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93 results for “Mergers”

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zenodo48/100

Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"

This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".   BBH_mergers_cat_*.dat contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as: <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> The files named data_*.txt contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows: <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> Finally, outcomes_*.dat contains two columns: <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.   Contacts: Marco Dall'Amico marco.dallamico@phd.studenti.unipd.it marco.dallamico@pd.infn.it

opencc-by-4.0Feb 2023View details →

zenodo48/100

Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"

We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.

opencc-by-4.0Sep 2023View details →

zenodo44/100

Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset

Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models: * `search_results.dat` contains the parameters and properties of the successful results of the study. * `model_*.log` are the logs with settings, parameters, and properties of the selected models. * `model_*.out` are the profiles of the selected models in binary format. * `XNS_reader.py` is a python script to read the binary format, convert it to text, and compute some derived and global quantities. EDIT 2022-05-30: the output file in binary format does contain the profiles of temperature and entropy per baryon, but those are not outputted in the converted text file. You can manually modify the python script in order to output these profiles too. * `properties.csv` is a summary of the parameters and properties of the selected models. This dataset has been obtained with the stationary code XNS in General Relativity with the Conformal Flatness Approximation [Bucciantini and Del Zanna 2011; Pili et al. 2014; Camelio et al. 2018 and 2019]. The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law. The models have been selected between those obtained in the parameter space exploration. For details see the companion paper [Camelio et al. 2021, PRD 103:063014]. If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014]. EDIT 2022-05-30: an updated version of the code that has been used to produce this dataset is now on Zenodo (https://doi.org/10.5281/zenodo.6594069). This updated version is called ASWNS code, and it does not contain the model of binary neutron star merger remnant used for this dataset, but an older version of the model of nonbarotropic neutron star (from Camelio et al. 2019). You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.

opencc-by-4.0Nov 2020View details →

zenodo44/100

The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves

We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4].   The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain: $( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )$ where $D_{\rm cm}$ is the distance in cm from the source. The data files include a machine readable "/metadata" group with: <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz. We make these waveforms freely available in the hope that they will be useful.  We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms. --- [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html [2] https://eob.ihes.fr/ [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103. [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.

opencc-by-4.0Feb 2016View details →

zenodo44/100

Simulation of GW150914 binary black hole merger using the Einstein Toolkit

On February 11, 2016, the LIGO collaboration announced that they had achieved the first ever direct detection of gravitational waves. The gravitational waves – which were detected by both LIGO detectors on September 14, 2015 at 09:51 UTC – were generated over a billion years ago by the merger of a binary black hole system. The announcement came along with the simultaneous publication of a peer-reviewed paper [Phys. Rev. Lett. 116, 061102]; several other papers giving technical details; and a full release of the data from the detection, which has been given the name GW150914. The LIGO analysis found that the merger consisted of a 36 + 29 solar mass binary black hole system, the remnant was a 62 solar mass black hole, and the remaining 3 solar masses were radiated as gravitational waves. This dataset represents a subset of the data from a simulation in which the Einstein Toolkit was used to evolve the last 6 orbits and merger of a binary black hole system with parameters that match the GW150914 event. More details on the simulation, including instructions for how to run it and how to analyse the data can be found in the Einstein Toolkit gallery at http://einsteintoolkit.org/about/gallery/gw150914/.

opencc-by-4.0Sep 2016View details →

zenodo44/100

Merger simulation (elliptical & spiral galaxy)

Gadget-2 (Springel 2005) simulation of 3.56 Gyr evolution of a shell-creating merger with 1:10 stellar-mass ratio, elliptical primary galaxy and secondary with inclined disk and 6 kpc impact parameter. One second of the video corresponds to 60 Myr. For more details, see <a href="https://ui.adsabs.harvard.edu/abs/2020A%26A...634A..73E/abstract">Ebrová et al. (2020, A&A 634, 73)</a>

opencc-by-4.0May 2024View details →

zenodo40/100

r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs

This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references. When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.

opencc-by-4.0Jan 2021View details →

zenodo40/100

How loud are neutron star mergers?

