Hasil untuk "physics.comp-ph"

R. Laureijs, J. Amiaux, S. Arduini et al.

Euclid is a space-based survey mission from the European Space Agency designed to understand the origin of the Universe's accelerating expansion. It will use cosmological probes to investigate the nature of dark energy, dark matter and gravity by tracking their observational signatures on the geometry of the universe and on the cosmic history of structure formation. The mission is optimised for two independent primary cosmological probes: Weak gravitational Lensing (WL) and Baryonic Acoustic Oscillations (BAO). The Euclid payload consists of a 1.2 m Korsch telescope designed to provide a large field of view. It carries two instruments with a common field-of-view of ~0.54 deg2: the visual imager (VIS) and the near infrared instrument (NISP) which contains a slitless spectrometer and a three bands photometer. The Euclid wide survey will cover 15,000 deg2 of the extragalactic sky and is complemented by two 20 deg2 deep fields. For WL, Euclid measures the shapes of 30-40 resolved galaxies per arcmin2 in one broad visible R+I+Z band (550-920 nm). The photometric redshifts for these galaxies reach a precision of dz/(1+z) < 0.05. They are derived from three additional Euclid NIR bands (Y, J, H in the range 0.92-2.0 micron), complemented by ground based photometry in visible bands derived from public data or through engaged collaborations. The BAO are determined from a spectroscopic survey with a redshift accuracy dz/(1+z) =0.001. The slitless spectrometer, with spectral resolution ~250, predominantly detects Ha emission line galaxies. Euclid is a Medium Class mission of the ESA Cosmic Vision 2015-2025 programme, with a foreseen launch date in 2019. This report (also known as the Euclid Red Book) describes the outcome of the Phase A study.

Arman Babakhani, Lev Barash, Itay Hen

We present a universal quantum Monte Carlo algorithm for simulating arbitrary high-spin (spin greater than 1/2) Hamiltonians, based on the recently developed permutation matrix representation (PMR) framework. Our approach extends a previously developed PMR-QMC method for spin-1/2 Hamiltonians [Phys. Rev. Research 6, 013281 (2024)]. Because it does not rely on a local bond decomposition, the method applies equally well to models with arbitrary connectivities, long-range and multi-spin interactions, and its closed-walk formulation allows a natural analysis of sign-problem conditions in terms of cycle weights. To demonstrate its applicability and versatility, we apply our method to spin-1 and spin-3/2 quantum Heisenberg models on the square lattice, as well as to randomly generated high-spin Hamiltonians. Additionally, we show how the approach naturally extends to general Hamiltonians involving mixtures of particle species, including bosons and fermions. We have made our program code freely accessible on GitHub.

Aviral Prakash, Yongjie Jessica Zhang

Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where access to the source of the underlying full order model (FOM) is unavailable; that is, FOM is a black-box. This article proposes a learn-then-project approach for nonintrusive model reduction. In the first step of this approach, high-dimensional stable sparse learned differential operators (S-LDOs) are determined using the generated data. In the second step, the ordinary differential equations, comprising these S-LDOs, are used with suitable dimensionality reduction and low-dimensional subspace projection methods to provide equations for the evolution of reduced states. This approach allows easy integration into the existing intrusive ROM framework to enable nonintrusive model reduction while allowing the use of Petrov-Galerkin projections. The applicability of the proposed approach is demonstrated for Galerkin and LSPG projection-based ROMs through three numerical experiments: 1-D advection equation, 1-D Burgers equation and 2-D advection equation. The results indicate that the proposed nonintrusive ROM strategy provides accurate and stable dynamics prediction.

Tomás Fernández Bouvier, Ville Jantunen, Saana Vihuri et al.

Quantum information technologies hold immense promise, with quantum computers poised to revolutionize problem-solving capabilities. Among the leading contenders are solid-state spin-qubits, particularly those utilizing the spin of phosphorous donors (31 P ). While significant progress has been made in enhancing quantum coherence and qubit control, challenges persist, notably in achieving precise and scalable P placement in Si substrate. This paper investigates by means of molecular dynamics the use of molecular PF2 ions for implantation, aiming to reduce placement uncertainty while maintaining detection efficiency. We examine energy transfer, molecule integrity, implantation profiles, electronic signal components, and stable damage. Among other things we find that the assumption that the molecule only breaks apart immediately due to the presence of an a-SiO2 layer on the surface of the crystal and that the intensity of the electronic signal from ion-solid interactions does not correlate necessarily with the penetration depth of P.

Thomas Blommel, David J. Gardner, Carol S. Woodward et al.

The non-equilibrium Green's function gives access to one-body observables for quantum systems. Of particular interest are quantities such as density, currents, and absorption spectra which are important for interpreting experimental results in quantum transport and spectroscopy. We present an integration scheme for the Green's function's equations of motion, the Kadanoff-Baym equations (KBE), which is both adaptive in the time integrator step size and method order as well as the history integration order. We analyze the importance of solving the KBE self-consistently and show that adapting the order of history integral evaluation is important for obtaining accurate results. To examine the efficiency of our method, we compare runtimes to a state of the art fixed time step integrator for several test systems and show an order of magnitude speedup at similar levels of accuracy.

