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5,462 results for “binary”
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>
Binary neutron-star simulation SXS:NSNS:0001
<p>Simulation of a 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>
Binary classification of the Kinshasa and Bandundu provinces in the Democratic Republic of the Congo — settled versus non settled
<p>This dataset was created based on a settlement layer produced by the Oak Ridge National Laboratory using feature extraction from high-resolution imagery for population modelling work undertaken in the Kinshasa and Kongo-Central provinces in the Democratic Republic of the Congo. The settlement layer consists of settlement polygons of approximately 7 meters resolution. The polygons were rasterized based on a reference grid with a resolution of 3 arc-seconds, approximately 90 meters. The presence of at least one settlement polygon designated a settled cell.</p> <p>We thank the Oak Ridge National Laboratory and the Bill and Melinda Gates Foundation for the support. We would also like to extend our gratitude to Eric M. Webber and Amy N. Rose at the Oak Ridge National Laboratory and Io Blair-Freese at the Bill and Melinda Gates Foundation.</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>
Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.
<p><strong>Figure captions:</strong></p> <p> </p> <p><strong>Fig. A.1.</strong> Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.</p> <p> </p> <p><strong>Fig. A.2.</strong> Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.</p> <p> </p> <p><strong>Fig. A.3.</strong> Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).</p> <p> </p> <p><strong>Fig. A.4.</strong> K-S test comparing radial SOM (black) and CNN (blue).</p> <p> </p> <p><strong>Fig. A.5.</strong> Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.</p> <p> </p> <p><strong>Fig. A.6.</strong> Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.7.</strong> Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.8.</strong> Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.9.</strong> Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.10.</strong> Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.11.</strong> Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.12.</strong> Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.13.</strong> Cluster 3 of spectra (red color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.14.</strong> Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.15.</strong> Cluster 2 of spectra (green color) with the other spectra in the background (gray color).</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>
Dataset from: Binary population synthesis with probabilistic remnant mass and kick prescriptions
<p>This contains the data behind the paper "<a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1380M">Binary population synthesis with probabilistic remnant mass and kick prescriptions</a>"<br> <br> See<a href="https://github.com/TeamCOMPAS/COMPAS"> https://github.com/TeamCOMPAS/COMPAS</a> for code information and instructions on parsing the data.</p>
Binary optimal LCD codes
<p>This data set contains generator matrices of binary LCD codes constructed with the program <strong>Generation,</strong> as well as files with non-existence results. The results are presented in the paper "Optimal Binary LCD Codes" by Stefka Bouyuklieva in Tables 1-4. The classification results are given in a separate directory. The form of the matrices is the same as that used in <strong>Q-Extension</strong>.</p>
Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release
