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71 results for “neutrino”
MicroBooNE BNB Electron Neutrino Overlay Sample (No 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 do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.</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>
Dataset: Evaluating approximate asymptotic distributions for fast neutrino flavor conversions in a periodic 1D box
<p>In this data package we include two txt files and two zip files.</p> <p>The file Gaussian.txt contains 4 columns. The first column shows the indices of the file numbers contained in the file Gaussian_100.zip. The second, third, and fourth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>The file Maxent.txt contains 5 columns. The first column shows the indices of the file numbers contained in the file Maxent_100.zip while the second column lists the corresponding indices in the file Gaussian.txt. The third, fourth, and fifth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>Files Gaussian_100 and Maxent_100 give the asymptotic simulated outcomes of fast neutrino flavor conversions with the initial Gaussian and maximum-entropy distributions, respectively. Each data file xxxx.dat lists four columns for <span class="math-tex">\(v_z\)</span>, <span class="math-tex">\(P_{ee}(v_z)\)</span>, <span class="math-tex">\(g_\nu(v_z)\)</span>, and <span class="math-tex">\(g_{\bar\nu}(v_z)\)</span>, respectively. We take 100 <span class="math-tex">\(v_z\)</span> grids for all data.</p>
Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited
<p>Reproduction Package for the Paper "Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited".</p> <p><strong>File Organization</strong></p> <ul> <li>MESA: MESA modifications to include losses due to the neutrino magnetic dipole moment, scripts to run the grid of models, and post processing pipeline scripts including the Worthey \& Lee bolometric correction code.</li> <li>ML_models: Machine learning code to train and use the models as well as the models themselves.</li> <li>analysis: Plotting code to create figures for papers and presentations.</li> <li>makeGrids: Scripts to create the different input grid files to run MESA on.</li> <li>mcmc: Scripts and plots for the MCMC analysis.</li> <li>mesa_data: All MESA models generated in this project.</li> <li>environment.yml: Conda environment for analysis and the mcmc. </li> </ul> <p>More details can be found in the README files within each directory.</p> <p><strong>Citation Policy</strong><br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers:</p> <ul> <li>This paper</li> <li>https://arxiv.org/abs/2303.12069</li> <li>https://arxiv.org/abs/2305.03113</li> <li>Astrophys. J. Suppl. 192, 3 (2011)</li> <li>Astrophys. J. Suppl. 208, 4 (2013)</li> <li>Astrophys. J. Suppl. 234, 34 (2018)</li> <li>Astrophys. J. Suppl. 243, 10 (2019)</li> </ul> <p><strong>Software</strong></p> <p>Python version 3.8, NumPy version 1.22.3, Pandas version 1.4.3, Matplotlib version 3.5.1, Seaborn version 0.11.2, Tensorflow version 2.4.1, corner version 2.2.1, emcee version 3.1.2, MESA version 12778, MESASDK version x86_64-linux-20.3.2.</p>
yosshiida/presnnu: Data for presupernova neutrino spectra
<p>The numerical data of the time variation of the neutrino spectra released from presupernova stars.</p> <p>v1.0: The data of the spectra of neutrinos from 12, 15, and 20 solar-mass during presupernova stage are released.</p> <ul> <li>ytui2016: Yoshida, Takahashi, Umeda, & Ishidoshiro (2016, PRD 93, 123012).</li> </ul>
Neutrino emission by plasmon decay in a strong magnetic field: Data material
<p>This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given. </p>
Particle-in-cell Simulation of the Neutrino Fast Flavor Instability
<p>1D_fiducial.tgz contains the input parameters and simulation output data from the fiducial simulation. Each timestamp with output data is stored in a plt* directory.</p> <p>scripts.tgz contains the data reduction and plotting scripts used in the paper. We require yt to read the data with our scripts.</p> <p>inputs.tgz contains input files for each of the simulations in the paper. The test problem inputs (MSW, bipolar, homogeneous FFI, inhomogeneous FFI) are part of the test suite in Emu.</p> <p>Simulations are run using the version of Emu (v1.1) at http://doi.org/10.5281/zenodo.4423166 or https://github.com/AMReX-Astro/Emu.</p>
A Detailed Comparison of Multi-Dimensional Boltzmann Neutrino Transport Methods in Core-Collapse Supernovae
