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5,021 results for “binary black holes”

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zenodo40/100

Binary black-hole simulation SXS:BBH_ExtCCE:0004

Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>, with CCE extraction by the <a href="https://spectre-code.org/">SpECTRE code</a>, and additional post-processing by the <a href="https://github.com/moble/scri">scri module</a>. This simulation has also been referred to as 'q1_aligned_chi0_6' in the related literature.

opencc-zeroMay 2021View details →

zenodo40/100

Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes

The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.

opencc-by-4.0Jul 2024View details →

zenodo40/100

Data For Survival of the Fittest: Testing Superradiance Termination with Simulated Binary Black Hole Statistics

This repository is associated with the GitHub repository: https://github.com/jacquelynzhy/Statistical_Superradiance, which includes the code to reproduce the findings of Zhu et al. (2025). Specifically, the file "Output1.dat" here represents the output of running ZEVN with the initial conditions outlined in Section 3.1 of Zhu et al. (2025), which only included BH-BH binaries. For the values generated by a ZEVN run and instructions on how to select the type of remnants you are interested in, please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.485..889S/abstract">Spera et al. (2019)</a> and the ZEVN GitHub page at: https://gitlab.com/sevncodes/sevn.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Binary black-hole surrogate waveform catalog

This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the <a href="http://www.black-holes.org/SpEC.html">Spectral Einstein Code</a>. The base method for building surrogate models can be found in <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.031006">Field et al., PRX 4, 031006 (2014)</a>. Several numerical relativity surrogate models are currently available in this catalog: <ul> <li>Current models <ol> <li> NRHybSur3dq8_CCE.h5 — This is a surrogate model for binary black hole systems built using CCE waveforms, capturing memory effects, with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers full stellar mass range for for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.064027"> Yoo et al., Phys. Rev. D 108, 064027 (2023)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>. </li> <li> NRHybSur2dq15.h5 — This is a surrogate model for binary black hole systems with a high mass ratio (up to 15), but restricted to nonprecessing spins and no secondary spin. Before constructing the surrogate, the NR waveforms are hybridized with SEOBNRv4HM to include the early inspiral. Therefore this model covers 9.5 solar mass or higher total mass system for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.044001">Yoo et al., Phys. Rev. D 106, 044001 (2022)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>. </li> <li> NRSur7dq4.h5 — This is a surrogate model for binary black hole mergers with generic spins and mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate">PyPI </a>. Instructions for evaluating this surrogate can be found at <a href="https://data.black-holes.org/surrogates/NRSur7dq4.html">this example IPython code </a>. </li> <li> NRHybSur3dq8.h5 — This is a surrogate model for binary black hole systems with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers the full stellar mass range for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.99.064045">Varma et al., PRD 99, 064045 (2019)</a>.  It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found this <a href="https://data.black-holes.org/surrogates/NRHybSur3dq8.html">example IPython code </a>. </li> <li> NRSur7dq4Remnant — This is a surrogate model for mass, spin, and recoil kick velocity of the remnant BH left behind in generically precessing binary black hole mergers, with mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link. </li> <li> NRSur7dq4EmriRemnant — This is a surrogate model for mass and spin of the remnant BH left behind in generically precessing binary black hole mergers, extending to arbitrary mass ratios. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.084015">Boschini et al., Phys. Rev. D 108, 084015 (2023)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link. </li> <li>NRSur3dq8_RD — This is a surrogate model for mass, spin, and complex quasinormal mode amplitudes of the remnant BH left behind from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at <a href="https://arxiv.org/abs/2408.05300">Magaña Zertuche et al., arxiv:2408.05300</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> <li>SEOBNRv4PHMSur — This is a surrogate model for binary black hole systems described by the precessing effective one body (EOB) waveform model SEOBNRv4PHM. The model is valid for mass ratio <= 20.  A paper describing it can be found at <a href="https://arxiv.org/abs/2203.00381" target="_blank" rel="noopener noreferrer">Gadre et al., arXiv:2203.00381</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate/" target="_blank" rel="noopener noreferrer">PyPI</a>.</li> <li>NRSur3dq8BMSRemnant — This is a surrogate model for the initial-to-final BMS transformation from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at Da Re et al., arxiv:2503.09569. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> </ol> </li> <li>Older models <ol> <li> SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is describedin <a href="http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.121102">Blackman et al., PRL115, 121102 (2015)</a>. It is evaluated with the gwsurrogate python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this <a href="https://data.black-holes.org/surrogates/GWSurrogate_example.html">example IPython code </a>. </li> <li> NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in <a href="https://dx.doi.org/10.1103/PhysRevD.95.104023">Blackman et al., PRD 95, 104023, (2017)</a>, and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this <a href="https://data.black-holes.org/surrogates/NRSur4d2s_tutorial.html">example IPython code </a>. </li> <li> NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be foundat <a href="https://dx.doi.org/10.1103/PhysRevD.96.024058">Blackman et al., PRD 96, 024058 (2017)</a>. This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this <a href="https://data.black-holes.org/surrogates/NRSur7dq2_tutorial.html">example IPython code </a>. </li> </ol> </li> </ul>     If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences). Caveats: <ol> <li> Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating. </li> <li> The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes. </li> </ol>

opencc-by-4.0Aug 2024View 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

Optical polarimetric observations of low-mass X-ray black hole binary MAXI J1820+070 during 2019-2021

The dataset contains raw polarimetric FITS images of the low-mass X-ray black hole binary <a href="https://www.astronomerstelegram.org/?read=11399">MAXI J1820+070</a> (and surrounding field), obtained by the <a href="https://doi.org/10.3847/1538-3881/abc74f">DIPol-UF </a>optical CCD polarimeter in three (BVR) filters while mounted on the 2.56m <a href="https://www.not.iac.es">Nordic Optical Telescope</a>. The data were collected over 5 observing runs throughout 2019--2021. During each observing night, a set of calibration images were also obtained. These typically include 7 dark and 7 bias images per filter per night (sometimes more if weather conditions or instrument settings changed during observations). Bias and dark FITS files have `_bias` or `_dark` labels in their names, as well as FITS key `IMAGETYP` set to either `Bias Frame` or `Dark Frame`, respectively.

opencc-by-4.0Dec 2021View details →

zenodo40/100

The Missing Link Between Black Holes in High-Mass X-ray Binaries and Gravitational-Wave Sources: Observational Selection Effects

Data tables containing the calculated binary parameters used to acquire all results in <a href="https://arxiv.org/abs/2210.01825v1">arXiv:2210.01825v1</a>. The file "xrb_params_illustris_z0.05_sample.csv" contains data for the z<0.05 sampled population and the file xrb_params_illustris_z20_sample.csv contains data for the z<20 sampled population.

opencc-by-4.0Oct 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 →

zenodo40/100

gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples

This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>. The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> . The changes in this release are <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> The data and configuration files included remain the same as in the v2.1 release. This release includes : <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>

opencc-by-4.0Apr 2019View details →

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