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71 results for “neutrino”
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
MESA model files and data for: 'Stellar Neutrino Emission Across The Mass-Metallicity Plane'
<p>Example MESA model files and stellar evolution tracks for download from "Stellar Neutrino Emission Across The Mass-Metallicity Plane".</p>
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon
<p>Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This data release corresponds with the results of "Analysis A" published in arXiv:2003.10630[nucl-ex]. The data release enables further studies of CEvNS.</p> <p>Use of the data release is presented in the accompanying pdf document within this submission. Example code is included within the release as part of this submission. The materials here are also available at http://coherent.ornl.gov/data/, which preserves the directory structure used within the accompanying document. Note the use of the example code in this release expects the directory structure written within the accompanying pdf document.</p>
COHERENT Collaboration data release from the first observation of coherent elastic neutrino-nucleus scattering
<p>Release of COHERENT Collaboration data associated with the first observation of coherent elastic neutrino-nucleus scattering (CEvNS), as published in Science (DOI: <a href="http://dx.doi.org/10.1126/science.aao0990">10.1126/science.aao0990</a>) and also available as arXiv:1708.01294[nucl-ex].</p> <p>This data set should enable researchers to extend the study of CEvNS as desired. Future COHERENT Collaboration results will have similar data releases.</p> <p>Example code can be accessed at https://code.ornl.gov/COHERENT/codeExamples_dataRelease_april2018.<br> The full data-release package, including data, code examples, and a descriptive accompanying document can be found at http://coherent.ornl.gov/data.</p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Ancillary files for "Reinterpreting the ATLAS bounds on heavy neutral leptons in a realistic neutrino oscillation model [arXiv: 2107.12980]"
<p><em>(Description copied from Appendix A "Ancillary files" of the companion paper)</em></p> <p>In order to simplify the interpretation of experimental results within realistic HNL models, we are including a number of data files along with the present publication. They can be used to generate the relevant signal samples, or to implement the extrapolation method presented in section 3.2.</p> <p><strong>Card files for the Monte-Carlo event generation</strong></p> <p>The /attachments/card_files folder contains the MadGraph card files (ending in .dat) and scripts (ending in .txt) for generating the signal samples used in this analysis, as well as for computing the total HNL width. Due to the OSSF veto, only processes with no opposite-charge same-flavor lepton pairs have been included. Additional relevant processes can easily be added by modifying the <em>generate</em> and <em>add process</em> lines in the *.txt files. All samples (except the ones used to compute the HNL width, which are generated at parton level) are generated at leading order, include up to two hard jets, and are showered and hadronized using Pythia 8. This is essential for obtaining a realistic W spectrum. The shower parameters could probably benefit from further tuning, and further improvements in the W spectrum accuracy are expected at NLO (using a suitable model). To allow computing the signal efficiencies, all cuts have been disabled in the run card (with the exception of the maximum <span class="math-tex">\(|\eta_{\mathrm{jet}}|\)</span> which needs to be set to 5 for correct matching).</p> <p><strong>Signal cross sections</strong></p> <p>The cross sections for the various processes considered in this analysis, as well as the total HNL width (both computed using MadGraph as described in section 3.2), are provided as JSON files in the /attachments/cross_sections folder.</p> <p>The file total_hnl_width.json contains the total HNL width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> (expressed in GeV), computed for the 5 mass points used in this analysis, and under the assumption of unit mixing with a single flavor <span class="math-tex">\(\alpha\)</span>, for each flavor. The total HNL width can then be computed for any combinations of mixing angles using eq. (3.2). The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N\)</span>, and the second one the flavor <span class="math-tex">\(\alpha\)</span> for which the total width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> has been computed for a unit mixing angle <span class="math-tex">\(|\Theta_{\alpha}|^2 = 1\)</span> (with <em>Wtot_e</em> for <span class="math-tex">\(\alpha=e\)</span>, <em>Wtot_mu</em> for <span class="math-tex">\(\mu\)</span> and <em>Wtot_tau</em> for <span class="math-tex">\(\tau\)</span>).