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Dataset results
5,090 results for “Black Hole”
Binary black-hole simulation SXS:BBH:0048
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:0044
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:0053
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:0051
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:0052
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:0037
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:0047
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:0036
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:0050
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:0031
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:0043
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0006
<p>Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.</p>
Black-hole neutron-star binary simulation SXS:BHNS:0004
<p>Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.</p>
Black-hole neutron-star binary simulation SXS:BHNS:0007
<p>Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.</p>
Black-hole neutron-star binary simulation SXS:BHNS:0005
<p>Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.</p>
Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory
<p>This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC). </p> <p>Several surrogate models are currently available in this catalog:</p> <ol> <li><strong>BHPTNRSur2dq1e3</strong>, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>BHPTNRSur1dq1e4</strong>, an updated version of the <strong>EMRISur1dq1e4 </strong>model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>EMRISur1dq1e4</strong>, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>
Reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070'
<p>This is a basic reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070', Bollemeijer et al., 2024, MNRAS, 528, 558-576.</p> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'.</li> <li>Authors: Niek Bollemeijer, Phil Uttley, Arkadip Basak, Adam Ingram, Jakob van den Eijnden, Kevin Alabarta, Diego Altamirano, Zaven Arzoumanian, Douglas J.K. Buisson, Andrew C. Fabian, Elizabeth Ferrara, Keith Gendreau, Jeroen Homan, Erin Kara, Craig Markwardt, Ronald A. Remillard, Andrea Sanna, James F. Steiner, Francesco Tombesi, Jingyi Wang, Yanan Wang and Abderahmen Zoghbi</li> <li>Paper DOI: https://doi.org/10.1093/mnras/stad3912</li> <li>Arxiv DOI: https://doi.org/10.48550/arXiv.2312.09835</li> <li>Published in the Monthly Notices of the Royal Astronomical Society (date of acceptance: 2023/12/12)</li> </ul> <h2>Raw Data</h2> <ul> <li>Raw event files for the described NICER observations can be obtained from the HEASARC at https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/w3browse.pl. Select NICER as the telescope and search for MAXI_J1820+070.</li> <li>We used HEASoft v6.28 with standard reprocessing settings to obtain event lists to make light curves. See paper for details.</li> </ul> <h2>Software</h2> <ul> <li>Linux Ubuntu 22.04.</li> <li>Jupyter Notebook (7.0.7)</li> <li>Programming languages used: Python (3.12.1)</li> <li>Python packages used: numpy (1.26.3), matplotlib (3.8.2), scipy (1.12.0), astropy (6.0.0)</li> </ul> <h2>Figures and Tables</h2> <ul> <li>Figures can be reproduced from the ./figures/ folder.</li> <li>All material and data used are available as intermediate data products.</li> <li>Jupyter notebooks (.ipynb files) can be used to make all figures. Running all cells at once does not work, but you can choose the figure you want to remake and executing the relevant cells will result in those figures.</li> </ul> <h2>Intermediate data products</h2> <ul> <li>The light curve arrays that are made in the first few cells of the main Jupyter Notebook can be found in 'datafiles.zip'. </li> <li>The parameters for the Lorentzian fits of the power spectra and the grouping of different observations can be found in 'qpofitsc.txt' and 'obsidsa.txt', respectively, in the same zipped folder.</li> </ul> <h2>End-to-End analysis scripts</h2> <ul> <li>The three Jupyter notebooks that have been added can be used to make the figures and reproduce the main results of the paper. Evidence_for_a_dynamic_corona_main.ipynb is about the main body of the paper, Evidence_for_a_dynamic_corona_energy_bands.ipynb is used for a part of the Discussion involving multiple narrow energy bands and Evidence_for_a_dynamic_corona_sim.ipynb is about the simulations described in Appendix A.</li> </ul>
Intermediate-mass black hole binary parameter estimation with next-generation ground-based detector networks
<p><strong>Reading the</strong> <strong>data</strong></p> <p>Each of the <em>alldetectors</em> and <em>CE40CE20ET</em> .zip file contains a folder with .pickle files. Each of these .pickle files corresponds to a point on the respective grid. The files contain dictionaries structured as follows:</p> <ul> <li>In the <em>skyareas </em>and <em>m1m2grid</em> folders, each dictionary stores the source-frame component masses, redshift, angular parameters, network SNR, network covariance matrix and 90% sky area.</li> <li>In the <em>Mzgrid</em> folders, each dictionary stores source-frame total mass, redshift, angular parameters, individual SNR and Fisher matrix for each detector in the network.</li> </ul> <p>The grids, detector network and parameters used to perform the Fisher calculations are described in the companion paper. The <em>converted_errors</em> folder contains network SNR, 1-sigma errors on source-frame masses, redshift, 90% sky areas for all the networks considered in the companion paper. These quantities are all angle-averaged.</p> <p>The <em>full_pe</em> .zip files cointain data comparing our Fisher results with full Bayesian PE runs for a few selected cases, as well as Jupyter notebooks to read those results (see Appendix B of the companion paper).</p>
Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation
<p>Input files and simulation results for stellar evolution tracks computed for the letter "Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation". Version 15140 of MESA was used for the simulations. More details in the README.txt file and in the letter.</p>
Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population
<p>The accompanying data and code release for the analyses presented in "Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population". See the github paper repository at https://github.com/bruce-edelman/CoveryingYourBasis and the arxiv release of the paper at: https://arxiv.org/abs/2210.12834</p>
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.