Ivan Novikau, Ilon Joseph

We propose an explicit algorithm based on the Linear Combination of Hamiltonian Simulations technique to simulate both the advection-diffusion equation and a nonunitary discretized version of the Koopman-von Neumann formulation of nonlinear dynamics. By including dissipation into the model, through an upwind discretization of the advection operator, we avoid spurious parasitic oscillations which usually accompany standard finite difference discretizations of the advection equation. In contrast to prior works on quantum simulation of nonlinear problems, we explain in detail how different components of the algorithm can be implemented by using the Quantum Signal Processing (QSP) and Quantum Singular Value Transformation (QSVT) methods. In addition, we discuss the general method for implementing the block-encoding (BE) required for QSP and QSVT circuits and provide explicit implementations of the BE oracles tailored to our specific test cases. We simulate the resulting circuit on a digital emulator of quantum fault-tolerant computers and investigate its complexity and success probability. The proposed algorithm is universal and can be used for modeling a broad class of linear and nonlinear differential equations including the KvN and Carleman embeddings of nonlinear systems, the semiclassical Koopman-van Hove (KvH) equation, as well as the advection and Liouville equations.

Motohisa Fukuda

We are upgrading the Python-version of RTNI, which symbolically integrates tensor networks over the Haar-distributed unitary matrices. Now, PyRTNI2 can treat the Haar-distributed orthogonal matrices and the real and complex normal Gaussian tensors as well. Moreover, it can export tensor networks in the format of TensorNetwork so that one can make further calculations with concrete tensors, even for low dimensions, where the Weingarten functions differ from the ones for high dimensions. The tutorial notebooks are found at GitHub: https://github.com/MotohisaFukuda/PyRTNI2. In this paper, we explain maths behind the program and show what kind of tensor network calculations can be made with it. For the former, we interpret the element-wise moment calculus of the above random matrices and tensors in terms of tensor network diagrams, and argue that the view is natural, relating delta functions in the calculus to edges in tensor network diagrams.

Minglei Yang, Diego del-Castillo-Negrete, Guannan Zhang et al.

An interpolation method to evaluate magnetic fields given unstructured, scattered magnetic data is presented. The method is based on the reconstruction of the global magnetic field using a superposition of orthogonal functions. The coefficients of the expansion are obtained by minimizing a cost function defined as the L^2 norm of the difference between the ground truth and the reconstructed magnetic field evaluated on the training data. The divergence-free condition is incorporated as a constrain in the cost function allowing the method to achieve arbitrarily small errors in the magnetic field divergence. An exponential decay of the approximation error is observed and compared with the less favorable algebraic decay of local splines. Compared to local methods involving computationally expensive search algorithms, the proposed method exhibits a significant reduction of the computational complexity of the field evaluation, while maintaining a small error in the divergence even in the presence of magnetic islands and stochasticity. Applications to the computation of Poincaré sections using data obtained from numerical solutions of the magnetohydrodynamic equations in toroidal geometry are presented and compared with local methods currently in use.

Shurui Li, Jianqin Xu, Jing Qian et al.

Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences. In the present work, we propose a novel learning method based on a hybrid network integrating two different kinds of neural networks: Long Short-Term Memory(LSTM) and Deep Residual Network(ResNet), in order to overcome the difficulty met in numerically simulating strongly-oscillating dynamical evolutions of physical systems. By taking the dynamics of Bose-Einstein condensates in a double-well potential as an example, we show that our new method makes a high efficient pre-learning and a high-fidelity prediction about the whole dynamics. This benefits from the advantage of the combination of the LSTM and the ResNet and is impossibly achieved by a single network in the case of direct learning. Our method can be applied for simulating complex cooperative dynamics in a system with fast multiple-frequency oscillations with the aid of auxiliary spectrum analysis.

Marta S. Fernández, P. Fromherz

Yifei Shi, Jessica Karaguesian, Rustam Z. Khaliullin

Locality of compact one-electron orbitals expanded strictly in terms of local subsets of basis functions can be exploited in density functional theory (DFT) to achieve linear growth of computation time with systems size, crucial in large-scale simulations. However, despite advantages of compact orbitals the development of practical orbital-based linear-scaling DFT methods has long been hindered because a compact representation of the electronic ground state is difficult to find in a variational optimization procedure. In this work, we show that the slow and unstable optimization of compact orbitals originates from the nearly-invariant mixing of compact orbitals that are mostly but not completely localized within the same subsets of basis functions. We also construct an approximate Hessian that can be used to identify the problematic nearly-invariant modes and obviate the variational optimization along them without introducing significant errors into the computed energies. This enables us to create a linear-scaling DFT method with a low computational overhead that is demonstrated to be efficient and accurate in fixed-nuclei calculations and molecular dynamics simulations of semiconductors and insulators.