<p><strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release</strong></p> <p>The results of gamma-ray observations of the binary system HESS J0632+057 collected during 450 hours over 15 years, between 2004 and 2019, with the H.E.S.S., MAGIC, and VERITAS telescopes are presented in <strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes</strong> (ApJ, to be published).<br> This repository provides access to all processed data presented in the publication in csv and ascii format.<br> For a detailed description of analysis and data processing, see the associated primary publication.</p> <p><strong>Please cite always the following primary reference when using these data: </strong></p> <ul> <li> <p><a href="https://doi.org/10.3847/1538-4357/ac29b7">The Astrophysical Journal, 923:241 (30pp), 2021 December 20</a></p> </li> <li> <p><a href="https://arxiv.org/abs/2109.11894">arXiv:2109.11894</a></p> </li> </ul> <p>Data Publication Year: 2021</p> <p>Citation: The VERITAS, MAGIC, and H.E.S.S. Collaborations (2021). Observation of the gamma-ray binary HESS J0632+057 with the HESS, MAGIC, and VERITAS telescopes - data release. DOI **[DOI to be added]**</p> <p>Additional information on the gamma-ray observatories:<br> - H.E.S.S. (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/">https://www.mpi-hd.mpg.de/hfm/HESS/</a>) and H.E.S.S. Auxiliary Data Page (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html">https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html</a>)<br> - MAGIC (<a href="https://magic.mpp.mpg.de/">https://magic.mpp.mpg.de/</a>) and MAGIC Data Page (<a href="http://vobs.magic.pic.es/fits/">http://vobs.magic.pic.es/fits/</a>)<br> - VERITAS (<a href="https://veritas.sao.arizona.edu/">https://veritas.sao.arizona.edu/</a>) and VERITAS Data Page (<a href="https://github.com/VERITAS-Observatory/VERITAS-VTSCat">https://github.com/VERITAS-Observatory/VERITAS-VTSCat</a>)</p> <p>This data repository is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/</p> <p>## List of data:</p> <p>(best to view with a markdown reader)</p> <p>1. Gamma-ray and X-ray fluxes (Figures 2, 3, and 9):<br> - Gamma-ray integral flux (>350 GeV) from H.E.S.S. observations: [Fig02_03_09/LightCurve-HESS.ecsv](Fig02_03_09/LightCurve-HESS.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from MAGIC observations: [Fig02_03_09/LightCurve-MAGIC.ecsv](Fig02_03_09/LightCurve-MAGIC.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from VERITAS observations: [Fig02_03_09/LightCurve-VERITAS.ecsv](Fig02_03_09/LightCurve-VERITAS.ecsv)<br> - X-ray fluxes (0.3–10 keV) from Swift-XRT, Chandra, XMM, NuSTAR, Suzaku observations: [Fig02_03_09/LightCurve-XRay.ecsv](Fig02_03_09/LightCurve-XRay.ecsv)<br> 2. Halpha observations (Figure 4):<br> - Profile parameters of Halpha observations [Fig04/Halpha.ecsv](Fig04/Halpha.ecsv)<br> 3. Gamma-ray - X-ray correlation (Figure 5):<br> - Contemporaneous gamma-ray (>350 GeV) vs X-ray (0.3–10 keV) integral fluxes: [Fig05/LC-cross-Gamma-XRay.ecsv](Fig05/LC-cross-Gamma-XRay.ecsv)<br> - Discrete cross-correlation function (DCF) between gamma- ray and X-ray data: [Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv](Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv)<br> 4. Gamma-ray vs Optical and X-ray vs Optical correlations (Figure 6):<br> - Halpha vs gamma-ray observations: [Fig06/Gamma-ray-Optical-Correlation.ecsv](Fig06/Gamma-ray-Optical-Correlation.ecsv)<br> - Halpha vs X-ray observations: [Fig06/X-ray-Optical-Correlation.ecsv](Fig06/X-ray-Optical-Correlation.ecsv)<br> 5. Spectral energy distributions (phase averaged; Figure 7 and 8)<br> - SEDs from H.E.S.S. observations: [Fig07_08/HESS-phaserange04-spectrum.ecsv](Fig07_08/HESS-phaserange04-spectrum.ecsv), [Fig07_08/HESS-phaserange1-spectrum.ecsv](Fig07_08/HESS-phaserange1-spectrum.ecsv), [Fig07_08/HESS-phaserange2-spectrum.ecsv](Fig07_08/HESS-phaserange2-spectrum.ecsv), [Fig07_08/HESS-phaserange3-spectrum.ecsv](Fig07_08/HESS-phaserange3-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig07_08/MAGIC-phaserange04-spectrum.ecsv](Fig07_08/MAGIC-phaserange04-spectrum.ecsv), [Fig07_08/MAGIC-phaserange1-spectrum.ecsv](Fig07_08/MAGIC-phaserange1-spectrum.ecsv), [Fig07_08/MAGIC-phaserange2-spectrum.ecsv](Fig07_08/MAGIC-phaserange2-spectrum.ecsv)<br> - SEDs