure stifles local inflow of matter. The outcome is a population of gaseous-rich planets with intermediate masses, while gas giants are found to be rarer, in qualitative agreement with the observed mass distribution of exoplanets. © 2021, CC BY.

关键词： Stability

Accurate deep potential model for the Al-Cu-Mg alloy in the full concentration space

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arXiv 2020年

作者： Jiang, Wanrun Zhang, Yuzhi Zhang, Linfeng Wang, Han Songshan Lake Materials Laboratory DongguanGuangdong523808 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Beijing Institute of Big Data Research Beijing100871 China Yuanpei College 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 Huayuan Road 6 Beijing100088 China

Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP), a neural network based representation of the PES, and DP Generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration. Copyright © 2020, The Authors. All rights reserved.

关键词： Copper alloys

Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional

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arXiv 2021年

作者： Piaggi, Pablo Miguel Panagiotopoulos, Athanassios Z. Debenedetti, Pablo G. Car, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye towards studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ice Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic. Copyright © 2021, The Authors. All rights reserved.

关键词： Ice

Hyperuniformity of generalized random organization models

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Physical Review E 2019年第2期99卷 022115-022115页

作者： Zheng Ma Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Studies of random organization models of monodisperse (i.e., identical) spherical particles have shown that a hyperuniform state is achievable when the system goes through an absorbing phase transition to a critical state. Here we investigate to what extent hyperuniformity is preserved when the model is generalized to particles with a size distribution and/or nonspherical shapes. We begin by examining binary disks in two dimensions and demonstrate that their critical states are hyperuniform as two-phase media, but not hyperuniform nor multihyperuniform as point patterns formed by the particle centroids. We further confirm the generality of our findings by studying particles with a continuous size distribution. Finally, to study the effect of rotational degrees of freedom, we extend our model to noncircular particles, namely, hard rectangles with various aspect ratios, including the hard-needle limit. Although these systems exhibit only short-range orientational order, hyperuniformity is still preserved. Our analysis reveals that the redistribution of the “mass” of the particles rather than the particle centroids is central to this dynamical process. The consideration of the “active volume fraction” of generalized random organization models may help to resolve which universality class they belong to and hence may lead to a deeper theoretical understanding of absorbing-state models. Our results suggest that general particle systems subject to random organization can be a robust way to fabricate a wide class of hyperuniform states of matter by tuning the structures via different particle-size and -shape distributions. This in turn potentially enables the creation of multifunctional hyperuniform materials with desirable optical, transport, and mechanical properties.

关键词： Classical statistical mechanics