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96 results for “binary star”

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zenodo40/100

Black-hole neutron-star binary simulation SXS:BHNS:0007

Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.

opencc-by-4.0Dec 2018View details →

zenodo40/100

Binary neutron-star simulation SXS:NSNS:0001

Simulation of a neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.

opencc-by-4.0Dec 2018View details →

zenodo40/100

Black-hole neutron-star binary simulation SXS:BHNS:0005

Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.

opencc-by-4.0Dec 2018View details →

zenodo40/100

NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all NSNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the \(\beta\) = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the \(\beta\) = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the \(\beta\) = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the \(\alpha = 0.1\) model (G) </li> <li>alpha0_5.zip, the \(\alpha = 0.5\) model (H) </li> <li>alpha2_0.zip, the \(\alpha = 2.0\) model (I) </li> <li>alpha10_0.zip, the \(\alpha = 10.0\) model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max \(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\) model (M) </li> <li>maxNSmass3_0.zip, the max \(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\) model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the \(\sigma_{\rm{cc}}\)= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the \(\sigma_{\rm{cc}}\)= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 0.1\) (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 5\) (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

zenodo40/100

BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all BHNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the \(\beta\) = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the \(\beta\) = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the \(\beta\) = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the \(\alpha = 0.1\) model (G) </li> <li>alpha0_5.zip, the \(\alpha = 0.5\) model (H) </li> <li>alpha2_0.zip, the \(\alpha = 2.0\) model (I) </li> <li>alpha10_0.zip, the \(\alpha = 10.0\) model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max \(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\) model (M) </li> <li>maxNSmass3_0.zip, the max \(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\) model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the \(\sigma_{\rm{cc}}\)= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the \(\sigma_{\rm{cc}}\)= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 0.1\) (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor \(f_{\rm{WR}} = 5\) (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

zenodo40/100

On the binary origin of Be stars and the nature of exotic Be binary systems

Despite the large impact of massive stars on their environment, important questions concerning their formation, evolution, and final explosions remain unanswered. One of them is the origin of the Be phenomenon, which occurs in ~20% of the early-type OB star. Observationally, Be stars are defined as B-type stars with Balmer line emission, indicative of a circumstellar decretion disk. While the presence of a disk is thought to strongly correlate with rapid rotation of the star, the processes that lead to such high rotation rates are still widely debated. One of the proposed mechanisms for the spin up of Be stars is mass and angular momentum transfer from an initially more massive companion in previous binary interactions. If merging is avoided, the mass donor is now a He-burning stripped star or a compact object. If this is the predominant channel for Be star formation, there are two important implications: first, there should be no Be + main sequence binaries on tight orbits; and second, Be stars should have a stripped star or a compact object as companion (unless the system has been disrupted). Here, we present observational evidence that the binary channel is predominant in Be star formation. We report on a lack of main-sequence companions to massive Be stars, and argue that the few known Be binaries are indeed exotic systems with stripped star or compact companions. We further present new analyses that shed light on the nature and evolutionary status of the two famous Be systems LB-1 and HR 6819 who have recently been reported to contain a black hole companion.