</p> <p>The file cross_sections.json contains the reference cross sections <span class="math-tex">\(\sigma_P^{\mathrm{ref}}\)</span> (in pb) for all the processes <em>P</em> considered in this analysis, expressed for <span class="math-tex">\(|\Theta|_{\mathrm{ref}}^2 = 1\)</span> and <span class="math-tex">\(\Gamma_{\mathrm{ref}} = 10^{-5}\,\mathrm{GeV}\)</span>. The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N \)</span> and the second the process <em>P</em>. The correspondence between the key and the physical process can be found in table 7.</p> <p><strong>Signal efficiencies</strong></p> <p>The efficiencies resulting from the event selection described in section 3.1, as well as their parametrization according to eq. (3.6) (as discussed in section 3.3) can respectively be found in the files efficiencies.json and fitted_efficiencies.json in the /attachments/efficiencies folder.</p> <p>The file efficiencies.json is organized as follows. The data is located in a triply nested dictionary under the data key: the first level corresponds to the HNL mass hypothesis <span class="math-tex">\(M_N\)</span>, the second to the process key (cf. table 7) and the third to the <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bin for which the efficiency is computed. The values of the bottom-most dictionary are lists containing the efficiencies for a number of HNL lifetimes, as listed in meters in levels/lifetime.</p> <p>Finally, the file fitted_efficiencies.json is also organized as a triply nested dictionary, with the first level corresponding to the HNL mass <span class="math-tex">\(M_N\)</span>, the second to the process key, and where the third level denotes the fit parameter from eq. (3.6). tau0 is for <span class="math-tex">\(\tau_0\)</span>, epsilon0_total for <span class="math-tex">\(\epsilon_0\)</span> (the unbinned prompt efficiency), and epsilon0_binned is a list containing the prompt efficiencies <span class="math-tex">\(\epsilon_{0,b}\)</span> for the five <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bins <em>b</em> (in the same order as in efficiencies.json). The layout described here (or a similar one) can be used by experiments to report their signal efficiencies in a way that allows theorists to compute the expected signal for arbitrary choices of mixing angles.</p>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
Diffuse Emission of High-Energy Neutrinos from a Global Fit to Cosmic Rays
<p>Model of diffuse emission of high-energy neutrinos from a global fit of cosmic rays and model of high-energy neutrino emission from unresolved pulsar-powered sources.</p> <p>The maps presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) of per-flavor intensity in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. We use a value of NSIDE=256 and the RING binning scheme.</p> <p>We here make available our fiducial model, which is calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections and the <em>GALPROP</em> gas maps. We calculated the emission from unresolved sources following Vecchiotti et al. 2022, ApJ, 928, 19.</p> <p>In Version 2 of this dataset, we also make available the local cosmic ray fluxes of our fiducial model obtained from a global fit to cosmic ray data together with the corresponding 68% and 95% uncertainty bands. These are shown in figure 6 of <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a>. The nuclear fluxes are in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>, the fluxes of electrons and positrons are in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> . The fluxes are local interstellar fluxes without solar modulation.</p> <p>In Version 3 of this dataset, we add the fiducial diffuse gamma ray model calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections as well as the <em>GALPROP</em> gas maps and ISRF model. We separately make available 3 maps: The hadronic emission on neutral atomic gas, the hadronic emission on molecular gas and the leptonic emission from Inverse Compton Scattering. </p> <p>Similar to the dataset of the fiducial neutrino model, the maps are presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>. We use a value of NSIDE=256 and the RING binning scheme. For the hadronic maps, the intensity is given at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. For the leptonic maps from Inverse Compton Scattering, the intensity is given at 48 logarithmically spaced energies between 1 GeV and 10<sup>6</sup> GeV.