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104 results for “stars: neutron”
MCMC samples for X-ray spectra fits summarised in the paper "A strangely light neutron star"
<p>Posterior samples for NS mass and radius described based on fitting of XMM-Newton and Suzaku spectra of the CCO in HESS J1731-347 and described in the paper the paper "A strangely light neutron star" (DOI: <a href="https://doi.org/10.21203/rs.3.rs-1509469/v1">10.21203/rs.3.rs-1509469/v1</a>). Two files uploaded contain posterior samples based based </p> <p>a) on fitting X-ray data alone using single temperature carbon atmosphere model and Gaia parallax priors (xray_only_carbatm.txt)</p> <p>b) on fitting the same X-ray data but including also all additional priors described in the main text and full priors on distance rather than inverted parallax value (full_priors_carbatm.txt). Note that initial version contained wrong file uploaded by error, so the correct file to use in this case is full_priors_carbatm_corr.txt. We urge, however, to use a) as a baseline, i.e. as input for incorporating of other constraints for two reasons: first, the procedure of incorporating other constraints adopted by us and you may be different, and second, this file contains relatively small number of samples as it was mainly meant as illustration putting our results in context of other constraints and to give an idea of impact which our measurement has on EOS selection for a particular family of EOSs, so the result would be different if other set of EOSs is considered. </p> <p>in addition a file with updated weights for EOSs used by Dietrich et al 2021 (2020Sci...370.1450D, available on https://github.com/diettim/NMMA) which take into the account constrains from X-ray spectral fitting presented in our work for the CCO and constrains based on modeling of the 4U 1702-429 bursts reported by Nattila et al 2017 (2017A&A...608A..31N) are included in file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_cco_weights.txt. This file is based on and has the same syntax as file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_weighting.dat provided by Dietrich et al 2021 available on https://github.com/diettim/NMMA along with the corresponding tabulated EOS files.</p> <p> </p>
Equation of state for simulations of core-collapse supernovae and neutron-star mergers
<p>We construct a new equation of state (EOS) for numerical simulations of core-collapse supernovae and neutron-star mergers based on an extended relativistic mean-field model with a small symmetry energy slope L, which is compatible with both experimental nuclear data and recent observations of neutron stars. The new EOS table (EOS4) based on the extended TM1 (TM1e) model with L=40 MeV is designed in the same tabular form as the commonly used Shen EOS (EOS2) based on the original TM1 model with L=110.8 MeV. This is convenient and useful for performing numerical simulations and examining the influences of symmetry energy and its density dependence on astrophysical phenomena. </p> <p> </p>
Dataset from: Common–Envelope Episodes that lead to Double Neutron Star formation
<p>The results of all simulations shown in "Common–Envelope Episodes that lead to Double Neutron Star formation" (<a href="https://arxiv.org/abs/2001.09829">arXiv:2001.09829</a>)</p> <p>Contents:</p> <p>COMPASOutput.h5<br> MATLABscripts.zip<br> README</p> <p>All simulations made using <a href="https://compas.science/">COMPAS</a> (internally referred to as COMPAS Legacy).</p> <p>If you use these data please kindly include a citation to:<br> A. Vigna-Gómez, M. MacLeod, C. J. Neijssel, F. S. Broekgaarden, S. Justham, G. Howitt, S. E. de Mink, S. Vinciguerra, and I. Mandel. Common envelope episodes that lead to doubleneutron star formation. PASA, 37:e038, Jan. 2020 (<a href="https://ui.adsabs.harvard.edu/abs/2020PASA...37...38V/abstract">ADS</a>)</p>
Dataset from: Fallback Supernova Assembly of Heavy Binary Neutron Stars and Light Black Hole-Neutron Star Pairs and the Common Stellar Ancestry of GW190425 and GW200115
