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639 results for “spin”
Critical transitions in degree mixed networks: A discovery of forbidden tipping regions in networked spin systems
<p>Simulation data of critical transitions obtained by running a networked implementation of the Ising model on networks with different degree sequences. </p>
Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes
<p>This is the data release associated with <strong>Vitale et al <a href="https://arxiv.org/abs/2209.06978">2209.06978</a></strong></p> <p><strong>Samples.zip: </strong>Contains all of the hyper posterior samples for the runs listed in Tables G.1.</p> <p>The files are in json format. Bilby offers a dedicated routine to read them in</p> <p> </p> <blockquote> <p>import bilby<br> data= bilby.core.result.read_in_result(path_to_json)</p> </blockquote> <p> </p> <p>See the <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby documentation </a>for what is contained in the result object. </p> <p>For each run, we report the posterior hyper samples for the mass model, reshift model, spin magnitude model, spin tilt model and merger rate [Gpc^-3 yr^-1]</p> <p>Here the name used to store and a short description of each parameter (Follow the references in the Method section of the paper for a description of each sub-model):</p> <ol> <li>Primary mass model (Power Law + Peak for all runs) <ol> <li>power_law_slope_m1, slope of the primary mass power law component</li> <li>minmass_m1, minimum BH mass</li> <li>maxmass_m1, maximum BH mass</li> <li>low_end_smoothing_m1, smoothing at the low-mass end</li> <li>peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)</li> <li>peak_mean_m1, mean of the Gaussian peak</li> <li>peak_sigma_m1, sigma of the Gaussian peak </li> </ol> </li> <li>Mass ratio model (power law for all runs) <ol> <li>power_law_slope_mass_ratio, slope of the mass ratio </li> </ol> </li> <li>Redshift (power law for all runs) <ol> <li>power_law_slope_redshift, slope of the redshift</li> </ol> </li> <li>Spin magnitude (IID beta distributions for all runs) <ol> <li>alpha_chi, first argument of beta distribution</li> <li>beta_chi, second argument of beta distribution</li> </ol> </li> <li>Cosine of tilt angle <ol> <li>Gaussian models <ol> <li>mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian</li> <li>sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian</li> <li>mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian</li> <li>sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian</li> <li>mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean</li> <li>mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean</li> <li>sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma</li> <li>sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma</li> </ol> </li> <li>Beta models <ol> <li>alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution</li> <li>alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution</li> <li>beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution</li> <li>beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution</li> </ol> </li> <li>Tukey models: <ol> <li>tukey_x0, the center of the Tukey as defined in appendix E of the paper</li> <li>tukey_k, Tk as defined in appendix E of the paper</li> <li>tukey_r, Tk as defined in appendix E of the paper</li> </ol> </li> <li>Branching ratios: <ol> <li>spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component</li> <li>spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component; for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong></li> </ol> </li> </ol> </li> <li>Merger rate <ol> <li>rates, merger rate per unit Gpc cubed per unit year</li> </ol> </li> </ol> <p>Note that some of the parameters for the tilt models might not be used, but still stored (and fixed to - usually - zero). This can be checked by verifying what priors were used for each parameter. For example the <em>Isotropic</em> run was obtained from the <em>Isotropic + Gaussian </em>model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) </p> <blockquote> <p>> data['prior']<br> [...]<br> <strong> 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> </p> </blockquote> <p> </p> <p><strong>Figures.zip:</strong> Contains PDFs for all figures in the paper, plus individual figures for p(costau) and dR/dcostau for each model.</p> <p>Drop me (Salvatore Vitale) an email if anything doesn't work, is missing, or if you spot issues. Thanks! </p> <p> </p>
Spin-Glass Behavior in LaCu4Mn Compound
<p>The experimental results of the X-ray powder diffraction, magnetic susceptibility, electrical resistivity, and specific heat measurements of the LaCu<sub>4</sub>Mn compound are presented. LaCu<sub>4</sub>Mn is an intermetallic compound that crystallizes in the hexagonal CaCu<sub>5</sub>-type structure (space group P6/mmm, No. 191), where the atoms on the 3g (1/2, 0, 1/2) site create a kagome lattice, which can cause the spin frustration and leads to a variety of interesting states of matter, such as spin-ice, spin-liquid, and spin-glass states. The Rietveld refinement reveals a random distribution of Mn and Cu atoms on the 3g and 2c site, where about 80% of Mn atoms occupy the 3g site. The lattice parameters are a=5.252(1) Å and c=4.176(1) Å. The low-field zero-field-cooled and field-cooled DC magnetic susceptibilities show splitting below the spin freezing temperature T<sub>f</sub> ≈33 K. At 2 K the magnetization M(H) exhibits hysteresis with coercivity field of ≈5 kOe. The AC susceptibility measurements exhibit a frequency-dependent cusp, associated with a frequency-dependent freezing temperature. Moreover, there is no clear sign of long range magnetic order in specific heat and resistivity measurements.</p>