</p> <p>The structure of the files is somewhat different from the file containing the fiducial neutrino model. This is to allow for easy use of the gamma ray maps with the <em>gammapy</em> package (Deil et al. 2017, <a href="https://arxiv.org/abs/1709.01751"> arXiv:1709.01751</a>).</p> <p>Also available in Version 3 are the full spatio-spectral cosmic ray distributions in the Milky Way as predicted by our fiducial model. The nuclear fluxes are given for each species in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 63 energies between 1 GeV and 10<sup>9</sup> GeV. The leptonic fluxes are given for each species in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 36 energies between 1 GeV and 10<sup>5</sup> GeV. </p> <p>All fluxes are given on a spatial grid at 81 galactocentric radii from 0 kpc to 20 kpc and 61 distances perpendicular to the galactic plane between -6 kpc and 6 kpc.</p> <p>Finally, a word of caution about the extra component of cosmic ray leptons included in our model: This component is contained in the last <em>HDUnit</em> of the <em>fits</em> file containing the leptonic cosmic ray distributions. It is there denoted as a flux of electrons. It must, however, also be added to the flux of positrons to achieve correct results.</p> <p>Please refer to <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a> for further details.</p> <p>When using these models in your research work, please refer to this Zenodo dataset and the publication.</p>
Supplementary Data: A Frequentist Analysis of Three Right-Handed Neutrinos with GAMBIT
<p><strong>Supplementary Data</strong><br> <em> A Frequentist Analysis of 3 Right-Handed Neutrinos with GAMBIT.</em></p> <p>The files in this record contain data for the frequentist global fit of a model with three right-handed neutrinos using the <a href="http://gambit.hepforge.org">GAMBIT</a> tool.</p>
Supplementary Data: Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)
<p><strong>Supplementary Data</strong></p> <p><em>Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)</em></p> <p>The files in this record contain data from the scans of the models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> paper on neutrino masses.</p> <p>The files consist of</p> <ul> <li>21 <code>.yaml</code> files corresponding to different models, sampling parameters and/or priors</li> <li>11 final <code>.hdf5</code> files, containing the results of running GAMBIT with each yaml file</li> <li>10 <code>.margestats</code> files containing 1D credible regions for parameters and observables, obtained by running <a href="https://github.com/cmbant/getdist">getdist</a> on the hdf5 files</li> <li>An example file 3-NHB_Neff2_PC500_pp.pip file for plotting the results of a single hdf5 file with <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball including all files in this record except the hdf5 files.</li> </ul> <p>The different yaml, hdf5 and margestats files corresponding to different models, priors or setttings follow the naming scheme <code>[scan index]-[hierarchy][m_nu0 prior]_[Neff prior]_[scanner]_[extra]_[step].[extension]</code>, where</p> <ul> <li>scan index = <code>1</code>-<code>11</code></li> <li>hierarchy = <code>NH</code> (normal hierarchy), <code>IH</code> (inverted hierarchy)</li> <li>m_nu0 pior = <code>A</code> (linear-log prior on m_nu0), <code>B</code> (linear prior on m_nu0)</li> <li>Neff prior = <code>0</code> (Delta N_eff = 0), <code>1</code> (Delta N_eff > 0), <code>2</code> (Delta N_eff free)</li> <li>scanner = <code>PC500</code> (Polychord with 500 live points), <code>DIV10k</code> (Diver with NP=1e4)</li> <li>extra = blank (standard likelihood combination), <code>Lyalpha</code> (likelihood also includes eBOSS DR14 Lyman-alpha BAO scale measurements)</li> <li>step = blank (main scan), <code>pp</code> (postprocessing of outputs of main scan).</li> <li>extension = <code>yaml</code>, <code>margestats</code>, <code>hdf5/hdf5.tar.gz</code></li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.5.0, and the pip file is tested with pippi 2.1. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please do not expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Tabulation and interpolation of NLO neutrino-antineutrino production and scattering rates at MeV temperatures
