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5,090 results for “Black Hole”
Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"
<p>Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum"</p> <p>Included are:</p> <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> <p>Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance</p> <p>Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE</p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> 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:</p> <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> <p>The files named <strong>data_*.txt</strong> 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:</p> <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> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <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> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
Spinning test-body orbiting around Schwarzschild black hole: circular dynamics and gravitational-wave fluxes
<p>We release gravitational wave fluxes at null-infinity from a spinning test-body in circular equatorial orbits around a Schwarzschild black hole. Four different prescriptions are used for the dynamics: the Mathisson-Papapetrou formalism under the Tulczyjew (TUL) spin-supplementary-condition (SSC), the Pirani (PIR) SSC and the Ohashi-Kyrian-Semerak (OKS) SSC, and the spinning particle limit of the effective-one-body Hamiltonian (HAM) of [Phys.~Rev.~D.90,~044018(2014)]. For more details see xxxx .</p> <p>The multipolar fluxes are given for l=2,3 m=1,2,3 at the Boyer-Lindquist radii</p> <p> r = 4 5 6 7 8 10 12 15 20 30 ,</p> <p>in cases they were not computed the data contains a "42". Note that the fluxes in these data files are assumed to contain both the +m and -m contributions, since they are identical for equatorial orbits and aligned spins. <br /> Additionally, the data files contain the key numbers describing the circular dynamics (see paper).</p> <p>Units <span class="math-tex"><em>c</em>=<em>G</em>=1.</span></p>
Simulation of GW150914 binary black hole merger using the Einstein Toolkit
<p>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.</p> <p>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.</p> <p>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/.</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
Data Release: "No evidence that the majority of black holes in binaries have zero spin"
<p>This dataset contains the results presented in "<em>No evidence that the majority of black holes in binaries have zero spin</em>".</p> <p>In this paper, we systematically explored the effective and component spin distributions of binary black holes among the LIGO/Virgo GWTC-3 catalog. In particular, we tried to answer the following core questions, which have been the subject of active exploration and some debate in the literature:</p> <p><em>1. Is there an excess of binary black holes with vanishing spin, as predicted by some theories of angular momentum transport in stellar cores?</em></p> <p><strong>We find no evidence for an excess of vanishing spin systems.</strong> This finding is confirmed by three complementary analyses: one relying only on the Bayes factors between spinning and non-spinning priors for each BBH observation, one that seeks to model the distribution of effective aligned spins, and one modeling the distribution of component spin magnitudes and misalignment angles. Instead, we find BBH spin magnitudes to be consistent with a single, continuous distribution that remains finite at magnitude zero.</p> <p><em>2. Do there exist binaries with component spins misaligned by more than 90 degrees relative to their orbits?</em></p> <p><strong>We find a strong preference for the existence of such strongly misaligned spins.</strong> Our analysis of the BBH component spin distribution indicates that at least some component spins are misaligned from their orbits by more than 90 degrees. This result is robust under a variety of modeling choices regarding both the distribution of component spin magnitudes and tilts.</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/gwtc3-spin-studies/">https://github.com/tcallister/gwtc3-spin-studies/</a>. This repository includes <a href="https://github.com/tcallister/gwtc3-spin-studies/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/gwtc3-spin-studies/build/html/index.html">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The file <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul>
Numerical data for "Black Hole Metamorphosis and Stabilization by Memory Burden [arXiv:2006.00011]"