opencc-by-4.0Sep 2021View details →

zenodo40/100

Massive overcontact binaries as progenitors of magnetic massive stars

Recent MHD simulations have demonstrated that magnetic massive stars can be formed via mergers of massive binary systems. This coalescence is preceded by a contact phase, which, while expected to be common, is poorly understood due to a lack of observational constraints: less than ten O-type overcontact binaries are currently known. The nature and degree of internal mixing during the contact phase is extremely important to the final evolutionary outcome of these objects. If the mixing is efficient enough, the stars will enter the Chemically Homogeneous Evolution regime and, instead of expanding as they evolve, the stars may shrink. This pathway has been proposed as a way to form gravitational wave progenitors. If the mixing is less efficient, then these systems may merge, forming objects such as magnetic massive stars, Be stars, LBVs etc. By studying the temperature and chemical abundances, we can determine the degree of internal mixing during this phase, and thus constrain the future evolution. Here, we present a study of several massive overcontact systems in different metallicity regimes. Our findings indicate that, while these systems are rapidly rotating, there is no strong evidence of chemical adjustments on the surface. In fact, the abundances all appear to be consistent with expectations for non-rotating stars. Interestingly, however, all systems show elevated temperatures, and the components of each system lay very close to each other on the HR diagram. This is true even for the unequal mass systems, indicating that the more massive component drives the observed temperature for both components in the system. We show that these results are robust by demonstrating that they are reproducible by a more representative three-dimensional spectroscopic analysis approach as well. Finally, we discuss the implications of our findings for the formation of magnetic massive stars and for massive star evolution in general.

opencc-by-4.0Sep 2021View details →

zenodo40/100

Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"

All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.

opencc-by-4.0Aug 2022View details →

zenodo36/100

All Luminosity Templates Associated with "Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates"

Zip files for the luminosity normalized individual stellar templates, and all combinations of SB2 templates. The templates are in fits format. The first table extension contains the template (wavelength, luminosity, variance, error). These luminosity templates have units of erg /s /angstrom.

opencc-by-4.0May 2020View details →

zenodo36/100

Stars Stripped in Binaries: The Living Gravitational-wave Sources

Binary interaction can cause stellar envelopes to be stripped, which significantly reduces the radius of the star. The orbit of a binary composed of a stripped star and a compact object can therefore be so tight that the gravitational radiation the system produces reaches frequencies accessible to the Laser Interferometer Space Antenna (LISA). Two such stripped stars in tight orbits with white dwarfs are known so far (ZTF J2130+4420 and CD−30°11223), but many more are expected to exist. These binaries provide important constraints for binary evolution models and may be used as LISA verification sources. We develop a Monte Carlo code that uses detailed evolutionary models to simulate the Galactic population of stripped stars in tight orbits with either neutron star or white dwarf companions. We predict 0–100 stripped star + white dwarf binaries and 0–4 stripped star + neutron star binaries with a signal-to-noise ratio >5 after 10 yr of observations with LISA. More than 90% of these binaries are expected to show large radial velocity shifts of \(\gtrsim\)200 km s-1, which are spectroscopically detectable. Photometric variability due to tidal deformation of the stripped star is also expected and has been observed in ZTF J2130+4420 and CD−30°11223. In addition, the stripped star + neutron star binaries are expected to be X-ray bright with LX \(\gtrsim\) 1033–1036 erg s-1. Our results show that stripped star binaries are promising multimessenger sources for the upcoming electromagnetic and gravitational wave facilities.   We provide detailed information about the run presented in Figures 2, 4, and 5 in the file named "entire_population_64.txt". We also provide information about sources with SNR > 4 in "pop_SNR4.txt" for 1000 runs of our standard model. The Jupyter notebook "Reading_stripped_star_compact_object_population.ipynb" reproduces figures of the manuscript and the Jupyter notebook "living_population.ipynb" shows how we model the Galactic population of stripped stars in tight orbit with compact objects.

opencc-by-4.0Oct 2020View details →

zenodo36/100

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

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>). Contents: <ol> <li>Run_Details_COMPAS</li> <li>COMPAS_Output_*.hdf5</li> <li>MESA.zip</li> <li>GADGET.zip</li> </ol> If you use any of these data please kindly include a citation to: Alejandro Vigna-Gómez et al 2021 ApJL 920 L17 <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ac2903">doi:10.3847/2041-8213/ac2903</a> If you use the MESA profile or history files please also cite: Aguilera-Dena, D.R., et al., in prep. Additionally, we point the reader towards the following GitHub repositories: 1) <a href="https://github.com/aldobatta/fallback-supernova">aldobatta/fallback-supernova</a> 2) <a href="https://github.com/avigna/heavy-BinaryNeutronStars">avigna/heavy-BinaryNeutronStars</a>

opencc-by-4.0Jun 2021View details →

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Synthetic J-H-K-W1-W2-W3-G-B-V photometry for random population of binary & single stars

