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62 results for “wave function”
Momentum space wave functions for the linear potential
<p>Normalized momentum space wave functions for the linear potential. The Schrödinger equation was solved with the methods described in "A simple high-accuracy method for solving bound-state equations with the Cornell potential in momentum space", Alfred Stadler, Elmar P. Biernat, Vasco Valverde. </p> <table> <tbody> <tr> <td><a href="https://arxiv.org/abs/2407.21789">arXiv:2407.21789</a> [hep-ph]</td> </tr> </tbody> </table> <p>(to be pulished in Physical Review D)</p> <p>The wave functions correspond to the energie eigenvalues shown in Table VI of this work.</p> <p>The name of each file indicates the orbital angular momentum and which eigenstates it contains. For instance, wf_n1-5_l=0_np=1000_NL=5.txt contains the wave functions of the states n=1, 2, 3, 4, 5 for l=0, and wf_n6-10_l=3_np=1000.txt the wave functions of the states n=6, 7, 8, 9, 10 for l=3. Furthermore, np=1000 means that 1000 momentum integration points were used for the solution of the Schrödinger equation, and NL=5 or NL=15 means that 5 or 15 points were used for the Lagrange interpolations.</p> <p>Each data file in text format contains 6 columns and 1000 lines. Column 1 ist the momentum (GeV), columns 2-6 the wave functions. The momenta were generated according to Eq. (4.3) of the article, with p_0=1.</p> <p> </p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Photon wave function and the electromagnetic vacuum
<p>The association of the density of states theory to the vector potential quantization of the electromagnetic field leads naturally to the definition of the photon quantization volume, ascribing an intrinsic physical geometrical property to a single photon. Photons are not point particles and constitute a particular case in particles standard model. Based upon the vector potential with quantized amplitude, we define a photon wave function representing an amplitude probability for the photon localization normalized with respect to the photon quantization volume. In addition, the established photon wave function satisfies Maxwell's propagation equation and Schrödinger's equation with the relativistic massless particle Hamiltonian as well as a Schrödinger-like equation for the vector potential. </p><p>The electromagnetic vacuum derives straightforward from the photon vector potential function and has both classical and quantum representations. It permits to remedy to the zero-point energy shortcomings and singularities in quantum electrodynamics. Finally, the photon wave function is naturally related to the electromagnetic vacuum states putting the basis for understanding entanglement. </p>
Molecular Models and Wave Function Definitions for Models A-G of the [2Fe]F Cluster in FeFe-hydrogenase Maturase Enzyme HydF
<p>The dataset contains all relevant atomic positional coordinates for 2Fe-cluster models, and electronic wave function data (using formatted Gaussian checkpoint files) as described in the related publication (see citation below).</p> <p>The version 2.0 contains additional models for [2Fe-2S] cluster linked [2Fe]F constructs.</p> <p>The top folder contains "analysis.xlsx" electronic spreadsheet that summarizes all the numerical results for absolute and relative electronic energy values, internal coordinates, calculated and scaled vibrational frequencies for diatomic stretching modes. The details of developing scaled quantum forcefields as a function of level of theory and model composition are also given.<br> The schematic structural definitions are given in the "models.pdf" file and keys for abbreviations are provided in "symbols.txt" file.<br> </p>
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
Supplemental Data for "Energy minimization of paired composite fermion wave functions in the spherical geometry"
<p>Includes extra data for "Energy minimization of paired composite fermion wave functions in the spherical geometry".</p>
Data of "Deterministic Shaping and Reshaping of Single-Photon Temporal Wave Functions"
<p>Data published in "<em>Deterministic Shaping and Reshaping of Single-Photon Temporal Wave Functions</em>".</p> <p>Phys. Rev. Lett. <strong>123</strong>, 133602</p>
Wave function for the lithium electride ROGDAS
<p>The wave function for the lithium electride ROGDAS at the b3lyp/6-31+G(d) level, analysed as a molecular electrostatic potential, a spin density, a molecular orbital and as a reduced density gradient (NCI) distribution.</p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
EXCEED-DMv0.2.8: DFT-computed electronic wave functions for Si and Ge
<p>Wave function coefficients, with and without the all-electron reconstruction, for Si and Ge on a 10x10x10 uniform k mesh. For use with EXCEED-DM to compute Dark Matter induced electronic excitation rates.</p> <p>Note:</p> <p>Compatible with EXCEED-DMv0.2.8</p>
Original data and code for "Wave-function engineering on superconducting substrates: Chiral Yu-Shiba-Rusinov molecules"
<p>We provide all experimental data and the code to simulate the tight-binding YSR patterns in the paper "Wave-function engineering on superconducting substrates: Chiral Yu-Shiba-Rusinov molecules"</p>
Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method"
<p>Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method" by Nicholas D. M. Hine</p>
Cryptic Magma Chamber in the Deccan Traps imaged using receiver functions and Surface wave dispersion
<p>This is a Dataset for the article titled "Cryptic Magma Chamber in the Deccan Traps imaged using receiver functions and Surface wave dispersion" by Saha et. al. (2023). For more information about the data, contact <a href="mailto:kumarsahagokul123@gmail.com">kumarsahagokul123@gmail.com.</a></p>
Plots of potential function of ion-acoustic waves
<p>Plots of the potential function of ion-acoustic waves under the KdV equation for nonextensive parameter (a) q = -0.4, (b) q = 0.1, (c) q = 1 and (d) q = 1.2. Here, the local minima of potential curves (a)-(d) show the existence of solitary wave solutions. The positive region corresponds to a compressive solitary solution and negative region corresponds to rarefactive solitary wave solution. </p>
Reproducibility pack for article: Quantum turbulence, superfluidity, non-Markovian dynamics, and wave function thermalization
<p>The supplementary material contains a reproducibility pack for results presented in the paper:</p> <p><em>Quantum turbulence, superfluidity, non-Markovian dynamics, and wave function thermalization</em><br>Aurel Bulgac, Matthew Kafker, Ibrahim Abdurrahman, and Gabriel Wlazłowski<br><a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.L042003">Phys. Rev. Research 6, L042003 (2024)</a></p> <p>The packs contain full information needed to restore the numerical simulation of dynamics of 12 quantum vortices.<br>To be able to restore the results of calculations, you need to use the <a href="https://wslda.fizyka.pw.edu.pl/">W-SLDA Toolkit</a>.<br>See the documentation of the <a href="https://wslda.fizyka.pw.edu.pl/">W-SLDA Toolkit</a> to learn how to use the code and the reproducibility packs.</p>
Convolutional transformer wave functions
<p>Ground state and dynamics data for the manuscript "Convolutional transformer wave functions".</p> <p> </p> <p>The ground state data (Fig.2 in the manuscript) is provided for the 10x10 J1J2 Heisenberg model with J2/J1=0.5.</p> <p>The dynamics data (Fig.3 in the manuscript) is the evolution of observables with Jt=1e-3 in our simulations and Jt=2e-3 in "CNN_Schmitt".</p>
Receiver functions, SKS/SKKS and local S-wave waveforms in western-central Tibet
<p>This data set consist of P-wave receiver functions (PRFs), SKS/SKKS and local S-wave waveforms collected from the seismic stations in western-central Tibet that are used in the study of Zhang et al. (2023). Recordings are mainly from the ZJU-Tibet seismological array between September 2018 and June 2021 operated by Zhejiang University, and supplemented with the PRFs from 'Hi-CLIMB', 'HIMNT', and 'U.S.-China West Tibet' networks. The picked arrivals of Moho P-to-S converted phases on PRFs are also provided. Please note that this data is provided for reproduction purposes only.</p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Data and Code for "Topological atom-optics and beyond with knotted quantum wave functions"
<p>This folder contains data files and Mathematica 12 Student Edition files for processing the data files and generating figures for the paper “<em>Topological atom optics and beyond with knotted quantum wavefunctions</em>”, authored by M. Jayaseelan, J. D. Murphree, J. T. Schultz, J. Ruostekoski, and N. P. Bigelow.</p> <p> </p> <ol> <li>Folder “Data_Only” contains *.csv and *.SPE files for each of the following magnetic phases: <ul> <li> <ol> <li>Polar</li> <li>Cyclic</li> <li>Biaxial Nematic</li> </ol> </li> </ul> </li> <li>Folder Fig2_Polar_code contains <ul> <li> <ol> <li>Data for the Polar magnetic phase (duplicated from Data_Only folder): etau.SPE and e.csv</li> <li>e_imGData, e_imGDataC, e_imLGData, e_imLGDataC: *.csv files that are output as intermediate data processing steps.</li> <li>Fig2_KnotsAtomsPolar_v2.nb: Mathematica file that produces the figures for Fig. 2</li> </ol> </li> </ul> </li> <li>Folder Fig3_Cyclic_code contains <ul> <li> <ol> <li>Data for the Cyclic magnetic phase (duplicated from Data_Only folder): lor_atau.SPE and lor_a_tau.csv</li> <li>lor_a_imGData, lor_a_imG0Data, lor_a_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig3_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 3</li> </ol> </li> </ul> </li> <li>Folder Fig4_Cyclic_code contains <ul> <li> <ol> <li>Fig4_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 4</li> </ol> </li> </ul> </li> <li>Folder Fig5_BN_code contains <ul> <li> <ol> <li>Data for the BN magnetic phase (duplicated from Data_Only folder): sk_ltau.SPE and sk_l.csv</li> <li>sk_l_imGData, sk_l_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig5_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 5</li> </ol> </li> </ul> </li> <li>Folder Fig6_Fig7_BN_code contains <ul> <li> <ol> <li>Fig6_Fig7_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 6 and Fig.7</li> </ol> </li> </ul> </li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.