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5,090 results for “Black Hole”
An upper limit on the spins of merging binary black holes formed through binary evolution
<p>Input files to reproduce simulations of the paper "An upper limit on the spins of merging binary black holes formed through binary evolution". Mesa version used was r23.05.01 with the MESA SDK x86_64-linux-22.6.1 for stripped star models and MESA version 11701 with MESA SDK x86_64-linux-20190503 for binary models (to reproduce old results). Files for MESA simulations are contained in the folder 'mesa_models', containing the following:</p><ul><li>bin_models: Binary evolution models for a chemically homogeneous binary evolved with different models for angular momentum transport.<ul><li>original: Original simulation from du Buisson et al. 2020 using the TS dynamo</li><li>TSF_Fuller_Lu2021: Same simulation but using the model for the TS dynamo of Fuller & Lu 2021</li><li>solid_body: Simulation done with solid body rotation</li></ul></li><li>Z10Msun: stripped star models with metallicity Zsun/10. Each folder inside contains one model labeled by log10(M/Msun)</li><li>Z50Msun: stripped star models with metallicity Zsun/50. Each folder inside contains one model labeled by log10(M/Msun)</li><li>Z250Msun: stripped star models with metallicity Zsun/250. Each folder inside contains one model labeled by log10(M/Msun)</li></ul><p>In addition, scripts to reproduce figures are included. These were done with julia version 1.9.2 and the Makie plotting package. A Manifest.toml and Project.toml are included to reproduce the environment used to produce the figures and analyze results. The included files are:</p><ul><li>waveform.jl: Recalculates detection probabilities of du Buisson et al. 2020 including the effect of spin.</li><li>simplified_model.jl: Produces figures related to the semi-analytical model presented in the paper. Used for figures 1-4 of the paper</li><li>merger_rates.jl: Produces merger rate predictions. Used for figures 5-10 of the paper.</li></ul><p>Extra folders included are:</p><ul><li>ComputerModernFont: Fonts used for plotting</li><li>duBuisson_data: Original data from du Buisson et al. 2020. This includes both the outcomes of their mesa grids and their population synthesis calculations (see https://zenodo.org/records/3667546 for details).</li><li>sensitivity_curves: Sensitivity curves for GW detectors used to recompute detection probabilities<br><br> </li></ul>
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Dataset and Code used in ApJ publication: "A novel approach to understanding the link between supermassive black holes and host galaxies"
<p>All the data of this paper is found under BH_M-sigma_compilation/Data</p> <p>The main data tables are found the at BH_M-sigma_compilation/Data/BHcompilation_updated.csv</p> <p>The SDSS data galSpecInfo-dr8.fits and galSpecLine-dr8.fits can be found at: <a href="https://www.sdss3.org/dr8/spectro/spectro_access.php" rel="nofollow">https://www.sdss3.org/dr8/spectro/spectro_access.php</a></p> <p>The main code for this paper is at BH_M-sigma_compilation/Code/Mixture_Upper_Limits and consists of the 3 files for the Stan model, R code and Python code respectively: model.stan stan_fit.r hurdle_model.ipynb</p> <p>The figures generated for the paper can be found at BH_M-sigma_compilation/Figures</p> <p>A novel approach to understanding the link between supermassive black holes and host galaxies © 2024 by Gabriel Sasseville is licensed under CC BY 4.0. To view a copy of this license, visit <a href="https://creativecommons.org/licenses/by/4.0/" rel="nofollow">https://creativecommons.org/licenses/by/4.0/</a></p>
PixelPop: Nonparametric analysis of correlations in the binary black hole population with LIGO–Virgo–KAGRA data
<p>Data release accompanying the PixelPop papers, analyzing gravitational wave populations.</p> <p>The first dataset (in gwtc3_result_files) is the posterior samples for the runs presented in analysis of LIGO--Virgo--KAGRA data, following the third gravitational wave catalog, see https://arxiv.org/abs/2406.16844. We include a python notebook (example_plot.ipynb) showing how to create the plots presented in this paper.</p> <p>In v2, we also include samples from the predictive distributions. Due to the large uncertainties, marginalizing over the hyperposterior may be a poor representation of the inferred distribution, and so instead we provide samples from the <em>median</em> predictive distribution. That is, samples from the distribution shown in the central panels of the figures. </p> <p>The second dataset (in o4inj_result_files) is the posterior samples accompanying the runs presented in the technical background paper, see https://arxiv.org/abs/2406.16813. </p>
Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
Open the record for dataset details and reuse information.