Table of synthetic photometry for ~800k stars, randomly distributed in the following ranges: <ul> <li>mass_A = (0.2, 2.0)</li> <li>log10(age) = (8.0, 9.9)</li> <li>feh = N(0, 0.2)</li> <li>distance = (50, 2000) [pc]</li> <li>A_V = (0, 1)</li> <li>q (mass ratio) = (0.2, 1); mass_B = q * mass_A</li> </ul> The table has columns for all the synthetic photometry as well as columns for the true properties and a boolean column for "is_binary"; the synthetic photometry is only a sum of the two stars if is_binary is True; otherwise the photometry is just for the primary star. Synthetic photometry is done using the MIST model grids, implemented by isochrones (github.com/timothydmorton/isochrones) The purpose of this table is to act a synthetic training set for single/binary classification algorithms.

opencc-by-4.0Oct 2016View details →

zenodo36/100

Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA

We present supplementary datasets accompanying our publication <a href="https://arxiv.org/abs/2310.06559">Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA</a>. These catalogues reperesent the Galactic population of double white dwarf (DWD) and neutron star  - white dwarf (NSWD) binaries emitting gravitational waves (GWs) in the Laser Interferometer Space Antenna (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. Data contents The dataset consists of 12 catalogues 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). Each catalogue is structured to describe a binary systems with the following attributes: <ul> <li>Name*: 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>Frequency: present-day GW frequency (Hz).</li> <li>Frequency Derivative: rate of change of GW frequency over time (Hz^2).</li> <li>Ecliptic Latitude: in radians (rad).</li> <li>Ecliptic Longitude: in radians (rad).</li> <li>Amplitude: GW amplitude (dimensionless).</li> <li>Inclination: angle between the binary's orbital plane and our line of sight, in radians (rad).</li> <li>Polarization: Orientation of the GW's polarization, in radians (rad).</li> <li>Initial Phase: initial phase (rad).</li> <li>Eccentricity: orbital eccentricity (dimensionless).</li> </ul> *Note that the Name 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. <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>   <h4>Citing the Dataset</h4> 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>.

opencc-by-4.0Mar 2024View details →

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Data for 'Cosmology with Binary Neutron Stars: Does Mass-Redshift Correlation Matter?'

The code is available at the following GitHub link: https://github.com/SoumendraRoy/RedevolBNS To generate the plots in the paper, see: https://github.com/SoumendraRoy/RedevolBNS/tree/main/Make_Plots A frozen version of the Make_Plots is included here. Description of the files: <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>

openapache2.0Nov 2024View details →

zenodo36/100

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>

opencc-by-4.0Nov 2021View details →

zenodo36/100

Dataset: High-accuracy simulations of highly spinning binary neutron star systems

Dataset for Gravitational Waveforms for the work of Dudi et al.,  "High-accuracy simulations of highly spinning binary neutron star systems"; arXiv: 2108.10429    The naming of the files:  EOS_SpinSetup_NumberOfPoints_Mode.dat EOS: SLy 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 NumberOfPoints: Points inside the finest level: 96, 144, 192, 240 Mode: Only 2,2-mode available