<p>This record contains data used in the paper "<em>Neutrino-antineutrino production, annihilation, and scattering at MeV temperatures and NLO accuracy</em>" <a href="https://arxiv.org/abs/2412.03958">2412.03958</a>. Please consult the main text for details on the tabulated coefficients and their proper implementations.</p> <p>We provide numerical data and an interpolation routine (c-code) for evaluating double-differential rates, intended to facilitate possible studies of the full kinetic equations for neutrino decoupling in the early universe. These data can also be used to obtain integrated quantities, such as the energy density transfer rates, or neutrino interaction rates (e.g. <a href="https://arxiv.org/abs/2312.07015">2312.07015</a>). The QED corrections to the spectral functions were computed using an adapted version of the public code: <a href="https://doi.org/10.5281/zenodo.3478143">https://doi.org/10.5281/zenodo.3478143</a> .</p> <p>The archive file contains:</p> <ul> <li><code>grid_ABCD.dat</code> : recorded coefficients A, B, C, D (on p+,p- grid in units of QED plasma temperature)</li> <li><code>interpolation.c</code> : code to interpolate and calculate the (integrated) energy density transfer rates</li> <li><code>aux/...</code> : additional files needed for multidimensional integration routine "<a href="https://github.com/stevengj/cubature/">cubature</a>"</li> </ul>
Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry
<p>This repository contains the simulation data used in the study titled `Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry`. All the necessary data are provided in the hdf5 file. Please check the article and the README enclosed herewith for more details.</p>
Diffuse Supernova Neutrino Background search at Super-Kamiokande (arXiv:2109.11174, PRD 104, 122002)
<p>This is the data release for the PRD 104, 122002 article about the Diffuse Supernova Background search at Super-Kamiokande. The figures which can be reproduced with this release are listed in the README of the data_release folder.</p> <p>The spectral_analysis_npy.SKMC.tar.gz folder contains the files needed to reproduce the spectral analysis in section VII of the paper. To incorporate it to the spectral analysis code please follow the instructions given in the README of <a href="https://github.com/soso128/spectral_analysis">the spectral analysis Github folder</a>.</p>
Fermi-GBM Data Release Related to Searches for Neutrinos from Gamma-Ray Bursts using the IceCube Neutrino Observatory
<p>This data release includes Fermi Gamma-ray Burst Monitor (GBM) localizations used in searches for neutrinos from gamma-ray bursts (GRB) by the IceCube Neutrino Observatory. These localizations are provided publicly to the community since they are generally useful for any analysis that needs the Fermi-GBM localization for a GRB.</p> <p><strong>Full Details:</strong></p> <p>The files contained herein are HEALPix representations of GRB localizations from the Fermi-GBM stored as FITS files and produced according to the automated method described in [1]. Each file represents the probability density (statistical + systematic) for the true source location. By definition, this excludes the Earth occulted region of the sky, which is set to 0 due to the fact that real sources are not visible through the Earth. These files cover a time range spanning the first detection of GRBs by GBM in July 2008 through July 2019 and should be considered preliminary. The files are preliminary in the sense that they contain some key differences to the official files hosted at HEASARC FTP server through the Fermi Science Support Center (FSSC; <a href="https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/">https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/</a>). We list the key differences here:</p> <ul> <li>Fermi began production HEALPix FITS files in early 2018, and files prior to that have not been officially provided. The files in this archive are currently the only version of HEALPix files pre-2018.<br> </li> <li>These files were not produced via the standard GBM operational pipeline; however they were produced with the same functional code that is used to make the files. The result of this is that the standard quality checks on the FITS headers by uploading to the FSSC were skipped. The primary header is most affected, with some null values, but these null values do not affect the HEALPix data.<br> </li> <li>These localizations may have centroids that are slightly different than reported in the online catalog. This is because an automated algorithm for localization (RoboBA) was used to localize the GRBs and produce these files as opposed to the manual Human-in-the-Loop localization performed for every GRB prior to 2016, and ~15% of GRBs thereafter [1].