General-relativistic precession in a black-hole binary - data release
<p>This page contains the data release associated with the publication <a href="https://www.nature.com/articles/s41586-022-05212-z">General-relativistic precession in a black-hole binary</a> from Hannam, M. <em>et al.</em> In this publication we show that one of the events in the <a href="https://arxiv.org/abs/2111.03606">most recent LIGO-Virgo-Kagra (LVK) data release</a> exhibits general relativistic orbital precession. The event is GW200129_065458.<br> <br> Here, we release the posterior samples (<code>*.h5</code>) that were obtained in this analysis. The files are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/index.html">PESummary</a>. For information about how to download and read the files, see the <a href="https://data.cardiffgravity.org/GW200129-precession/">public documentation</a>.<br> <br> If you use the material provided here, please cite the paper and this data release.</p>
Light Axion Emission and the Formation of Merging Black Holes Binaries
<p># Reproduction Package for the Paper "Light axion emission and the formation of merging binary black holes"</p> <p>## Authors:<br> Djuna Croon (djuna.l.croon@durham.ac.uk) <br> Jeremy Sakstein (sakstein@hawaii.edu) </p> <p>## Software</p> <p>MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0 </p> <p>## Example Directories</p> <p>**work:** Contains inlists, run\_star\_extras, and run\_binary_\extras needed to reproduce our results. These were adapted from the reproduction package of A&A 650, A107 (2021). A small number of models did not converge using the default controls. These can be made to complete by either setting the control make\_gradr\_sticky\_in\_solver\_iters = .true. (see inlist1, this works for models with small initial periods) or by relaxing delta\_HR\_limit and delta\_HR\_hard\_limit (see run\_binary\_extras line 900).</p> <p>## Citation Policy</p> <p>If you use any part of this reproduction package for independent work we recommend you cite the following papers:</p> <p>- <a href="https://arxiv.org/abs/2208.01110">https://arxiv.org/abs/2208.01110</a><br> - Phys.Dark Univ. 32 (2021) 100801<br> - Phys.Rev.D 102 (2020) 11, 115024<br> - Phys.Rev.Lett. 125 (2020) 26, 261105<br> - Astrophys.J.Lett. 916 (2021) 2, L16<br> - Phys.Rev.D 105 (2022) 095038<br> - A&A 650, A107 (2021)<br> - Astrophys. J. Suppl. 192, 3 (2011)<br> - Astrophys. J. Suppl. 208, 4 (2013)<br> - Astrophys. J. Suppl. 234, 34 (2018)<br> - ApJS 243, 10 (2019) </p>
Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?
<p>Input files and data used in the paper "Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?". MESA models were computed with version 12115 of MESA. COSMIC models new computed with version 3.4. README file included. </p>
Optical polarimetric observations of black hole binary Cyg X-1 with DIPol-2
<p>The dataset contains raw polarimetric FITS images of black hole X-ray binary <a href="https://en.wikipedia.org/wiki/Cygnus_X-1">Cyg X-1</a> (and surrounding field), obtained with the DIPol-2 optical CCD polarimeter in three (<em>BVR</em>) filters while mounted on the remotely operated Tohoku 60 cm (T60) telescope at the Haleakala Observatory, Hawaii. The data were collected over 5 observing nights during the week 2022 May 15–21 for about 4 hours each night. 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. Bias and dark FITS files have `bias` or `dark` labels in their names.</p>
Mathematica code for "Delicate windows into evaporating black holes"
<p>We provide the Mathematica code to reproduce the numerical and analytical results presented in the paper "Delicate windows into evaporating black holes".</p>
The data for Reconstruction of Cosmic Black Hole Growth and Mass Distribution from Quasar Luminosity Functions at z>4: Implications for Faint and Low-mass Populations in JWST
<p>The data for Figure 1 in the AAS article: Reconstruction of Cosmic Black Hole Growth and Mass Distribution from Quasar Luminosity Functions at z>4: Implications for Faint and Low-mass Populations in JWST</p> <p>in each file, the column heads are x: M1450; y_f0: total Phi(Mpc^-3 mag^-1) for f_seed=1.0 y1_f0: unobscured Phi for f_seed=1.0 y_f1: total Phi for f_seed=0.1 y1_f1: unobscured Phi for f_seed=0.1</p>
Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries
<p>This is the data repository for the paper "Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries"</p>
Inlists for "An Alternative Channel to Black Hole Low-Mass X-ray Binaries: Dynamical Friction of Dark Matter? "
<p>In this work, we employ MESA code to diagnose whether the dynamical friction between dark matter and the companion stars can drive BH binaries to evolve toward the observed BH LMXBs and alleviate the effective temperature problem. Assuming that there exists a density spike of dark matter around BH, the dynamical friction can produce an efficient angular momentum loss, driving BH binaries with an intermediate-mass companion star to evolve into BH LMXBs for a spike index higher than $\gamma = 1.58$.</p> <p>MESA version: r12115; SDK compiler: mesasdk-x86_64-linux-20190830</p> <p> </p>