We release neutron star merger waveforms computed using fully general relativistic simulations of equal and unequal-mass binaries drawn from the galactic population. The simulations employ finite-temperature microphysical equations of state (LS220, DD2, and SFHo) and neutrino cooling. Please, see http://arxiv.org/abs/1512.06397 for details.   Each tarball refers to a simulation and contains <ul> <li>Curvature multipolar waveform $\psi^{(4)}_{\ell m}$</li> <li>Metric multipolar waveform $h_{\ell m}$</li> <li>Radiated energy and angular momentum</li> </ul> Files: <ul> <li>waveforms/Psi4_l?_m?_r200.txt <ul> <li>Columns: $t,\ \Re{(\psi^{(4)}_{\ell m})},\ \Im{(\psi^{(4)}_{\ell m})} $</li> </ul> </li> <li>waveforms/Rh_l?_m?_r200.txt <ul> <li>Columns: $u/M,\ \Re{(h_{\ell m})}/M,\ \Im{(h_{\ell m})}/M,\ \Re{(\dot{h}_{\ell m})},\ \Im{(\dot{h}_{\ell m}}),\ M\omega_{\ell m},\ A_{\ell m}/M,\ \phi_{\ell m},\ t $</li> </ul> </li> <li>waveforms/Ej_r200.txt <ul> <li>Columns: $E_b,\ j,\ E_\text{rad},\ J_\text{rad},\ t $</li> </ul> </li> </ul> where <ul> <li>$t$ simulation time</li> <li>$u$ retarded time</li> <li>$M$ binary mass</li> <li>$\omega_{\ell m}$ wave frequency</li> <li>$A_{\ell m}$ wave amplitude</li> <li>$\phi_{\ell m}$ wave phase</li> <li>$E_\text{GW}$ radiated energy</li> <li>$J_\text{GW}$ radiated angular momentum</li> <li>$E_b$ binary energy</li> <li>$j$ binary specific angular momentum</li> </ul> Please refer to the paper and references therein for the definition of the different quantities. Units $c=G=M_\text{Sun}=1$

opencc-zeroJul 2016View details →

zenodo40/100

Contact tracing of binary stars: Pathways to stellar mergers (online data)

# Data for Henneco et al. (2024) This repository contains the input files required to reproduce the MESAbinary models from Henneco et al. (2024). It also contains the full machine-readable version of Table G.1. For an overview of the quantities in each column, we refer to the notes underneath Table G.1 in the paper. MESA r12778 MESA SDK 20.3.2 ## MESA_inlists - inlist1: inlist for the initially more massive primary star - inlist2: inlist for the initially less massive secondary star - inlist_project: inlist for the binary system   ## run_extras - run_star_extras.f: subroutines and functions for the individual stars - run_binary_extras.f: subroutines and functions for the binary system   ## MESA_ZAMS_models Precomputed ZAMS models read in through inlist1 and inlist2.   ## table_G1_full.txt Full machine-readable version of Table G.1.   ## MESA_models_output Detailed output of the MESAbinary calculations. Will be added in due time.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints

<h1>README</h1> This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background. In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as $$ R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}. $$ At redshifts $z < z_p$, the merger rate grows approximately as $R(z) \propto (1+z)^\alpha$, whereas at $z>z_p$ it falls as $R(z) \propto (1+z)^{-\beta}$. The analysis was performed as described in <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab9743">Callister et al (2020)</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott et al (2021)</a>, now using binary black hole detections from the GWTC-3 catalog (<a href="https://link.aps.org/doi/10.1103/PhysRevX.13.041039">Abbott et al 2023a</a>, <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">2023b</a>). <ul> <li>The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023</li> <li>Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398</li> <li>Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott et al (2021).</a></li> </ul> As discussed in <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">Abbott et al (2023b)</a>, the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (<a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott et al 2021</a>); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over. <h1>Contents</h1> <ul> <li><code>processed_emcee_samples_together_r00r01.npy</code>: File containing posterior samples when jointly analyzing BBH detections and stochastic background upper limits.</li> <li><code>processed_emcee_samples_noStochastic_r00r01.npy</code>: File containing posterior samples analyzing only direct BBH detections.</li> <li><code>run_emcee_plPeak.py</code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_together_r00r01.npy</code></li> <li><code>run_emcee_plPeak_noStochastic.py</code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_noStochastic_r00r01.npy</code></li> </ul> <h1>Accessing posterior samples</h1> Posterior samples are contained in the files <code>processed_emcee_samples_together_r00r01.npy</code> and <code>processed_emcee_samples_noStochastic_r00r01.npy</code>. This is loaded via python as, e.g. <blockquote> >>> import numpy as np >>> dataset = np.load('processed_emcee_samples_together_r00r01.npy') </blockquote> Contained in this file is a single <code>numpy</code> array of size <code>(# of posterior samples, # of hyperparameters)</code>: <blockquote> >>> dataset.shape (1152, 13) </blockquote>   The 13 hyperparameters are defined as follows: <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Definition</td> </tr> <tr> <td><code>dataset[:, 0]</code></td> <td><code>xeff_mu</code></td> <td>Mean effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 1]</code></td> <td><code>xeff_sig</code></td> <td>Standard deviation of effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 2]</code></td> <td><code>R0</code></td> <td>Total BBH merger rate at redshift $z=0$</td> </tr> <tr> <td><code>dataset[:, 3]</code></td> <td><code>mMin</code></td> <td>Minimum black hole mass</td> </tr> <tr> <td><code>dataset[:, 4]</code></td> <td><code>mMax</code></td> <td>Maximum black hole mass</td> </tr> <tr> <td><code>dataset[:, 5]</code></td> <td><code>lmbda</code></td> <td>Power-law index on primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 6]</code></td> <td><code>mu_peak</code></td> <td>Mean of Gaussian peak in primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 7]</code></td> <td><code>sig_peak</code></td> <td>Standard deviation of Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 8]</code></td> <td><code>frac_peak</code></td> <td>Fraction of events occupying Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 9]</code></td> <td><code>bq</code></td> <td>Power-law index on mass ratio distribution $p(q\|m_1)$</td> </tr> <tr> <td><code>dataset[:, 10]</code></td> <td><code>alpha</code></td> <td>Slope of $R(z) \propto (1+z)^\alpha $ at low redshifts</td> </tr> <tr> <td><code>dataset[:, 11]</code></td> <td><code>beta</code></td> <td>Slope of $R(z) \propto (1+z)^{-\beta}$ at high redshifts</td> </tr> <tr> <td><code>dataset[:, 12]</code></td> <td><code>zpeak</code></td> <td>Redshift at which $R(z)$ peaks and turns over</td> </tr> </tbody> </table>   The exact usage of the above parameters can be seen in the included scripts <code>run_emcee_plPeak.py</code> and <code>run_emcee_plPeak_noStochastic.py</code>, with which the inference was performed.

opencc-by-4.0Jun 2024View details →

zenodo40/100

Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"

This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.

opencc-by-4.0Oct 2021View details →

zenodo40/100

Reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif"

This is a basic reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif".

opencc-by-4.0Apr 2021View details →

zenodo40/100

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

The data for all BHBH 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-4.0Nov 2021View details →

zenodo40/100

Search for merger ejecta emission in Short Gamma Ray Bursts from very late time radio observations

Coalescence of inspiral binary neutron stars (BNS) system, giving rise to short Gamma Ray Bursts (GRBs), are one of the most probable candidates for Gravitational Waves (GWs). If the resultant product of the merger is a millisecond magnetar, a significant proportion of the rotational energy deposited to emerging ejecta that produce late time radio brightening from the interaction with the surrounding ambient medium. Detection of this late-time radio emission from short GRBs can have profound implications for understanding the physics of the progenitor. This study presents the deepest and an extensive search for radio emission at late times following a short GRB to date incorporating proper frequency regime, wider observation span and relativistic correction. Five short GRBs were observed with the Giant Meter Wave Radio Telescope (GMRT) at 1250, 610, and 325 MHz band $\sim$ 2 - 11 years since the burst to search for radio emission from the merger ejecta. The estimated upper limits at the burst location are used to constrain the parameters of the burst and its surrounding environment. The magnetar model, with appropriate modifications, constrains the number density of the ambient medium for these bursts to be between $10^{-4}$ - $10^{-2}$ $cm^{-3}$. Our analysis rules out a stable magnetar with an energy of $10^{53}$ erg for four out of the five GRBs in our sample.