<p>This is the numerical data that belongs to the paper</p> <p>G. Dvali, L. Eisemann, M. Michel, S. Zell, <em>Black Hole Metamorphosis and Stabilization by Memory Burden</em>, <a href="https://doi.org/10.1103/PhysRevD.102.103523">Phys. Rev. D <strong>102</strong> (2020) 103523</a>, <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>.</p> <p>The numerical data is generated using the computer program <em>TimeEvolver</em>, which was presented in</p> <p>M. Michel, S. Zell, <em>TimeEvolver: A Program for Time Evolution With Improved Error Bound, </em><a href="https://doi.org/10.1016/j.cpc.2022.108374">Comput. Phys. Commun. <strong>277</strong> (2022) 108374</a>, <a href="https://arxiv.org/abs/2205.15346">arXiv:2205.15346</a>.</p> <p>In the following, equation numbers refer to the latest arXiv-version of <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>, where also all relevant definitions can be found. In this paper, the procedure for generating the data, which we summarize in the following, is also described in more detail.</p> <p>First, among the five parameters N<sub>c</sub>; <sup><span class="math-tex">\(\epsilon\)</span></sup><sub>m</sub>; C<sub>0</sub>; ∆N<sub>c</sub>; K all but one are fixed (according to eq. (36)). Then for different values of the remaining unfixed parameter - subsequently called X - the following 2-step process is performed.</p> <ol> <li>Time evolution is computed (with the initial state shown in eq. (35)) for many different values of the parameter C<sub>m</sub> in the interval [0;1] (sampling step 10<sup>-3</sup>). The results for X are stored in five folders called "X", the subfolders of which contain data for different values of X. For example, the subfolder "01" of the folder "C0" consists of data for C<sub>0</sub>=0.01. Please note that the folders "Cgap", "N0" and "Q" correspond to X=<span class="math-tex">\(\epsilon\)</span><sub>m</sub>, X=N<sub>c</sub> and X=K, respectively. Subsequently, "rewriting values" of C<sub>m</sub> are selected as those for which the amplitude of n<sub>0</sub> is sufficiently large (1.2 times than in the case C<sub>m</sub>=0). This is done in Mathematica-notebooks "_New.nb". Finally, rewriting values for different values of X are collected using Mathematica-notebooks with names that start on "_Meta". These notebooks generate the 5 plots shown in figures 4(a), 5(a), 6(a), 7(a) and 8(a).</li> <li>Next finer scans are performed around some of the rewriting values determined in step 1 (new sampling step 5 10<sup>-5</sup>). The results are stored in five folders called "XRates", where again subfolders correspond to different values of X. For example, the subfolder "01" of the folder "C0Rates" consists of finer scans in C<sub>m</sub> around rewriting value of C<sub>m</sub> for C<sub>0</sub>=0.01. Next, Mathematica-notebooks with the names "_New.nb" or "_NewRates.nb" are used to select around each rewriting value the C<sub>m</sub> that leads to the largest rate (see definition in <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>). Finally, these maximal rates for different values of X are collected using Mathematica-notebooks with names that start on "_Meta" and end on "Rates.nb". These notebooks generate the 5 plots shown in figures 4(b), 5(b), 6(b), 7(b) and 8(b).</li> </ol>
Optical polarimetric observations of the black hole binary star Cyg X-1 with RoboPol
<p>The dataset contains raw FITS images of the black hole X-ray binary star <a href="https://simbad.cds.unistra.fr/simbad/sim-id?Ident=%402905066&Name=HD%20226868&submit=submit">Cyg X-1</a>, raw FITS images of a nearby field star used for the interstellar polarization correction and processed measurements of polarimetric standards used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 13 May and 1 June 2022.<br> </p>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
Precessing binary-black-hole numerical relativity catalogue (minimal data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Data Release: "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3"
<p>This repository contains the data behind the figures presented in v2 of "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3" (<a href="https://ui.adsabs.harvard.edu/link_gateway/2023arXiv230715824F/arxiv:2307.15824">arXiv:2307.15824</a>), to appear in ApJL.</p><p>The csv files (in Output.zip) and the h5 files contain the data products. The three Jupyter notebooks include code for plotting the figures and calculating the summary statistics that appear in the paper. </p><p> </p>
Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries
<p>These are the results from:</p> <p>"Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries"<br> Authors: L.A.C. van Son, S. E. de Mink, F. S. Broekgaarden, M. Renzo, S. Justham, E. Laplace, J. Moran-Fraile, D. D. Hendriks, and R. Farmer</p> <p>ADS: https://ui.adsabs.harvard.edu/abs/2020arXiv200405187V/abstract<br> arXiv: https://arxiv.org/abs/2004.05187</p> <p>If you use (part of) these results in a scientific publication, we would greatly appreciate it if you would cite the source paper.</p> <p>This work uses <a href="https://compas.science/">COMPAS</a> to compute binary population properties (<a href="http://https://github.com/TeamCOMPAS/COMPAS/tree/master/docs">https://github.com/TeamCOMPAS/COMPAS/tree/master/docs</a>).</p> <p>*****************************</p> <p>For each of our 4 model variations (0. Fiducial, 1. Stable accretion, 2. Common envelope accretion and 3. Combined) we provide 2 files:</p> <p>1.) pythonSubmit.py file describing the initial conditions that were used to run the simulations</p> <p>2.) COMPASOutput.h5 file, which contains the following datasets resulting from our simulations :<br> ['systems',<br> 'doubleCompactObjects',<br> 'commonEnvelopes',<br> ]</p> <p>Detailed descriptions of these groups can be found in the accompanying README file.</p>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