<br> </li> <li>These localizations contain an updated and improved systematic uncertainty model compared to the pre-July 2019 localizations at the FSSC. The new systematic uncertainty model is explained in [1], while the older localizations at the FSSC contain a systematic uncertainty model from [2].<br> </li> <li> In general, the official localizations hosted at the FSSC currently do not remove localization probability that overlaps the Earth, but these files do remove the probability that overlaps the Earth and renormalizes the remaining PDF. This encodes the assertion that the localization is indeed of an astrophysical nature.</li> </ul> <p>The FITS files are organized with two HDUs:</p> <ul> <li> PRIMARY HDU with some basic metadata about the mission from which the data originated<br> </li> <li> HEALPIX HDU containing header information about the GBM detector pointings, as well as the Sun and Geocenter localizations with respect to Fermi. There are two data fields contained in the extension: <ul> <li> PROBABILITY: the differential localization probability per pixel (NSIDE=128)</li> <li> SIGNIFICANCE: integrated probability for estimating confidence intervals (NSIDE=128)</li> </ul> </li> </ul> <p>Furthermore, we provide images of each localization. The images are a Mollweide projection of the sky, with the 50% and 90% localization confidence regions marked in shaded purple. The location of the Earth from Fermi's perspective is marked in shaded blue.</p> <p>The GBM trigger number associated with each FITS file and image is listed in the filename.</p> <p><strong>References:</strong></p> <p><a href="https://iopscience.iop.org/article/10.3847/1538-4357/ab8bdb">[1] Goldstein, A. et al. 2020, ApJ, 895, 40</a><br> <a href="https://iopscience.iop.org/article/10.1088/0067-0049/216/2/32/meta">[2] Connaughton, V. et al. 2015, ApJS, 216, 32</a></p>
Data from: Improved FIFRELIN de-excitation model for neutrino applications
<p>New FIFRELIN cascades for the isotopes 156,158Gd are distributed to the community, to be used for various applications.</p> <p>The new cascades feature an improved modeling of de-excitation. The main improvements are:</p> <p>1) Inclusion of primary transitions from EGAF database.</p> <p>2) Treatment of gamma-directional correlations</p> <p>3) Improved physics for the Internal Conversion process and X ray emission.</p> <p>With the use of the files provided, please cite the following publication:</p> <p>H. Almazán et al, <a href="http://doi.org/10.1140/epja/s10050-023-00977-x"><em>The European Physical Journal A</em> <strong>volume 59</strong>, Article number: 75 (2023)</a> </p> <p>DOI: <a href="http://doi.org/10.1140/epja/s10050-023-00977-x">https://doi.org/10.1140/epja/s10050-023-00977-x</a></p> <p> </p> <p> </p> <p> </p>
Neutrino asymmetry evolution + BBN | Public grids
<h3>Datasets associated to “Constraints on primordial lepton asymmetries with full neutrino transport”, J. Froustey and C. Pitrou [2405.06509].</h3> <p>We solve the neutrino Quantum Kinetic Equations with the full collision term in the range of temperatures [25 MeV, 0.006 MeV], in order to get the evolution of neutrino distributions. We explore a range of primordial neutrino asymmetries, whose evolution results from a complicated interplay between the Hamiltonian terms (in particular, the self-interaction mean-field) and collisions. The output of this neutrino calculation is used in the Big Bang nucleosynthesis code <em>PRIMAT</em> to determine the primordial abundances obtained in this cosmological scenario.</p> <p> </p> <p>The datasets are respectively:</p> <ul> <li><code>NEVO_PRIMAT_grid_equalxi.csv</code>, used in Section IV.A ;</li> <li><code>NEVO_PRIMAT_grid_xiav_xie.csv</code>, used in Section IV.B ;</li> <li><code>NEVO_PRIMAT_grid_ximu_xitau.csv</code>, used in Section IV.C ;</li> <li><code>NEVO_PRIMAT_grid_xiav_xie_zoom.csv</code>, used in Section V ;</li> <li><code>NEVO_PRIMAT_grid_3D.csv</code>, an additional grid exploring the full 3D parameter space (xi_e,xi_mu,xi_tau).