Alan A. S. Amad, Antonio A. Novotny, Bojan B. Guzina

In this work, we propose a full-waveform technique for the spatial reconstruction and characterization of (micro-) seismic events via joint source location and moment tensor inversion. The approach is formulated in the frequency domain, and it allows for the simultaneous inversion of multiple point-like events. In the core of the proposed methodology is a grid search for the source locations that encapsulates the optimality condition on the respective moment tensors. The developments cater for compactly supported elastic bodies in $\mathbb{R}^2$; however our framework is directly extendable to inverse (seismic) source problems in $\mathbb{R}^3$ involving both bounded and unbounded elastic domains. A set of numerical results, targeting laboratory applications, is included to illustrate the performance of the inverse solution in situations involving: (i) reconstruction of multiple events, (ii) sparse (pointwise) boundary measurements, (iii) "off-grid" location of the micro-seismic events, and (iv) inexact knowledge of the medium's elastic properties.

J. L. Vay

We present the standard electromagnetic Particle-in-Cell method, starting from the discrete approximation of derivatives on a uniform grid. The application to second-order, centered, finite-difference discretization of the equations of motion and of Maxwells equations is then described in one dimension, followed by two and three dimensions. Various algorithms are presented, for which we discuss the stability and accuracy, introducing and elucidating concepts like numerical stochastic heating, CFL limit and numerical dispersion. The coupling of the particles and field quantities via interpolation at various orders is detailed, together with its implication on energy and momentum conserving. Special topics of relevance to the modeling of plasma accelerators are discussed, such as moving window, optimal Lorentz boosted frame, the numerical Cherenkov instability and its mitigation. Examples of simulations of laser-driven and particle beam-driven accelerators are given, including with mesh refinement. We conclude with a discussion on high-performance computing and a brief outlook.

Francesco Camana, Massimiliano Gori, Luca De Rosa et al.

The reconstruction of the area of origin of spatter patterns is usually a fundamental step to the determination of the area of the crime scene where the victim was wounded. In this field, for almost a decade, the italian Polizia di Stato has employed AnTraGoS, a forensic software which implements a probabilistic approach to identify the area where the horizontal projections of the trajectories of a set of blood drops converge (area of convergence) and to estimate the height of origin. In this paper we summarize a series of tests performed on a published dataset of spatter patterns, whose results confirm the validity of AnTraGoS and of its algorithms. As a side result, some useful suggestions are derived, concerning the determination of the height of origin, within a statistical and fluid dynamic approach.

Susi Lehtola

Knowledge of the repulsive behavior of potential energy curves $V(R)$ at $R\to0$ is necessary for understanding and modeling irradiation processes of practical interest. $V(R)$ is in principle straightforward to obtain from electronic structure calculations; however, commonly-used numerical approaches for electronic structure calculations break down in the strongly repulsive region due to the closeness of the nuclei. In the present work, we show by comparison to fully numerical reference values that a recently developed procedure [S. Lehtola, J. Chem. Phys. 151, 241102 (2019)] can be employed to enable accurate linear combination of atomic orbitals calculations of $V(R)$ even at small $R$ by a study of the seven nuclear reactions He2 <=> Be, HeNe <=> Mg, Ne2 <=> Ca, HeAr <=> Ca, MgAr <=> Zn, Ar2 <=> Kr, and NeCa <=> Zn.

Michal Pavelka, Vaclav Klika, Miroslav Grmela

Imagine a freely rotating rigid body. The body has three principal axes of rotation. It follows from mathematical analysis of the evolution equations that pure rotations around the major and minor axes are stable while rotation around the middle axis is unstable. However, only rotation around the major axis (with highest moment of inertia) is stable in physical reality (as demonstrated by the unexpected change of rotation of the Explorer 1 probe). We propose a general method of Ehrenfest regularization of Hamiltonian equations by which the reversible Hamiltonian equations are equipped with irreversible terms constructed from the Hamiltonian dynamics itself. The method is demonstrated on harmonic oscillator, rigid body motion (solving the problem of stable minor axis rotation), ideal fluid mechanics and kinetic theory. In particular, the regularization can be seen as a birth of irreversibility and dissipation. In addition, we discuss and propose discretizations of the Ehrenfest regularized evolution equations such that key model characteristics (behavior of energy and entropy) are valid in the numerical scheme as well.

Yunfeng Xiong, Sihong Shao

To implement the Wigner branching random walk, the particle carrying a signed weight, either $-1$ or $+1$, is more friendly to data storage and arithmetic manipulations than that taking a real-valued weight continuously from $-1$ to $+1$. The former is called a signed particle and the latter a weighted particle. In this paper, we propose two efficient strategies to realize the signed-particle implementation. One is to interpret the multiplicative functional as the probability to generate pairs of particles instead of the incremental weight, and the other is to utilize a bootstrap filter to adjust the skewness of particle weights. Performance evaluations on the Gaussian barrier scattering (2D) and a Helium-like system (4D) demonstrate the feasibility of both strategies and the variance reduction property of the second approach. We provide an improvement of the first signed-particle implementation that partially alleviates the restriction on the time step and perform a thorough theoretical and numerical comparison among all the existing signed-particle implementations. Details on implementing the importance sampling according to the quasi-probability density and an efficient resampling or particle reduction are also provided.

R. Thomas

D. Piper, B. Fenton

K. Eaton, S. Krakowka