opencc-by-4.0Feb 2022View details →

zenodo36/100

The cosmic carbon footprint of massive stars stripped in binary systems

# Structure ## Overview The folder data/ contains the inlists and mod files used in this work. Intermediate data (like history or profile) files must be regenerated from the provided files. The folder plots/ contains a jupyter notebook set-up to remake all plots (assuming the data is saved in the data/ folder). There are also additional scripts and files needed to reproduce this work. The griffith.txt file is the data from https://ui.adsabs.harvard.edu/abs/2021arXiv210309837G/abstract and was accessed from https://github.com/giganano/VICE/blob/master/vice/yields/ccsne/S16/W18F/FeH0/v0/explosive/c.dat ## Data folders corehedep - Evolution from ZAMS to end of core helium burning coreodep - Evolution from end of core helium burning to end of core oxygen burning cc - Evolution from end of core oxygen burning up to core collapse ccsn - Evolution from core collapse to shock breakout engmc - Tests variations in the injection energy and mass cut of ccsn explosions spacetime - Test variations in space/time resolution of ccsn explosions ccsn_t_m - Test variations in injection time and injection mass of ccsn explosions ## Sub-folders corehedep/base - Base folder with inlists for this set of models corehedep/binary - Contains a folder for each mass for the binary-stripped stars (11-45) corehedep/single - Contains a folder for each mass for the single stars (11-45) corehedep/net/23 - A single star 23msun model ran with a larger nuclear network coreodep/base - Base folder with inlists for this set of models coreodep/binary  - Contains a folder for each mass for the binary-stripped stars (11-45) coreodep/single - Contains a folder for each mass for the single stars (11-45) coreodep/mesh - Test variations with respect to space and time during carbon burning coreodep/overshoot - Test variations with respect to overshoot during carbon burning coreodep/net - A single star 23msun model ran with a larger nuclear network cc/base - Base folder with inlists for this set of models cc/binary - Contains a folder for each mass for the binary-stripped stars (11-45) cc/single  - Contains a folder for each mass for the single stars (11-45) ccsn/base - Base folder with inlists for this set of models ccsn/binary  - Contains a folder for each mass for the binary-stripped stars (11-45) ccsn/single  - Contains a folder for each mass for the single stars (11-45) ccsn/laplace_binary - Contains core collapse explosions of the binary-stripped models from Laplace et al 2021 ccsn/laplace_single - Contains core collapse explosions of the single star models from Laplace et al 2021 spacetime/base - Base folder with inlists for this set of models spacetime/binary - Test variations in space/time resolution of ccsn explosions ccsn_t_m/base - Base folder with inlists for this set of models ccsn_t_m/binary - Test variations in injection time and injection mass of ccsn explosions engmc/base - Base folder with inlists for this set of models engmc/binary - Tests variations in the injection energy and mass cut of ccsn explosions ## Notes Folders with the name '_k' have enhanced profile output for use in Kippenhan plots. Folders with the name '_v' have enhanced profile output for use in the video of the shock explosion. Each numbered folder contains a set of inlists used (which usually only vary one or two parameters), the rest of the inlists are stored in the base/ folders (See the submit.sh files for how to get MESA to read these files). They also contain a initial.mod file (which is the starting point for this phase of evolution), this is a softlink to the final.mod file from the previous phase (thus coreodep soft links to files in corehedep, corehdep uses MESA's built in ZAMS models to start). The folders that handle the core collapse explosions have additional .mod files that handle each phase of the explosion. See the base/ folders for details on the order. ## Files cacheHist.py - Runs mesaplot code to turn history files into a python binary file for faster reading. plotKip.py - Does a quick kippenhan plot for diagnostics

opencc-by-4.0Oct 2021View details →

zenodo36/100

Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"

Posterior samples and code to reproduce all figures associated with Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries. The <code>parameter_estimation</code> folder contains the following: <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> 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: <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> 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. 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.

opencc-by-4.0Oct 2023View details →

zenodo36/100

The Binary Fraction of Stars in Dwarf Galaxies: The Cases of Draco and Ursa Minor

supplementary data products, including all sky-subtracted spectra from individual targets, as well as random draws from posterior PDFs for model parameters (see enclosed README file)

opencc-by-4.0Sep 2018View details →

zenodo36/100

Ionizing spectra of stars that lose their envelope through interaction with a binary companion: role of metallicity

MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017A&A...608A..11G/abstract">Götberg et al. (2017)</a>. MESA version 7624. Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201730472">10.1051/0004-6361/201730472</a>

opencc-by-4.0Mar 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record