<p>This dataset contains results of 1D and 2D static neutrino transport calculations using discrete ordinates and Monte Carlo methods, as described in the 2017 paper by the same name. The results include the neutrino momentum space grids used for the simulations, the full spectral and angular distribution functions, energy-dependent angular moments of the distribution functions, and neutrino heating rates. The HDF5 dataset name corresponds to the simulation of the same name in the 2017 paper, Table 1. Also included are the fluid backgrounds and opacities. See the readme for a description of all quantities in the datasets.</p> <p>There are also two simulation setups including all initial conditions and parameters for 1D and 2D Monte Carlo calculations using the open-source code Sedonu. This accompanies Sedonu commit 8bd509b27aa0461df26b194df65b4526d6c153fe.</p>
Datasets and model checkpoints for overlapping-sparse neutrino interaction decomposition
<p>Datasets and model checkpoints corresponding to the article: "Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions." (https://arxiv.org/abs/2310.19695):</p><ul><li><strong>dataset_p.tar.gz</strong>: proton dataset.</li><li><strong>dataset_mu.tar.gz</strong>: muon dataset.</li><li><strong>dataset_D+.tar.gz</strong>: deuterium dataset.</li><li><strong>dataset_T+.tar.gz</strong>: tritium dataset.</li><li><strong>metadata.tar.gz</strong>: datasets metadata.</li><li><strong>checkpoints.tar.gz</strong>: checkpoints for the trained neural network models.</li></ul><p> </p>
Standing wave and neutrino
<p>A solution to Maxwell's equations in the form of a cylindrical wave is given. It is shown that under certain conditions such a wave does not move, but rotates around its axis and can be identified with a stationary but rotating massive cylinder. It is shown that there can be a pair of such cylinders that form a particle consisting of two parts rotating in different directions. It is proposed to consider the neutrino as such a particle. Such a particle has a rest mass, which is consistent with experiments. In contrast, the existing theory states that neutrinos have zero rest mass.</p>
Chaos in Inhomogeneous Neutrino Fast Flavor Instability
<p>Data from paper "Chaos in inhomogeneos fast flavor instability".</p> <p>Dense neutrino gases exhibit complex flavor oscillations due to coherent forward scattering among neutrinos. The potentially chaotic nature of these oscillations challenges the prediction of neutrino flavor transformations in simulations. By studying small flavor perturbations in a small region within a NSM and a simplified neutrino distribution, we observe chaotic paths in the flavor state space, making micro-scale neutrino flavor changes unpredictable. However, this chaos has minimal impact on the stability of the domain-averaged neutrino density matrix, indicating that despite exponential errors, domain-averaged quantities remain reliable.</p> <p>For information regarding the data read the "information.txt" files in "nsm.tar.gz" and "fiducial.tar.gz".</p>
Data release for "The T2K Neutrino Flux Prediction" (2013)
<p>This data release is superseded by the<a href="https://zenodo.org/record/5734267#.YaYEsFOnxsE"> flux release in 2016</a>.<br><br>The files in the following archive file contain the neutrino beam flux predictions for the T2K ND280 (near) and Super-Kamiokande (far) detectors. The description of the flux predictions is published in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.87.012001">Phys. Rev. D.87.012001</a> (<a href="https://arxiv.org/abs/1211.0469">arxiv.org:1211.0469 [hep-ex]</a>). The provided flux predictions include no neutrino oscillations. This tar file contains the flux predictions as well as a README.pdf file with detailed information on the included files.</p>
Supernova Neutrino Light Curves from Proto-Neutron Star Cooling with Various Nuclear Equation of State
<p>We present the model spectra of neutrinos emitted from proto-neutron star (PNS) cooling used in Nakazato et al., <a href="https://doi.org/10.3847/1538-4357/ac3ae2">Astrophys. J. <strong>925</strong> (2022) 98</a>, <a href="https://arxiv.org/abs/2108.03009">arXiv:2108.03009 [astro-ph.HE]</a>. So as to obtain the time evolution of neutrino spectra, PNS cooling simulations are performed with use of four nuclear equation of state (EOS) models and eight PNS cooling models with different initial conditions are involved for each EOS. The format of the spectral data is the same with that of <a href="http://asphwww.ph.noda.tus.ac.jp/snn/">Supernova Neutrino Database</a>. For the details, see readme.pdf</p>
Effects of variations of SUSY breaking scale on neutrino parameters at low energy scale under radiative corrections