Multiwavelength observations reveal a faint candidate black hole X-ray binary in IGR J17285-2922
<h2>Reproduction package for the paper "Multiwavelength observations reveal a faint candidate black hole X-ray binary in IGR J17285-2922"</h2><h4>This is a reproduction package with the internal API designation of 'silver'</h4><h4>Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 1, October 2021, Pages 330–349</h4><h4>Authors: <strong>M. Stoop</strong>, J. van den Eijnden, N. Degenaar, A. Bahramian, S. J. Swihart, J. Strader, F. Jiménez-Ibarra, T. Muñoz-Darias, M. Armas Padilla, A. W. Shaw, T. J. Maccarone, R. Wijnands, T. D. Russell, J. V. Hernández Santisteban, J. C. A. Miller-Jones, D. M. Russell, D. Maitra, C. O. Heinke, G. R. Sivakoff, F. Lewis D. M. Bramich</h4><h4>Paper DOI: https://doi.org/10.1093/mnras/stab2127</h4><h4>Zenodo DOI: https://doi.org/10.5281/zenodo.4664505</h4><p> </p><h2>Raw Data</h2><p> </p><p>- Uncalibrated X-ray data is given in ./raw_data</p><p> </p><p>- Radio data is too large in size to be stored on Zenodo. If you want to acquire these images, but can be found under https://data.nrao.edu searching for project code SF8027</p><p> </p><p>- Raw data for the optical spectra can be acquired by contacting J. van den Eijnden</p><p> </p><h2>Software</h2><p> </p><p>- OS: MacOS Big Sur 11.6</p><p> </p><p>Programming languages:</p><p> </p><p>- Python (3.9.7), matplotlib, numpy, pandas, scipy, linmix</p><p> </p><p>- Jupyter Notebook (6.3.0)</p><p> </p><p>NASA HEASARC's Software:</p><p> </p><p>- xrtpipeline (version 0.13.5)</p><p> </p><p>- caldb in the heasoft package (version 6.26.1)</p><p> </p><p>- xselect (version v2.4g)</p><p> </p><p>- xrtmkarf (version 0.6.3)</p><p> </p><p>- xspec (v. 12.10.1f)</p><p> </p><p>- casa pipeline (5.6.2)</p><p> </p><h2>Figures and Tables</h2><p> </p><p>- scripts and data to make the figures and tables can be found in ./figures_tables</p><p> </p><p>- figure 4, 5, 6, and 7 are made by collaborators. Please contact J. van den Eijnden if you would like access to data files or scripts for these figures.</p><p> </p><p>- X-ray lightcurve fit results in Table 3 is done by collaborators. Please contact J. van den Eijnden if you would like access to data files or scripts for this table.</p><p> </p><h2>Intermediate data products </h2><p> </p><p>- Intermediate data products can be found in the directory ./intermediate_data</p><p> </p><p>- This includes the calibrated X-ray data, VLA imaging scripts to determine the flux density and spectral index.</p><p> </p><p>- Scripts can also be found here for intermediate data products for several figures (1, 2, 3, 8)</p><p> </p><h2>Scientific-analysis</h2><p> </p><p>- The directory ./scientific_analysis contains scripts and data to reduce the raw data to the intermediate data products.</p><p> </p><p>- ./Xray_files how to calibrate the Swift X-ray spectra</p><p> </p><p>- ./Xray_spectral_evolution contains how the intermediate data products for figure 3</p><p> </p><p>- ./VLA_data_reduction how to reduce the VLA data and determine flux densities and spectral indices</p><p> </p><p>- ./Radio_Xray_Coupling contains the intermediate data products for figure 2</p><p> </p><p>- ./Xray_lightcurve_fitting contains intermediate data products for Table 3 and fitting performed in section 3.4</p><p> </p><p>- ./Orbital_Period contains intermediate data products for Table 4 and Figure 8</p><p> </p><p>- ./xray contains backup files related to the x-ray spectra</p><p> </p><p>- ./radio contains backup files related to the radio data</p><p> </p><p>- the main results (intermediate data products) are the .txt files in this directory</p>
Binary black-hole simulation SXS:BBH_ExtCCE:0013
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 'q4_precessing' in the related literature.
Binary black-hole simulation SXS:BBH_ExtCCE:0012
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 'q4_antialigned_chi0_4' in the related literature.
Binary black-hole simulation SXS:BBH_ExtCCE:0011
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 'q4_aligned_chi0_4' in the related literature.
Binary black-hole simulation SXS:BBH_ExtCCE:0009
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_superkick' in the related literature.
Binary black-hole simulation SXS:BBH_ExtCCE:0010
<p>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 'q4_nospin' in the related literature.</p>
Binary black-hole simulation SXS:BBH_ExtCCE:0008
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_precessing' in the related literature.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.