<p>The results of the simulations shown in "Fallback Supernova Assembly of Heavy Binary Neutron Stars and Light Black Hole-Neutron Star Pairs and the Common Stellar Ancestry of GW190425 and GW200115" (<a href="https://arxiv.org/abs/2106.12381">arXiv:2106.12381</a>).</p> <p>Contents:</p> <ol> <li>Run_Details_COMPAS</li> <li>COMPAS_Output_*.hdf5</li> <li>MESA.zip</li> <li>GADGET.zip</li> </ol> <p>If you use any of these data please kindly include a citation to:<br> Alejandro Vigna-Gómez <em>et al</em> 2021 <em>ApJL</em> <strong>920</strong> L17 <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ac2903">doi:10.3847/2041-8213/ac2903</a></p> <p>If you use the MESA profile or history files please also cite:</p> <p>Aguilera-Dena, D.R., et al., in prep.</p> <p>Additionally, we point the reader towards the following GitHub repositories:<br> 1) <a href="https://github.com/aldobatta/fallback-supernova">aldobatta/fallback-supernova</a><br> 2) <a href="https://github.com/avigna/heavy-BinaryNeutronStars">avigna/heavy-BinaryNeutronStars</a></p>
Ensembles for neutron-star-matter equation-of-state interpolations
<p>Ensembles for neutron-star-matter equation-of-state interpolations to reproduce the figures in arXiv:2303.11356</p>
Output from paper: The s process in massive stars, a benchmark for neutron capture reaction rates
<p>Title: "The s process in massive stars, a benchmark for neutron capture reaction rates"; Authors: Marco Pignatari, Roberto Gallino, Rene Reifarth</p><p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p><p>Content: tar.gz package including a README file and two folders. The folders contain all the abundance plots associated to the work Pignatari, Gallino & Reifarth, 2023 The European Physical Journal A, Special Issue on: 'From reactors to stars' in honor of Franz Kaeppeler. </p>
Dataset: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model
<p>Codes and figures for the paper: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model</p>
Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA
<p>We present supplementary datasets accompanying our publication<em> </em><a href="https://arxiv.org/abs/2310.06559">Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA</a>.<em> </em>These catalogues reperesent the Galactic population of double white dwarf (DWD) and neutron star - white dwarf (NSWD) binaries emitting gravitational waves (GWs) in the <em>Laser Interferometer Space Antenna</em> (LISA) frequency band (0.1 mHz - 1 Hz). The catalogues have been constructed based on binary evolution models <a href="https://arxiv.org/abs/1208.6446">Toonen et al. 2012</a> for DWDs and <a href="https://arxiv.org/abs/1804.01538">Toonen et al. 2018</a> for NSWD binaries, obtained using SeBa binary population synthesis code.</p> <p><strong>Data contents</strong></p> <p>The dataset consists of <strong>12 catalogues </strong>representing Galactic populations of NSWD and/or DWD binaries, which are expected to be the most numerous types of binaries amongt LISA's Galactic sources. Each catalogue is distinguished by its model ID, which specifies the presence of NSWD and/or DWD binaries, the CE model used, CE efficiency values, and the NS natal kick prescription applied (see table below).</p> <p>Each catalogue is structured to describe a binary systems with the following attributes:</p> <ul> <li><strong>Name*</strong>: binary identifier; this consist of a prefix indicating the binary type (<code>'MW_DWD'</code> for a DWD binary, <code>'MW_NSWD_ecc0'</code> for a circular NSWD bianry, or <code>'MW_NSWD_ecc1'</code> for an eccentric NSWD binary) followed by a unique ID number. For example, <code>'MW_DWD 28713637'</code>.</li> <li><strong>Frequency</strong>: present-day GW frequency (Hz).</li> <li><strong>Frequency Derivative</strong>: rate of change of GW frequency over time (Hz^2).</li> <li><strong>Ecliptic Latitude</strong>: in radians (rad).</li> <li><strong>Ecliptic Longitude</strong>: in radians (rad).</li> <li><strong>Amplitude</strong>: GW amplitude (dimensionless).</li> <li><strong>Inclination</strong>: angle between the binary's orbital plane and our line of sight, in radians (rad).</li> <li><strong>Polarization</strong>: Orientation of the GW's polarization, in radians (rad).</li> <li><strong>Initial Phase</strong>: initial phase (rad).</li> <li><strong>Eccentricity</strong>: orbital eccentricity (dimensionless).</li> </ul> <p><strong>*</strong>Note that the <strong>Name </strong>field for eccentric NS+WD binaries (staring with <code>'MW_NSWD_ecc1'</code>) is not unique because these binaries are represented by multiple harmonics sharing the same name ID. The number of harmonics included varies for each binary to ensure that at least 99% of the binary's total GW power is represented. Thus, for each binary, we added harmonics incrementally until this threshold is reached.