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29 results for “Spin models”
Experimental data and scripts used for the paper "Experiments and low-order modelling of intermittent transitions between clockwise and anticlockwise spinning thermoacoustic modes in annular combustors"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p> <p>Because of difficulties for uploading large files on zenodo, the heaviest files, which are the acoustic measurement files (.TDMS format), are not included in the zip file, but are put aside of it.</p> <p>For the scripts to work correctly, all the tdms files should be moved in the folder Faure-BeaulieuA_StochasticTransitionsAzimuthalMode_PROCI_20200713/01_input_data/</p>
Dataset for "Quantum spin models for numerosity perception"
<p>Data to recreate the figures 2-3, 6-9 of the manuscript "Quantum spin models for numerosity perception". For figure 5, we provide all of the numerically simulated data for the time evolution of our system whose average, spectrum and analysis is presented in the figure.</p> <p>The data is structured in individual folders for every figure and we provide a README file for each folder.</p>
Example input files for Wolf-Rayet star models in paper "Tidal Spin-up of Black Hole Progenitor Stars"
<p>The files here show example input files for the models in the paper "Tidal Spin-up of Black Hole Progenitor Stars" by Ma & Fuller (2023).</p> <p>File "inlist_MS" and "inlist_WR" show MESA inlists to set up a Wolf-Rayet star model of 10 solar-masses (model 3 in Ma & Fuller 2023, Table 1).<br> We used MESA version r12778 for our calculation.<br> Specifically, "inlist_MS" starts a stellar model at zero-age main-sequence (ZAMS), and evolves it to the end of core hydrogen depletion.<br> After resuming the model from a photo file, "inlist_WR" turns on artificial mass-loss ("relax_mass" and "new_mass") to remove its hydrogen envelope, and then evolves the model throughout the helium burning Wolf-Rayet phase, until the end of core helium depletion.<br> During this phase, pulsation data are also created by MESA ("write_pulse_data_with_profile = .true.").</p> <p><br> File "gyre.in" is an example GYRE input file to solve for oscillation modes in the established Wolf-Rayet star models.<br> We used GYRE version 6.0.1 for our calculation.</p>
Data from: Emergent electrostatics in planar XY spin models: The bridge connecting topological order with broken U(1) symmetry
Open the record for dataset details and reuse information.
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Exact Spin-Boson-Model Tunneling Dynamics with Time Dependent Variation Matrix Product States (TDVMPS). Barrier height and temperature parameter space
<p>Spin-Boson tunnelling data acquired using the T-TEDOA method for Time-Dependent-Variational-Matrix-Product-States (TDVMPS) accompanying the paper <a href="https://doi.org/10.3389/fchem.2020.600731">https://doi.org/10.3389/fchem.2020.600731</a>.</p> <p> </p>
Spin-up time and internal variability analysis for overlapping time slices in a regional climate model
<p>In order to increase computational efficiency, several long-term regional climate simulations were split into overlappings time slices. These overlappings slices were used to explore the relative role of spin-up time and internal variability in the discontinuities that are produced once the slices are joined.</p> <p>This dataset was generated using the Weather Research and Forecasting (WRF) model and includes two sets of time slices for the periods 2002-2006 and 2006-2010 over the CORDEX South American domain at 0.44º horizontal resolution (SAM-44), regular on a rotated latitude-longitude projection. The data was forced by the scenario RCP 8.5 and driven by the Canadian Earth System model (CanESM2). The model configuration files are also attached.</p>
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.
Supplementary material to 'Exotic Symmetry Breaking Properties of Self-Dual Fracton Spin Models'
<p>I. GENERAL INFORMATION</p> <p>1. Title<br>Dataset of "Degeneracy and Scaling Properties of Self-Dual Fracton Spin Models"</p> <p>2. Author Information<br> <br>Giovanni Canossa [1,2], Lode Pollet [1,2], Miguel A. Martin-Delgado [3,4], Hao Song [5], and Ke Liu [1,2,6,7]<br>1. Arnold Sommerfeld Center for Theoretical Physics, University of Munich</p> <p>2. Munich Center for Quantum Science and Technology (MCQST)</p> <p>3. Departamento de Física Teórica, Universidad Complutense, 28040 Madrid, Spain</p> <p>4. CCS-Center for Computational Simulation, Universidad Politécnica de Madrid, Spain</p> <p>5. CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China</p> <p>6. Hefei National Research Center for Physical Sciences at the Microscale, University of technology of China</p> <p>7. Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China</p> <p>Links to publications that cite or use the data:<br>TBA</p> <p>II. Files</p> <p>1. Convention</p> <p>Datas from the multicanonical MC simulations are stored in "Tetra-Ising" and "Fractal-Ising" folders.</p> <p>Lattice size: Each subfolder is named "L=value" where value denotes the linear system size.</p> <p>Multicanonical weights: Files "g_init_T=value.data" contains the set of log(weights) at a given temperature and lattice size, derived from the iterative weight-learning procedure.</p> <p>Datas: HDF5 files "name.out.h5" contain the results of the multicanonical MC simulation at a given lattice size.