from VERITAS observations: [Fig07_08/VERITAS-phaserange04-spectrum.ecsv](Fig07_08/VERITAS-phaserange04-spectrum.ecsv), [Fig07_08/VERITAS-phaserange1-spectrum.ecsv](Fig07_08/VERITAS-phaserange1-spectrum.ecsv), [Fig07_08/VERITAS-phaserange2-spectrum.ecsv](Fig07_08/VERITAS-phaserange2-spectrum.ecsv), [Fig07_08/VERITAS-phaserange3-spectrum.ecsv](Fig07_08/VERITAS-phaserange3-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig07_08/XRT-phaserange04-spectrum.ecsv](Fig07_08/XRT-phaserange04-spectrum.ecsv), [Fig07_08/XRT-phaserange1-spectrum.ecsv](Fig07_08/XRT-phaserange1-spectrum.ecsv), [Fig07_08/XRT-phaserange2-spectrum.ecsv](Fig07_08/XRT-phaserange2-spectrum.ecsv), [Fig07_08/XRT-phaserange3-spectrum.ecsv](Fig07_08/XRT-phaserange3-spectrum.ecsv)<br> 6. Spectral energy distributions (orbit 9 and 17; Figure 10):<br> - SEDs from VERITAS observations: [Fig10/VERITAS-MJD55585-55600-spectrum.ecsv](Fig10/VERITAS-MJD55585-55600-spectrum.ecsv), [Fig10/VERITAS-MJD55600-55603-spectrum.ecsv](Fig10/VERITAS-MJD55600-55603-spectrum.ecsv), [Fig10/VERITAS-MJD55614-55623-spectrum.ecsv](Fig10/VERITAS-MJD55614-55623-spectrum.ecsv), [Fig10/VERITAS-MJD55624-55631-spectrum.ecsv](Fig10/VERITAS-MJD55624-55631-spectrum.ecsv), [Fig10/VERITAS-MJD58136-spectrum.ecsv](Fig10/VERITAS-MJD58136-spectrum.ecsv), [Fig10/VERITAS-MJD58141-spectrum.ecsv](Fig10/VERITAS-MJD58141-spectrum.ecsv), [Fig10/VERITAS-MJD58142-spectrum.ecsv](Fig10/VERITAS-MJD58142-spectrum.ecsv), [Fig10/VERITAS-MJD58143-spectrum.ecsv](Fig10/VERITAS-MJD58143-spectrum.ecsv), [Fig10/VERITAS-MJD58153-58154-spectrum.ecsv](Fig10/VERITAS-MJD58153-58154-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig10/MAGIC-MJD55585-55600-spectrum.ecsv](Fig10/MAGIC-MJD55585-55600-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig10/XRT-MJD55585-55600-spectrum.csv](Fig10/XRT-MJD55585-55600-spectrum.csv), [Fig10/XRT-MJD55600-55603-spectrum.csv](Fig10/XRT-MJD55600-55603-spectrum.csv), [Fig10/XRT-MJD55614-55623-spectrum.csv](Fig10/XRT-MJD55614-55623-spectrum.csv), [Fig10/XRT-MJD55624-55631-spectrum.csv](Fig10/XRT-MJD55624-55631-spectrum.csv), [Fig10/XRT-MJD58142-spectrum.csv](Fig10/XRT-MJD58142-spectrum.csv), [Fig10/XRT-MJD58143-spectrum.csv](Fig10/XRT-MJD58143-spectrum.csv), [Fig10/XRT-MJD58152-spectrum.csv](Fig10/XRT-MJD58152-spectrum.csv), [Fig10/XRT-MJD58153-spectrum.csv](Fig10/XRT-MJD58153-spectrum.csv)<br> 7. Contemporaneous X-ray and gamma-ray spectral energy distribution (Appendix D)<br> - SEDs from VERITAS observations: [Auxiliary/VERITAS*](Auxiliary/)</p>
NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>NSNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>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.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </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><strong>csvFilesForFigure1_DCOpaper.zip </strong># 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><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># 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> <p> </p> <p>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> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>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.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </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><strong>csvFilesForFigure1_DCOpaper.zip </strong># 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><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># 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> <p> </p> <p>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> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
On the binary origin of Be stars and the nature of exotic Be binary systems