opencc-by-4.0Feb 2022View details →

zenodo40/100

NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers

Data release associated with the preprint "NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers" Data includes: EOS files: <ul> <li>5000 eos files with radius (km), mass (Msun), and tidal deformability as columns stored under eos/eos_data</li> <li>prior probabilities for the EOSs are stored in eos/eos_prior_probability.dat</li> </ul> Posterior samples: <ul> <li>Posterior samples based on the analysis of GW170817 and AT2017gfo stored in posterior_samples/GW170817-AT2017gfo_posterior_samples.dat</li> <li>Posterior samples based on the analysis of GW170817,  AT2017gfo, and the afterglow of GRB170817A are stored in posterior_samples/GW170817-AT2017gfo-GRB170817A_afterglow_posterior_samples.dat</li> </ul>

opencc-by-4.0May 2022View details →

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Data Release: Population properties and multimessenger prospects of neutron star-black hole mergers following GWTC-3

Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm rate  ≤1 yr−1 to constrain the mass and spin distributions and multimessenger prospects of these systems. We find that the black holes in NSBHs are both less massive and more slowly spinning than those in black hole binaries. We also find evidence for a mass gap between the most massive neutron stars and least massive black holes in NSBHs at 98.6% credibility. We consider both a Gaussian and a power-law pairing function for the distribution of the mass ratio between the neutron star and black hole masses but find no statistical preference between the two. Using an approach driven by gravitational-wave data rather than binary simulations, we find that fewer than 14% of NSBH mergers detectable in gravitational waves will have an electromagnetic counterpart. Finally, we propose a method for the multimessenger analysis of NSBH mergers based on the nondetection of an electromagnetic counterpart and conclude that, even in the most optimistic case, the constraints on the neutron star equation of state that can be obtained with multimessenger NSBH detections are not competitive with those from gravitational-wave measurements of tides in binary neutron star mergers and radio and X-ray pulsar observations.

opencc-by-4.0Jul 2022View details →

zenodo40/100

Dataset from: Multi-messenger observations of binary neutron star mergers in the O4 run

The binary neutron stars population data from the paper "Multi-messenger observations of binary neutron star mergers in the O4 run" (<a href="https://arxiv.org/abs/2204.07592">https://arxiv.org/abs/2204.07592</a>). Details of how to use the data, as well as the scripts to reproduce the figures of the main text of the paper are given in the accompanying Github repository <a href="https://github.com/acolombo140/O4NSNS">https://github.com/acolombo140/O4NSNS</a> If you use this data, please cite the above manuscript.

opencc-by-4.0Jul 2022View details →

zenodo40/100

Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"

This is a basic reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole neutron star mergers".

opencc-by-4.0May 2022View details →

zenodo40/100

r-process abundances in neutron star merger dynamical ejecta given different fission yields

This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE). When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.

opencc-by-4.0Sep 2022View details →

zenodo40/100

Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift

Database of catalogs and numerical results for the paper: Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift.   ABSTRACT Hierarchical black hole (BH)  mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution.  We find that the evolution of the host GC  quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1 > 50 \, \msun$ regardless of the assumed BH pairing function.  At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density.  We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \msun$ for $z\geq{}2$. Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very  sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs in order to reproduce misaligned spin observations.   FILES DESCRIPTION: Files Catalogs.zip contain the data used in this paper.  The directory Metallicities contains the outputs of the Fastcluster runs at Z=0.0002. For each model and for each GC evolutionary case, we report the populations of BBHs at first ("first_generation.csv") and nth ("nth_generation.csv") generation.  The directory Merger_Rate_Density contains the catalogs from Cosmorate+Fastcluster at redshift 0 to 4 ("redshift_*.csv") and the merger rate density as a funcion of redshift ("merger_rate_density.csv"), for different GC models. Also, it contains the mixing fractions for all the models presented in this paper ("mixing_fractions.csv"). The Jupyter notebooks generate the Figures in the main body of the paper.

opencc-by-4.0May 2024View details →

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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