</p> <table> <tbody> <tr> <td>Model ID</td> <td>WD+WD</td> <td>NS+WD</td> <td>CE model</td> <td>CE efficiency</td> <td>NS natal kick</td> </tr> <tr> <td>1_0</td> <td>Yes</td> <td>No</td> <td>αα</td> <td>αλ=2.00</td> <td>N/A</td> </tr> <tr> <td>1_1</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Verbunt</td> </tr> <tr> <td>1_2</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Arzoumanian</td> </tr> <tr> <td>1_3</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Hobbs</td> </tr> <tr> <td>1_4</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Blaauw</td> </tr> <tr> <td>2_0</td> <td>Yes</td> <td>No</td> <td>αα2</td> <td>αλ=0.25</td> <td>N/A</td> </tr> <tr> <td>2_1</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Verbunt</td> </tr> <tr> <td>2_2</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Arzoumanian</td> </tr> <tr> <td>2_3</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Hobbs</td> </tr> <tr> <td>2_4</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Blaauw</td> </tr> <tr> <td>3_0</td> <td>Yes</td> <td>No</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>N/A</td> </tr> <tr> <td>3_1</td> <td>Yes</td> <td>Yes</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>Verbunt</td> </tr> </tbody> </table> <p> </p> <h4><strong>Citing the Dataset</strong></h4> <p>When utilising these catalogues in your research, please cite <a href="https://arxiv.org/abs/2310.06559">Korol et al. 2024.</a> We also note our companion data-analysis-focused paper <a href="https://arxiv.org/abs/2310.06568">Moore et al. 2024</a>.</p>
Observation of Gravitational Waves from the Coalescence of a 2.5-4.5 Msun Compact Object and a Neutron Star --- Data Release
<p>This data release contains the analysis results and data behind the figures of the GW230529 discovery paper (<a href="https://urldefense.com/v3/__https://dcc.ligo.org/LIGO-P2300352/public/__;!!Dq0X2DkFhyF93HkjWTBQKhk!W4i4x3JfGgemcFsnnEYP5qxiknddvrG1LWpTLjs_JGK907kTrEBkS8o6i5T6RUFMX0v04jCPhtTq9K2SLcv_4g$" target="_blank" rel="nofollow noreferrer noopener">https://dcc.ligo.org/LIGO-P2300352/public/</a>). Strain data for this event (the L1:GDS-CALIB_STRAIN_CLEAN_AR channel) can be downloaded on GWOSC (<a href="https://doi.org/10.7935/6k89-7q62" target="_blank" rel="noopener">https://doi.org/10.7935/6k89-7q62</a>).</p> <p>The PESummary metafile containing the parameter estimation posterior samples for all analyses performed in the paper (<strong>posterior_samples.h5</strong>) and skymap fits file (<strong>skymap_combined_PHM_high_spin.fits</strong>) for the preferred parameter estimation analysis (high-spin, combined samples using binary black hole waveforms) can be downloaded directly as individual files.</p> <p>The other analysis results are grouped by type: rates, populations, searches, and tidal. The <strong>figure_scripts.tar.gz</strong> file contains all the paper figures in jpeg format along with a Jupyter notebook to reproduce them and additional required helper scripts. Example code for working with the individual result files is given in the <strong>PaperPlots.ipynb</strong> notebook included in this tar file.</p> <p>In brief, the <strong>rates.tar.gz</strong> file contains two files that each include a subset of the rates probability distributions shown in Fig. 3 of the paper. The <strong>populations.tar.gz</strong> file contains all the data behind Figs. 4-8, with subdirectories for each of the three population analyses considered in the paper: Binned Gaussian Process, NSBH-pop, and Power-Law + Dip + Break. In addition to the data behind the figures, the Power-Law + Dip + Break subdirectory additionally includes two *result.json files for the hyper-parameter posterior samples. These files have the same format as the corresponding NSBH-pop *result.json files and can be manipulated in the same way, as shown in the figures notebook.</p> <p>The <strong>searches.tar.gz</strong> file contains the data behind Figs. 9-11 for each of the three search pipelines whose results are included in the paper. Finally, the <strong>tidal.tar.gz</strong> file contains the four probability distributions plotted in Fig. 14. All other figures are produced only using the posterior_samples.h5 file.</p>
Data for 'Cosmology with Binary Neutron Stars: Does Mass-Redshift Correlation Matter?'