</li> </ul> <p>Each dataset has the same format, with 21 columns identified in the header of each file:</p> <ol> <li>xit_av, average of the initial xitilde (= xi + xi^3/pi^2)</li> <li>xit_e - xit_av, initial difference between the e flavor xitilde and the average</li> <li>(xit_mu - xit_tau)/2, also called \tilde{Delta}, difference between the mu and tau flavor initial asymmetries</li> <li>xi_e, reduced initial chemical potential of nu_e (opposite one for nu_ebar)</li> <li>xi_mu</li> <li>xi_tau</li> <li>N_eff, effective number of neutrino species after decoupling (i.e., for T = 0.006 MeV)</li> <li>Y_He4, primordial helium-4 abundance obtained from PRIMAT (with omega_baryon = 0.02242)</li> <li>D/H, primordial deuterium abundance obtained from PRIMAT (with omega_baryon = 0.02242)</li> <li>d[ln(Y_He4)]/d[ln(omega_b)] = dY, relative derivative such that Y_He4(omega) = Y_He4^ref * (1+dY*(omega-omega^ref)/omega^ref)</li> <li>d[ln(D/H)]/d[ln(omega_b)], same for D/H</li> <li>eta^f_e, final electron flavor asymmetry, defined as eta_e = (n_nue - n_nuebar)/Tcm^3</li> <li>eta^f_mu, final muon flavor asymmetry</li> <li>eta^f_tau, final tau flavor asymmetry</li> <li>eta^f_1, final asymmetry of the nu_1 mass eigenstate</li> <li>eta^f_2, final asymmetry of the nu_2 mass eigenstate</li> <li>eta^f_3, final asymmetry of the nu_3 mass eigenstate. <em>These "mass asymmetries" are determined using the fact that the final density matrix is diagonal in the mass basis, which allows to relate {eta_alpha}_(flavor) to {eta_i}_(mass). Note that we take the mean values of each mixing parameter from the <a href="https://pdg.lbl.gov/2023/tables/rpp2023-sum-leptons.pdf" target="_blank" rel="noopener">Particle Data Group (2023)</a>, neglecting the CP-phase.</em></li> <li>rho^f_e, final comoving energy density of nu_e + nu_ebar</li> <li>rho^f_mu, final comoving energy density of nu_mu + nu_mubar</li> <li>rho^f_tau, final comoving energy density of nu_tau + nu_taubar</li> <li>z^f, final dimensionless photon temperature (z = T_gamma/T_cm)</li> </ol>
Neutrinos from Beta Processes in a Presupernova: Probing the Isotopic Evolution of a Massive Star
<p>We present datasets for neutrino luminosity, differential in neutrino energy, of 15 <span class="math-tex">\(M_{\odot}\)</span>and 30 <span class="math-tex">\(M_{\odot}\)</span> presupernova stars at various times during the stellar evolution. We include here the total luminosity from both pair production and beta processes. The beta process neutrino luminosities are also split into contributions from individual isotopes. For more information, please see the attached README.txt file.</p>
Anomaly-free, flavour-dependent U(1) charge assignments for Standard Model/Standard Model plus three right-handed neutrino fermionic content
<p>We present lists of anomaly-free charge combinations up to a maximum magnitude charge Qmax given by the number at the end of the filename. Filenames beginning "SMcharges" are for the Standard Model fermion content, whereas "SMnuRcharges" are for Standard Model plus three right-handed neutrino fermion content. Use the bunzip2 program to unpack the larger files with a bz2 suffix.</p> <p>The files searchU1.cpp and searchU1.h contain C++ files (in the 2014 standard) to produce the solutions. runme.sh is a bash script that compiles the programs and then runs it several times, once to produce each file.</p> <p>filterNeut.cpp contains an example program that reads in one of the solution lists, applies a filter to it, and only prints out solutions that satisfy the filter.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/1812.04602</p> <p> </p>
Dataset for Probing Majorana neutrinos with double-β decay
<p>This dataset includes the plots in the publication "Probing Majorana neutrinos with double-β decay" and its supplementary materials.</p>
MicroBooNE BNB Electron Neutrino Overlay Sample (With Wire Info)
<p>MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.</p> <p>This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is restricted to charged-current electron neutrino interactions within the argon active volume of the time projection chamber.</p> <p>The HDF5 files in this sample include the information at the wire waveform level (after deconvolution and finding of regions of interest). As this information significantly increases the file size, this sample contains about 20% of the events of the corresponding sample without wire information.</p> <p>More documentation, including detailed description of content, recipes, and example usage, at <a href="https://github.com/uboone/OpenSamples/tree/v01">https://github.com/uboone/OpenSamples</a>.</p> <p>Suggested text for acknowledgment is the following:<br> <em>We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].</em></p> <p>In addition, we request that software products resulting from the usage of the datasets are also made publicly available.</p>
ScienceDex guides
Understand access before you commit
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.