<p>The paper addresses the effects of the variations of the SUSY breaking scale $m_s$ in the range (2-14) TeV on the three neutrino masses and mixings, in running the renormalization group equations (RGEs) for different input values of high energy seesaw scale $M_R$, in both normal and inverted hierarchical neutrino mass models. The present investigation is a continuation of the earlier works based on the variation of $m_s$ scale. Two approaches are adopted one after another - bottom-up approach for running gauge and Yukawa couplings from low to high energy scale, followed by the top-down approach from high to low energy scale for running neutrino parameters defined at high energy scale, along with gauge and Yukawa couplings. A self-complementarity relation among three mixing angles is also employed in the analysis. Significant effect due to radiative corrections on neutrino parameters with the variation of SUSY breaking scale $m_s$, is observed.</p>
Data release for the paper "Measurements of protons and charged pions emitted from the $\nu_{\mu}$ charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector"
<p>This data release is associated with the paper "Measurements of protons and charged pions emitted from the <span class="math-tex">\(\nu_{\mu}\)</span> charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector". It is currently available on <a href="http://arxiv.org/abs/2203.08367">arXiv:2203.08367</a> and to be submitted to Phys. Rev. D.</p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p>The provided zip file contains the data as below.</p> <ol> <li>event.root: Event by event information of 183 iron-target interactions.</li> <li>plot.root: Plot information as shown in the paper.</li> <li>detector_efficiency.root: Detectrion efficiencies for muons, charged pions, and protons.</li> <li>momentum_resolution.root: Relation between true and reconstructed momentum for muons, charged pions, and protons.</li> <li>misPID.root: Mis-PID rates of protons and pions.</li> <li>syscov.root: Covariance matrices of systematic uncertainties.</li> <li>flux.root: The neutrino flux and the covariance matrix of the flux error.</li> </ol> <p>The zip file also contains a README.pdf file with detailed information on the included files. Please read it.</p>
Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"
<p>This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>.<br><br>The data release contains:</p> <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>
Data Release for "First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data"
<p>This archive contains the electronic version in ROOT and pdf formats of the measurements of oscillation parameters obtained with the analyses from the paper “First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data”.<br><br>It is published in <a href="https://doi.org/10.1103/PhysRevLett.134.011801">Physical Review Letters</a> and is available on the <a href="https://arxiv.org/abs/2405.12488">arXiv:2405.12488 [hep-ex]</a>. </p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>This release includes the results of the measurements of the different oscillation parameters obtained with the four analyses appearing in the paper. This corresponds to various 1D and 2D DeltaChi^2 and posterior probability maps as well as 2D confidence/credible regions for the parameters sin^2(theta13), sin^2(theta23), dm^2_32/|dm^2_31|, delta_cp, J_cp.</p> <p>The results are separated into different files for the four analyses. Additional details on these analyses can be found below, but two of them use a Bayesian approach (producing posterior probabilities and credible intervals/regions) and two of them follow a frequentist approach (producing DeltaChi^2 maps and confidence intervals/regions). A tag in the TGraph and histogram names also allow to differentiate the different types of intervals/regions: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. The tag “posterior” indicates that the object corresponds to a posterior probability distribution, while “chi2” indicates a DeltaChi^2 one.</p> <p>Results for each mass ordering hypothesis are provided, denoted "NO" for normal ordering and "IO" for inverted ordering. The Bayesian files also include results marginalised over the mass ordering, denoted by the tag "both" in the object names. The frequentist files include results profiled over the mass ordering, indicated by a tag “profMO” in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted ordering: the Bayesian results are in term of #Deltam^{2}_{32} for both NO and IO, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NO, and |#Deltam^{2}_{31}| for the IO. </p> <p>A constraint on theta13 from reactor experiment measurements is used for all results in this release. It corresponds to the value in the PDG 2019 review: sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}. This is commonly referred to as "the reactor constraint", and a tag “wRC” is included in the name of the different objects as a reminder that it is used for these results.