<p>The code is available at the following GitHub link: https://github.com/SoumendraRoy/RedevolBNS</p> <p>To generate the plots in the paper, see: https://github.com/SoumendraRoy/RedevolBNS/tree/main/Make_Plots</p> <p>A frozen version of the Make_Plots is included here.</p> <p>Description of the files:</p> <ol> <li><a href="https://zenodo.org/api/records/14704635/draft/files/Cosmo_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Cosmo_Plots.ipynb</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/Pop_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Pop_Plots.ipynb</a> : Jupyter notebooks containing all plots in the main text.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/Appendix_Plots.ipynb/content" target="_blank" rel="noopener noreferrer">Appendix_Plots.ipynb</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/All_Contours.ipynb/content" target="_blank" rel="noopener noreferrer">All_Contours.ipynb</a> : Jupyter notebooks containing all plots in Appendix.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_marginal_fiducial.h5/content" target="_blank" rel="noopener noreferrer">inference_marginal_fiducial.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_fiducial.h5/content" target="_blank" rel="noopener noreferrer">inference_full_fiducial.h5</a> : The samples of the Hubble constant and dark matter density for the fiducial injected population, with uncorrelated and correlated mass-redshift populations, respectively.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_marginal_MM.h5/content" target="_blank" rel="noopener noreferrer">inference_marginal_MM.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_MM.h5/content" target="_blank" rel="noopener noreferrer">inference_full_MM.h5</a> : The samples of the Hubble constant and dark matter density for the Mandel-Müller injected population, with uncorrelated and correlated mass-redshift populations, respectively.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_result_Uncorrelated.h5/content" target="_blank" rel="noopener noreferrer">inference_full_result_Uncorrelated.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/inference_full_result_Injected.h5/content" target="_blank" rel="noopener noreferrer">inference_full_result_Injected.h5</a> : The samples of the Hubble constant and dark matter density for the fiducial injected population, with uncorrelated and correlated mass-redshift populations, respectively for varying number of detections.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/simulation.h5/content" target="_blank" rel="noopener noreferrer">simulation.h5</a> : The injected mass, redshift samples for different population synthesis variations.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/compare_pop.h5/content" target="_blank" rel="noopener noreferrer">compare_pop.h5</a>, <a href="https://zenodo.org/api/records/14704635/draft/files/variant_pop.h5/content" target="_blank" rel="noopener noreferrer">variant_pop.h5</a> : Comparison of different population synthesis variations.</li> <li><a href="https://zenodo.org/api/records/14704635/draft/files/gmm.h5/content" target="_blank" rel="noopener noreferrer">gmm.h5</a> : Gaussian mixture model fit of the fiducial and Mandel-Müller injected population.</li> </ol>
Data release: Understanding binary neutron star collisions with hypermodels
<pre># Data release for "Understanding binary neutron star collisions with hypermodels" ## Summary For each event, we include sub-directories of the studies performed. In each directory, we provide the `bilby_pipe` configuration (`.ini`) files, `bilby` result file (`.json`). We also provide figures, samples (in the form of `.csv` files), and summary statistics where they are relevant. ## Hypermodel script All results obtained using the *hypermodel* technique use the script `multiwaveform.py`. We include this script at the top-level of this directory. To reproduce results, in each configuration file, replace the `analysis-executable` with the path to this script. ## Software versions All results obtained using * bilby_pipe=1.0.3: (CLEAN) 1220709 2021-05-14 06:28:48 -0700 * bilby=1.1.2: (CLEAN) e5481028 2021-07-05 16:45:01 +0100</pre>
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>
Dataset: High-accuracy simulations of highly spinning binary neutron star systems
<p><strong>Dataset for Gravitational Waveforms for the work of Dudi et al., "High-accuracy simulations of highly spinning binary neutron star systems"; arXiv: 2108.10429 </strong></p> <p> </p> <p><strong>The naming of the files: </strong>EOS_SpinSetup_NumberOfPoints_Mode.dat<br> EOS: SLy<br> SpinSetup: dd - down down; ud - up, down; uu037 -- up, up with dimensionless spin of 0.37; uu057 -- up, up with dimensionless spin of 0.57<br> NumberOfPoints: Points inside the finest level: 96, 144, 192, 240<br> Mode: Only 2,2-mode available <br> </p>
Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"