Dataset - In-situ amplification of spin echoes within a kinetic inductance parametric amplifier
<p>Raw data and analysis for arXiv:2211.11333</p> <p> </p>
Raw data for 'Signature of spin triplet exciton condensations in LaCoO3 at ultrahigh magnetic fields up to 600 T'
<p>Raw data for the paper below</p> <p>Signature of spin triplet exciton condensations in LaCoO3 at ultrahigh magnetic fields up to 600 T<br> A. Ikeda, Y. H. Matsuda, K. Sato, Y. Ishii, H. Sawabe, D. Nakamura, S. Takeyama, J. Nasu<br> arXiv:2201.02704</p> <p> </p>
Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions "
<p>Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions" </p> <p>URL: https://arxiv.org/abs/2205.11965<br> DOI: In press in Nature Communications</p>
Modular nanomagnet design for spin qubits confined in a linear chain
<p>supporting data</p>
Inlist Files for Effects of Full Sphere Convection On M-Dwarf Dynamo Action, Flux Emergence, and Spin-Down
<p>Inlist files for a model of a ZAMS 0.3 M_sun star used as background for global convection simulations in <em>Effects of Full Sphere Convection On M-Dwarf Dynamo Action, Flux Emergence, and Spin-Down</em>. Run using MESA version r12115.</p>
Sentinel-2 Wind Turbine Images with Spin and Spin Quality Labels
<p>Reproduction Data for training a classifier on the Sentinel-2 Band Offset for motion detection.</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 Repo for "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"
<p>Data repo for the paper "Momentum-resolved spin-conserving two-triplon bound state and continuum in a cuprate ladder"</p>
Mbit/s-range alkali vapour spin noise quantum random number generators - DATASETS
<p>Two example datasets. Each dataset consists of 2 files; one, where spin noise is present in the spectrum (noise_on.npy) and one, where spin noise is absent (noise_off.npy). The former is used generate random numbers, while the latter is used to 1. find a baseline threshold \Sigma, as described in the paper, and 2. to find the spin noise spectrum. </p> <p>The spin noise spectrum can be found by subtracting the spectra of the first and second file, as it is only present in the first file. Every dataset should be filtered around the Larmor frequency prior to any bit generation. The width of the band-pass filter should be such that the entirety of the spin noise spectrum is contained.</p> <p>Bit generation is to be done by protocols described in the paper "Mbit/s-range alkali vapour spin noise quantum random number generators". All bitrates are calculated at \Sigma = 5 \sigma, where \sigma is the standard deviation of noise_off.npy for the given experiment.</p> <p>Cs12: Sample rate 50 MHz. Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 670 kHz. There is a large induced gradient present in the system, so the spin noise spectrum is not a Lorentzian shape, but is spread with a full-width half max of 500 kHz . Protocol 1 raw bitrates are approximately 40 kb/s, whereas protocol 2 raw bitrates are approximately 2.40 Mb/s.</p> <p>Cs14: Sample rate 100 MHz. Spin noise measurement (noise_on.npy) has 1G samples (16 bit signed). Background measurement (noise_off.npy) has 10M samples (16 bit signed). Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 440kHz ,T2 is approximately 2e-6 s. This is the dataset presented in the paper. Protocol 1 raw bitrates are approximately 40 kb/s, protocol 2 raw bitrates are approximately 1.97 Mb/s.</p>
Dataset for 'Identifying Pauli spin blockade using deep learning'
<p>This data set contains the raw and processed data used in the publication 'Identifying Pauli spin blockade using deep learning' (<a href="https://arxiv.org/pdf/2202.00574.pdf">https://arxiv.org/pdf/2202.00574.pdf</a>). This also includes trained neural networks, and evaluation results from training neural networks. </p> <p>For code related to this publication, see <a href="https://github.com/oxquantum-repo/identifying_psb/">https://github.com/oxquantum-repo/identifying_psb/</a>.</p>
Magnetocaloric effect in spin-glass-like GdCu4Mn compound
<p>Dataset for: Magnetocaloric effect in spin-glass-like GdCu<sub>4</sub>Mn compound</p> <p>Materials based on Gd with magnetic frustration may have high magnetocaloric application potential. GdCu<sub>4</sub>Mn crystallizes in hexagonal CaCu<sub>5</sub>-type structure with space group <em>P</em>6/<em>mmm</em> (no. 191, Pearson symbol: <em>hP</em>6). Lattice parameters for the polycrystalline sample are equal to <em>a</em> = 5.114(3) Å and <em>c</em> = 4.151(4) Å. Due to structural disorder, namely random distribution of Cu and Mn atoms on 2<em>c</em> and 3<em>g</em> positions, a spin-glass-like behavior is observed in this material at low temperatures. In addition to the standard measurements of DC magnetization and AC magnetic susceptibility, the spin-glass state was confirmed by the memory effect and relaxation behavior. The absence of clear anomalies in the temperature dependencies of specific heat and electrical resistance suggests the lack of long-range magnetic order.</p>