</p> <p><br>III. Data in HDF5 file</p> <p>1. Convention</p> <p>The results obtained at each temperature point is stored in a separate subdirectory of the .out.h5 file. Each of these subdirectories contains:</p> <p>Energy_Hist: normalized energy histograms obtained from the multicanonical MC simulation (unweighted)</p> <p>Energy_Hist_rw: normalized reweighted energy histograms. For each bin, Energy_Hist_rw[i] = Energy_Hist[i] * e**g[i] / norm, where norm = sum( Energy_Hist[i] * e**g[i] ).</p> <p>c: 1/norm. Gives an estimate of the ratio Z_muca/Z_ca.</p> <p>g: vector containing the weights used for the multicanonical MC simulation at that specific temperature. These are derived from reweighting the weights in "g_init_T=value.data" file.</p> <p>Energy, Energy_Susc, Energy_Kurt Q_x, Q_x_Susc, Q_x_Kurt: canonical expectation value of each relevant observables, along with their susceptibilities and Kurtosis, obtained by reweighting each measurement taken during the simulation by the appropriate weight. (NB: Energy and Q_x need to be multiplied by C in order to give the correct canonical expectation value.)</p> <p><br>2. Relevance</p> <p>These data reproduce Figs. 5 & 6 in the manuscript.</p> <p><br>III. Finite size scaling</p> <p>1. Convention</p> <p>All estimated transition temperatures with their respective uncertainties are stored in "fitting_Tetra" and "fitting_Fractal" folders in the fittemps.txt file.</p> <p>2. Relevance</p> <p>These data reproduce Figs. 3 & 4 in the manuscript.<br> </p> <p> </p>
Spin and orbital textures of KTaO3 (001) two-dimensional electron gases from a tight-binding model
<p>The dataset contains the iso-energy lines with the expectation values of spin and orbital moments of KTaO3 (001) two-dimensional electron gases for selected energies. The data was generated using a tight-binding model.</p>
Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
<p>Aggregated numerical data and analysis routines required to reproduce the figures in "Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model"</p>
Dataset for Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs
<p>Dataset for the paper titled Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs</p> <p>Pre-print located at https://arxiv.org/abs/2312.00997</p> <p>See README.md for complete data description</p> <p>LA-UR-24-31392</p>
Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"
<p>Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.</p>
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
Open the record for dataset details and reuse information.
Data set for "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit"
<p>This data set is for the paper "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit". It contains the raw DCA HD5 and DQMC plain text output files, as well as the scripts and final processed data used to generate figures 1-5 of the main text and supplementary figures 1-12. Copies of the figures and latex files are also included for completeness. </p>
UFO model for Spin-2 simplified model
<p>The model contains a spin-2 field and its interactions with the SM fields through dimension-5 operators. The UFO model can be used for computation at the NLO in QCD.</p>
Engineering random spin models with atoms in a high-finesse cavity
<p>Data corresponding to the article "Engineering random spin models with atoms in a high-finesse cavity".</p>
Extended Hubbard Model with spin-valley polarization DMRG solution
<p>The data set for containing observables obtained by means of Density Matrix Renormalization Group for the Extended Hubbard model at filling n=2/3 and with spin-valley polarization included in the single-particle term. For all the considered case the on-site Hubbard repulsion U is set to 15|t| and |t| = 1. The phase for hopping termi is 2*pi/3.</p> <p>Folders naming convention is following:</p> <p>VXYZ</p> <p>V stands for intersite interaction and XYZ is its magnitude.</p> <p> </p> <p>In each folder there are following files:</p> <p>- n_single</p> <p>columns: i j Re_up Im_up Re_dn Im_dn</p> <p>Where: i,j are site indicies Re(Im)_up(dn) stand for the real(imaginary) part of single particle equal-time Green function for up(dn) spin</p> <p>- nn</p> <p>columns: i j Re_up Im_up Re_dn Im_dn Re_(up+dn) Im_(up+dn)</p> <p>Where: i,j are site indicies Re(Im)_up(dn) stand for the real(imaginary) part of two particle equal-time Green function <n_i,up(dn) n_j,up(dn)> for up(dn) spin and two last columns correspond to <(n_i,up+n_i,dn)(n_j,up+n_j,dn)></p> <p>- ss</p> <p>columns: i j Re Im</p> <p>Where: i,j are site indicies Re(Im) stand for the real(imaginary) part of two particle equal-time Green function <S_i S_j> where S_i is total spin operator on site "i"</p> <p>- szsz</p> <p>columns: i j Re Im</p> <p>Where: i,j are site indicies Re(Im) stand for the real(imaginary) part of two particle equal-time Green function <S_z S_z> where S_z is z-th component of spin operator on site "i"</p> <p>- spsm</p> <p>columns: i j Re Im Re Im</p> <p>Where: i,j are site indicies Re(Im) stand for the real(imaginary) part of two particle equal-time Green function <S_i^(+)S_j^(-)> and <S_i^(-)S_j^(+)> where S_i^(+/-) are spin ladder operators</p> <p> </p>
Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set
<p>Data supporting the original figures 1, 2, 3 and 4 of the related publication.</p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.</p>
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