<p>Posterior samples and code to reproduce all figures associated with <em>Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries</em>.</p> <p>The <code>parameter_estimation</code> folder contains the following:</p> <ul> <li><code>campaigns</code>: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the <code>README</code> inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21. </li> <li><code>fiducial_source_checks</code>: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the <code>golden_binary</code> folder. </li> <li><code>nhat_runs</code>: Various analyses used for Figures 5, 6, B1, and C1. See the <code>README</code> inside for more information. Also see the <code>README</code> in <code>eccentric_gb_scripts</code> and links therein.</li> </ul> <p>Within each parameter estimation output folder there are <code>.dat</code> files for quantities such as the source SNR, log evidence, and posterior. There are also configuration <code>.yaml</code> files which are used by the BALROG code. These contain:</p> <ul> <li><code>lisa_config</code>: Parameters describing the LISA mission, including the duration in seconds. </li> <li><code>nessai_opts</code>: Settings used by nessai (the sampler used in this work). </li> <li><code>priors</code>: Lower and upper limits used for each source parameter. </li> <li><code>sources</code>: Injected values for each source parameter.</li> </ul> <p>The <code>notebooks</code> folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (<code>emin_grid.ipynb</code>), and to inspect analyses in the <code>campaigns</code> and <code>fiducial_source_checks</code> folders.</p> <p>The <code>eccentric_gb_scripts</code> folder contains code to produce Figures 1, 5, B1 and C1. See the <code>README</code> inside for more information.</p>
Data+Analysis+Plotting scripts for "Constraint on the dissipative tidal deformability of neutron stars"
<p>The .zip file contains three directories. </p> <p>1. GW170817-Strain: the raw data (glitch free). Downloaded from https://gwosc.org/events/GW170817/.<br>2. Bilby-Output: the output from running our Bilby sampling scripts. These can be found at https://github.com/JLRipley314/NRTidal-D/tree/main<br>3. Plotting-Scripts: the plotting scripts we used in our paper https://arxiv.org/abs/2312.11659.</p> <p>NOTE: If you want to make sure the plotting scripts work properly, you should download bilby and related dependencies as described in https://github.com/JLRipley314/NRTidal-D/tree/main (or at https://doi.org/10.5281/zenodo.11589416)</p>
Novel modelling of ultracompact X-ray binary evolution - stable mass transfer from white dwarfs to neutron stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017MNRAS.470L...6S/abstract">Novel modelling of ultracompact X-ray binary evolution - stable mass transfer from white dwarfs to neutron stars</a></p>
Light-curve and spectral properties of ultrastripped core-collapse supernovae leading to binary neutron stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.2085M">Moriya et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stw3225">10.1093/mnras/stw3225</a></p>
Constraints on Bygone Nucleosynthesis of Accreting Neutron Stars
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017ApJ...837...73M/abstract">Meisel & Deibel (2017)</a>. MESA version 9575.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa618d">10.3847/1538-4357/aa618d</a></p>
Dataset from: On the formation history of Galactic double neutron stars
<p>The results of all simulations shown in "<a href="http://doi.org/10.1093/mnras/sty2463">On the formation history of Galactic double neutron stars</a>", published in <em>Monthly Notices of the Royal Astronomical Society</em>, Volume 481, Issue 3, December 2018, Pages 4009–4029 (<a href="https://arxiv.org/abs/1805.07974">arXiv</a>).</p> <p>Contents:</p> <p>allDoubleCompactObjects_XX.dat<br> README</p> <p>Where XX is the number of the simulation of interest. XX = {00,01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18}.</p> <p>All simulations made using <a href="http://compas.science/">COMPAS</a></p>
Ultra-luminous X-ray sources and neutron-star-black-hole mergers from very massive close binaries at low metallicity
<p>MESA inlists, run_star_extras, and data associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...604A..55M">Marchant et al. (2017)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201630188">10.1051/0004-6361/201630188</a></p> <p>Files are also available in a gihub repository <a href="https://github.com/orlox/mesa_input_data/tree/master/2016_ULX">here</a></p> <p>Upload includes post-processed simulation output in the files Z-XX.tar.xz, where XX represents the metallicity (Z-25.tar.gz is for a metallicity of log10(Z)=-2.5). Each folder inside the archive corresponds to a single MESA simulation, with the name indicating the value of log10(M_1), q=M2/M1 and the orbital period in days. For example, the directory 1.600_0.500_0.900 corresponds to the simulation with log10(M1/Msun)=1.6, M2/M1=0.5 and Porb=0.9 days. Each folder is also a MESA template folder, containing all input files neccesary to reproduce that individual simulation.</p> <p>The file summary_tables.tar.gz contains summarized information for each simulation in ascii format.</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.