</p> <p>**************************************<br>***** Brief descriptions of the four analyses<br>**************************************<br>Results are provided for the four analyses mentioned in the “Oscillation analysis” part of the paper. They were given names (Bayesian1, Bayesian2, Frequentist1, Frequentist2) based on the statistical approach they follow.</p> <p>The Bayesian analyses are based on the two T2K analyses described in Eur. Phys. J. C 83, 782 (2023), extended to include the Super-Kamiokande atmospheric data, and with modifications to use the model described in the paper to which the present release is attached to. These analyses use Markov Chain Monte Carlo methods to compute marginal likelihoods for the parameter of interests. </p> <p>For the frequentist analyses, Frequentist1 is a modified version of Bayesian1, optimized for speed to be able to address the computational challenges of producing frequentist results from an ensemble of pseudo-experiments. Frequentist2 is based on the Super-Kamiokande atmospheric analysis described in PTEP 2019, 053F01 (2019), extended to include the T2K data, and also with modifications to follow the model described in the paper. These two analyses compute profile likelihood on a grid of oscillation parameters of interest to produce measurements of these parameters.</p> <p>In terms of the differences between analyses mentioned in the paper, Bayesian2 is the analysis that does a simultaneous fit of the T2K near detector data with the events observed at SK, and the one for which the momentum scale uncertainty is not correlated between the atmospheric and T2K events observed at SK. The three other analyses use a covariance matrix to propagate the constraint on systematic uncertainties from T2K near detector data to the analysis of the events observed at SK, and treat the momentum scale uncertainty as correlated between atmospheric and T2K far detector events.</p> <p>**************************************<br>***** Example codes<br>**************************************<br>Example codes are provided for each of the four analyses, showing how to produce the pdf file from this analysis from the corresponding ROOT file. How to run these example codes is indicated in the comments at the start of each of the example files.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass ordering tag.</p> <p>For the Bayesian results, there is an additional tag to indicate if the results was obtained with a prior probability uniform in deltaCP (“flatdcp”) or uniform in sin(deltaCP) (“flatsindcp”)</p> <p>A glossary is provided at the end of this readme.</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form:<br>gr2D_varX_varY_wRC_<NO,IO,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). <br>N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).</p> <p>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and therefore have only approximate coverage. However, the {sin^2(theta_23), deltaCP} confidence regions of analysis Frequentist1 were built using critical DeltaChi^2 values computed with the Feldman-Cousins method. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_wRC_<NO,IO,both>_bestfit</p> <p>The best fit markers and contour lines are generally for each MO *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass ordering considered. There are some exceptions, in particular some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MO to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMO" in its name to distinguish it from the others.</p> <p><br>**************************************<br>*** 1D and 2D histograms<br>**************************************<br>Objects of the form<br>h1D_var_<chi2,posterior>_wRC,_<NO,IO, both, profMO><br>h2D_var1_var2_<chi2,posterior>_wRC_<NO,IO, both><br>are respectively TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var" or TH2D for the couple of parameters (var1, var2)</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass ordering:<br>- DeltaChi^2 plots use a global minimum over both hierarchies<br>- Posterior probability plots integrate to unity *individually*</p> <p>**************************************<br>***** Critical values for frequentist results<br>**************************************<br>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP <br>grCritical_{variable}_chi2_wRC_{NO,IO,profMO}_conf{68, 90, 955}<br>variable: th23, dCP</p> <p>The FC-corrected confidence intervals for these 2 variables can be obtained as the region for which the corresponding 1D DeltaChi^2 histogram is below the grCritical graph of a given level.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the mass ordering is given by the sign:<br> dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br> dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties.</p> <p>Plots with "_bestfit" appended indicate the point in the 2D parameter space (marginalized over the other parameters) with the highest posterior density, and is not necessarily the global minimum of the likelihood.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce highest posterior credible intervals, which is the kind of credible intervals reported in the paper. </p> <p>**************************************<br>***** Glossary of tags used in objects names<br>**************************************</p> <p>"wRC" - Uses “reactor constraint” on theta13, sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}<br>"FC" - Feldman-Cousins<br>"NO" - Normal mass Ordering<br>"IO" - Inverted mass Ordering<br>"both" - Marginalised over normal and inverted mass orderings<br>"profMO" - Profiled over normal and inverted mass orderings<br>"cred" - Credible interval<br>"conf" - Confidence interval<br>"68" - 68.3% (1 sigma)<br>"90" - 90%<br>"955" - 95.5% (2 sigma)<br>"997" - 99.7% (3 sigma)<br>"chi2" - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed using Feldman-Cousins method<br>"th13" - sin^2(theta_13)<br>"th23" - sin^2(theta_23)<br>"dCP" - delta CP<br>"dm2" - Delta m^2_{23} (NO), |Delta m^2_{13} (IO)| for confidence intervals; used for frequentist analyses results.<br>"dm32" - Delta m^{2_{23} regardless of mass ordering; in the Bayesian analyses, Delta m^2_{23} is always the variable that is plotted.<br>"jarlskog" - Jarlskog invariant<br>"flatdcp" - Using prior probability uniform in deltaCP<br>"flatsindcp" - Using prior probability uniform in sin(deltaCP)</p>
Presupernova neutrinos: realistic emissivities from stellar evolution
<p>MESA inlists associated with <a href="http://Presupernova neutrinos: realistic emissivities from stellar evolution">Presupernova neutrinos: realistic emissivities from stellar evolution</a></p>
Neutrino Quantum Kinetics in Compact Objects
<p>This contains the codebase and data from "Neutrino Quantum Kinetics in<br> Compact Objects" by Sherwood Richers, Gail McLaughlin, Alexey<br> Vlasenko, and James P. Kneller. (2019, submitted to PrD)</p> <p>======================<br> All f.h5 file contents<br> ======================<br> r(cm) - proxy for the time (r=ct)<br> dr_block(cm) - proxy for the block timestep (dr=cdt). Stored for<br> recovery purposes<br> dr_int(cm) - proxy for the interaction integration timestep<br> (dr=cdt). Stored for recovery purposes.<br> dr_osc(cm) - proxy for the oscillation integration timestep<br> (dr=cdt). Stored for recovery purposes.<br> fmatrixf - dimensionless distribution function. Accessed with<br> [time][helicity][energy][flavor1][flavor2][0=real,1=imaginary]</p> <p>==================<br> Directory contents<br> ==================</p> <p>fiducial - simulation output from the fiducial case of a background<br> with rho=1e10g/ccm, T=10MeV, Ye=0.3, the HShen EOS, including<br> oscillations, neutrino absorption on nucleons and nuclei, inelastic<br> electron scattering, elastic nucleon scattering, electron-positron<br> pair production, effective-absorption nucleon-nucleon bremsstrahlung,<br> and neutrino-neutrino scattering and pair processes. Using 25 energy<br> bins covering up to 100 MeV.</p> <p>noosc_full - simulation output from the fiducial case with the same<br> circumstanses as above, except without oscillations and using 50<br> energy bins up to 100 MeV.</p> <p>noosc_supernova_background.dat - snapshot from a 1D core-collapse<br> supernova simulation. The only relevant<br> quantities are the radius (col. 2), density<br> (col. 3), electron fraction (col. 4),<br> temperature (col. 5), electron chemical<br> potential (col. 9), proton chemical potential<br> (col. 10), and neutron chemical potential<br> (col. 11)<br> <br> noosc_*_tdecohere.dat - decoherence time (col. 4) for neutrinos at<br> every radial point (col. 1), each helicity<br> (col. 2), and every energy (col. 3), using 50<br> energy bins at integer multiples of 2 MeV.</p> <p> full - calculations done including the full set of<br> collision interactions listed in fiducial.</p> <p> nonu4 - calculations done including the full set of<br> collision interactions listed in fiducial<br> except neutrino-neutrino scattering/pair<br> processes.</p>
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Three-dimensional simulations of rapidly rotating core-collapse supernovae: finding a neutrino-powered explosion aided by non-axisymmetric flows
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Suwa 2016 Monthly Notices of the Royal Astronomical Society: Letters, Volume 461, Issue 1, p.L112-L116.</p> <p> </p> <p> </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.