Reconstruction of eccentric binary black hole gravitational wave signals using deep learning
<p>Dataset of deep learning-based reconstruction of simulated binary black hole gravitational wave signals with non-zero eccentricity. The details of the deep learning models and studies conducted can be found in the following papers: 1. https://arxiv.org/abs/2403.01559 and 2. https://arxiv.org/abs/2406.06324.<br><br>The data consists of 1 sec long whitened BBH injections ('Injection_signal') sampled at 2048 Hz, and their corresponding reconstructions ('Mean_reconstruction'), alongwith 50% ('Lower_50, Upper_50') and 90% ('Lower_90, Upper_90') C.I. of the reconstructions. The eccentricity values are labelled 'Eccentricity'. The masses and SNR of these signals are 30+30 solar mass and 12 respectively.</p>
Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)
<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>
Data from: "Eddington envelopes: The fate of stars on parabolic orbits tidally disrupted by supermassive black holes" (Price et al. 2024)
<p>The paper by Price et al. (2024; <a href="https://arxiv.org/abs/2404.09381">arXiv:2404.09381</a>) simulates the tidal disruption of a one solar mass polytropic star by a million-solar-mass supermassive black hole, using the Phantom general relativistic smoothed particle hydrodynamics code (Price et al. 2018). The code used to perform the simulations is available at:</p> <p><a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom</a></p> <p>This dataset contains:</p> <ol> <li>Scripts, intermediate data products and other small files used to create each figure in the paper</li> <li>Selected snapshots and other raw data from the four main calculations shown in the paper</li> </ol> <p>The main datasets contain parameter files and snapshots from the simulations used to create figures in the paper.small data files from the simulations and post-processing scripts used to create the scientific results and figures shown in the paper. Each simulation dataset contains:</p> <p><strong>.setup file:</strong> parameter file used by phantomsetup to create the initial conditions for the star itself<br><br><strong>.in file:</strong> parameter file used by phantom to perform the calculation<br><br><strong>tde_00000, tde_00100, tde_00200 etc:</strong> binary code snapshots containing raw data (particle positions, velocities, thermal energy, etc), these can be read/visualised/converted using the free and open source codes SPLASH (Price et al. 2007):<br><br><a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a></p> <p>or Sarracen:</p> <p><a href="https://github.com/ttricco/sarracen">https://github.com/ttricco/sarracen</a></p> <p>SPLASH was used to create figures in the paper. The format and ways to read these data files are described in:</p> <p><a href="https://phantomsph.readthedocs.io/en/latest/user-guide/dumpfile.html">https://phantomsph.readthedocs.io/en/latest/user-guide/dumpfile.html</a></p> <p><strong>tde01.ev, tde02.ev etc:</strong> ascii files (see header) containing global quantities like total angular momentum, total momentum, total energy etc as a function of time. Each number corresponds to a restart of the code (i.e. resubmission of the job to the queue on the cluster).</p> <p><strong>tde01.log, tde02.log: </strong>output log from the simulations themselves, this contains some useful information like the unit scalings, typical timestep and code performance, hardware information etc.</p> <p><em><strong>.defaults, .limits, .units and .filenames:</strong> parameter files used by SPLASH to create particular figures. For example, to use the files lightcurve.defaults, lightcurve.units, lightcurve.limits and lightcurve.filenames one would plot using splash -p lightcurve</em></p> <p>The relevant raw data directories are:</p> <p><strong>beta1_hres: </strong>raw data from the adiabatic, beta=1 calculation</p> <p><strong>beta5_hres:</strong> raw data from the adiabatic, beta=5 calculation</p> <p><strong>beta1_isentropic: </strong>raw data from the isentropic, beta=1 calculation with a non-spinning black hole</p> <p><strong>beta1_isentropic_kerr_a99_i60:</strong> raw data from the isentropic, beta=1 calculation with a maximally-spinning black hole with the initial stellar orbit at an incliation of 60 degrees to the black hole spin</p> <p><strong>The procedure to recreate each figure and post-processing analysis used in the paper is explained in the README.md placed in each directory unpacked from the figureX.zip file</strong></p> <p> </p>
Binary black-hole simulation SXS:BBH:1757
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1706
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:2035
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:2076
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
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