Supplementary material for "Spin-Polarized Tunable Photocurrents"
<p>Supplementary material for "Spin-Polarized Tunable Photocurrents", <em>Nano Lett.</em> 2021, 21, 7, 3177–3183 (2021), https://pubs.acs.org/doi/full/10.1021/acs.nanolett.1c00420</p> <p> </p>
Real-space correlation functions of nearest-neighbour spin ice
<p>Real-space correlation functions <span class="math-tex">\(\langle \sigma_i\sigma_j\rangle\)</span> of the nearest-neighbour spin ice Hamiltonian <span class="math-tex">\(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\)</span> on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells.</p> <p><strong>File formats</strong></p> <p>The file name indicates the temperature (in units of J) where the measurement was taken.</p> <p>Files with extension .dat contain the correlation functions as double-precision real floats.<br> Files with extension .err contain the standard errors as single-precision real floats.</p> <p>Both file types contain an array of shape (4, 4, 4, 128, 128, 128). The meaning of the indices (from major to minor):</p> <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>index λ of FCC lattice point in the cubic unit cell</li> <li>offset of cubic lattice cells</li> </ul> <p>Entry [μ, ν, λ, x, y, z] corresponds to the correlator <span class="math-tex">\(\langle \sigma(\vec R + \vec r^\mathrm{FCC}_\lambda + \vec r_\mu)\sigma(\vec r_\nu)\rangle\)</span>, where <span class="math-tex">\(\vec R = [xyz] \)</span>, the FCC index λ corresponds to the lattice points <span class="math-tex">\(\vec r^\mathrm{FCC}_0 = [000], \vec r^\mathrm{FCC}_1=[011]/2, \vec r^\mathrm{FCC}_2=[101]/2,\vec r^\mathrm{FCC}_3 = [110]/2\)</span>, and the sublattice indices correspond to the site offsets <span class="math-tex">\(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\)</span> from the nearest FCC lattice point. Coordinates x,y,z run between 0 and 127 in periodic boundary conditions.</p> <p>The utility loader.py loads the files, shapes them in the correct array format, and extracts individual correlators.</p> <p><strong>Details of data generation</strong></p> <p>We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.</p>
Reciprocal-space correlation functions of nearest-neighbour spin ice
<p>Reciprocal-space correlation functions <span class="math-tex">\(\langle \sigma_\mu(k)\sigma_\nu(-k)\rangle\)</span> of the nearest-neighbour spin ice Hamiltonian <span class="math-tex">\(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\)</span> on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells.</p> <p><strong>File formats</strong></p> <p>The file name indicates the temperature (in units of J) where the measurement was taken.</p> <p>Files with extension .dat contain the correlation functions as double-precision complex floats.<br> Files with extension .err contain the standard errors as single-precision real floats.</p> <p>Both file types contain an array of shape (4, 4, 128, 256, 256). The meaning of the indices (from major to minor):</p> <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>wave vector components in units of <span class="math-tex">\(2\pi/(128a_0)\)</span></li> </ul> <p>The sublattice indices correspond to the site offsets <span class="math-tex">\(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\)</span> from the nearest FCC lattice point. For the Fourier transforms, all sublattices are shifted to these lattice points, so the correlators stored in the files are periodic with respect to the FCC reciprocal lattice.</p> <p>The wave vector range covered is <span class="math-tex">\(0\le k_x< 2\pi/a_0, 0\le k_y,k_z< 4\pi/a_0\)</span>, an (unconventional) reciprocal-space unit cell of the FCC pyrochlore lattice.</p> <p>The utility loader.py loads the files, shapes them in the correct array format, and extracts single k-points.</p> <p><strong>Details of data generation</strong></p> <p>We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.</p>
Unidirectional spin wave emission by travelling pair of magnetic field profiles
<p>This is the full data for this paper: https://arxiv.org/pdf/2307.12653.pdf</p> <p>G. P. anf J. W. K. would like to acknowledge the erasmus mundus MaMaSELF programm and the support from the National Science Center – Poland grant No. 2021/43/I/ST3/00550</p>
Dataset: "GW190521: tracing imprints of spin-precession on the most massive black hole binary"
<p>This dataset contains the results presented in "<em>GW190521: tracing imprints of spin-precession on the most massive black hole binary</em>": <a href="https://arxiv.org/abs/2310.01544">https://arxiv.org/abs/2310.01544</a>.</p> <p>The accompanying repository used to take this data and generate the figures in the paper can be found at <a href="https://github.com/simonajmiller/gw190521-timedomain-release/">https://github.com/simonajmiller/gw190521-timedomain-release/</a>.</p>
Magic Angle Spinning Effects on Longitudinal NMR Relaxation: 15N in L-Histidine
<p>Experimental datasets for the publication entitled: Magic Angle Spinning Effects on Longitudinal NMR Relaxation: <sup>15</sup>N in L-Histidine</p>
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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.