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11 results for “perturbation theory”
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".
<p>Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".</p>
Perturbative effective field theory expansions for cosmological phase transitions, dataset
<p>This dataset is the work of Oliver Gould and Tuomas V.I. Tenkanen. It collects the numerical data from the paper <a href="https://arxiv.org/abs/2309.01672">"Perturbative effective field theory expansions for cosmological phase transitions"</a> (2023). It primarily contains data from perturbative calculations of the thermal evolution of the real-triplet extended Standard Model at two benchmark parameter points.</p><p>In addition, for comparison to the perturbative results, we have included data of the scalar quadratic condensates as a function of temperature from the lattice Monte-Carlo simulations of Lauri Niemi, Michael J. Ramsey-Musolf, Tuomas V.I. Tenkanen and David J. Weir, from the paper <a href="https://doi.org/10.1103/PhysRevLett.126.171802">"Thermodynamics of a Two-Step Electroweak Phase Transition"</a> (2020). We thank the authors for granting permission to reproduce this data here.</p><p>Everything is contained within the archive file <a href="https://zenodo.org/api/records/10353066/draft/files/triplet_two_step_data.tar.gz/content">triplet_two_step_data.tar.gz</a>, a tarball compressed with Gzip. For further details and for the context of this dataset, see the above papers. Details of the conventions used in the dataset can be found in the accompanying README.md file.</p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory
<p>This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC). </p> <p>Several surrogate models are currently available in this catalog:</p> <ol> <li><strong>BHPTNRSur2dq1e3</strong>, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>BHPTNRSur1dq1e4</strong>, an updated version of the <strong>EMRISur1dq1e4 </strong>model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>EMRISur1dq1e4</strong>, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>
Applicability and limiations of Cluster Perturbation Theory for Hubbard models
<p>These are the Cluster Greensfunctions that were used in the paper "Applicability and limiations of Cluster<br> Perturbation Theory for Hubbard models" published as part of the special edition “S.I.: Non-Equilibrium Quantum<br> Physics, Many Body Systems, and Foundations of Quantum Mechanics” in the European Journal of Phyiscs in 2023.<br> The Greensfunctions were generated via a Chebyshev expansion and are currently in a real space representation.<br> You may use python and import them via numpy as follows:</p> <p>```console<br> import numpy as np</p> <p>MC = <number_of_sites> # Here you have to add the number of cluster sites (e.g. 16 for a 4x4 cluster)</p> <p>greensfunctions = np.genfromtxt("<file_name>")<br> greensfunctions = greensfunctions.reshape(greensfunctions.shape[0], MC, MC)<br> ```</p> <p>This way you obtain a tensor where the first dimension corresponds to the frequency and the other two<br> to the real space indices.</p> <p>For further questions please contact the corresponding author Nicklas Enenkel via E-mail<br> (nicklas.enenkel@quantumsimulations.de)</p>
GPR surrogate model dataset for remnant black hole properties using perturbation theory and NR
<p>This is the data-set used in <strong><code>BHPTNR_Remnant</code></strong> which is an easy-to-use python package to efficiently predict the remnant mass, remnant spin, peak luminosity and the final kick imparted on the remnant black hole directly from the gravitational radiation using GPR fits. These fits have been built on the remnant data calculated from numerical relativity informed black hole perturbation theory based waveforms.</p>
Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory
<p>Permittivity-over-temperature diagram for the publication "Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory" published under DOI 10.1021/acs.jced.3c00323 in the Journal of Chemical and Engineering Data.</p>
Supplementary data for "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory"
<p>Supplementary data belonging to "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory".</p> <p>The data includes geometries for the molecules in the paper as well as the Hamiltonian matrix elements, orbital coefficients and overlap matrices to reproduce the data in the paper.</p>
Phonons from Density-Functional Perturbation Theory using the All-Electron Full-Potential Linearized Augmented Plane-Wave Method FLEUR
<p>The archive files contain the input and result files for the corresponding publication in IOP Electronic Structure - Technical Notes, as well as a short python script to plot them.</p>
Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory
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