<p>Despite the large impact of massive stars on their environment, important questions concerning their formation, evolution, and final explosions remain unanswered. One of them is the origin of the Be phenomenon, which occurs in ~20% of the early-type OB star. Observationally, Be stars are defined as B-type stars with Balmer line emission, indicative of a circumstellar decretion disk. While the presence of a disk is thought to strongly correlate with rapid rotation of the star, the processes that lead to such high rotation rates are still widely debated. One of the proposed mechanisms for the spin up of Be stars is mass and angular momentum transfer from an initially more massive companion in previous binary interactions. If merging is avoided, the mass donor is now a He-burning stripped star or a compact object. If this is the predominant channel for Be star formation, there are two important implications: first, there should be no Be + main sequence binaries on tight orbits; and second, Be stars should have a stripped star or a compact object as companion (unless the system has been disrupted). Here, we present observational evidence that the binary channel is predominant in Be star formation. We report on a lack of main-sequence companions to massive Be stars, and argue that the few known Be binaries are indeed exotic systems with stripped star or compact companions. We further present new analyses that shed light on the nature and evolutionary status of the two famous Be systems LB-1 and HR 6819 who have recently been reported to contain a black hole companion.</p>
Modeling of the optical spectra of the High-mass X-ray binaries IGR J17544-2619 and IGR J21343+4738
<p>We present the results of our long-term photometric and spectroscopic observations at the Russian–Turkish RTT-150 telescope for the optical counterparts of the High-mass X-ray binaries IGR J17544-2619 and IGR J21343+4738. Based on our optical data, we have determined for the first time the orbital and physical parameters of the IGR J17544-2619 by the methods of Doppler spectroscopy. We have calculated theoretical spectra for the optical counterpart by applying non-LTE corrections for selected lines and obtained the parameters of the stellar atmosphere (<em>T</em><sub>eff</sub> = 33 000 K, log <em>g</em> = 3.85, <em>R<sub>V </sub></em>= 9.5 R<sub>sun</sub>, and <em>M<sub>V</sub></em> = 23M<sub>sun</sub>). The variability of the Hα line in the optical Be star spectra of the IGR J21343+4738 is studied. It reflects the dynamic evolution of the equatorial disk of the optical star. Decrease of the equivalent width of the central absorption of the Hα line from 2006 to 2012 and from 2014 to 2019 is accompanied by a decrease in the photometric brightness of the system, which is explained by an increase in the radius of the equatorial disk of the Be star, which eclipses the star itself. In 2013, the disk size reached its maximum value and was destroyed. From 2014 to 2019 the process of accumulation of matter again began in the equatorial disk of the star, and it can be assumed that in the near future the source will again flare up in the X-ray range.</p>
Binary Classification as a Phase Separation Process (data repository)
<p><strong>For version 0.0.2 (from 2021) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>This is a second version, which I wrote using tensorflow. It is much smaller (5 Gb when decompressed), a remarkable improvement when compared to the more than 100 Gb of the previous version).</p> <p>The new files are </p> <ul> <li>PSBC_BCs.tar.gz</li> <li>PSBC_classifier_PCA.tar.gz</li> <li>PSBC_dataset.tar.gz</li> <li>PSBC_libs_grids_statistics.tar.gz</li> <li> PSBC_notebooks.tar.gz</li> </ul> <p>Their content is explained in the file README_v2.pdf</p> <p><strong>UPDATE: <a href="https://drive.google.com/drive/folders/18l_92HuHDWJDkZnvXRuyGedcyC_3YZ2M?usp=sharing">a Google Colab folder is also available</a>. You can also find all the data and libraries there, unpacked.</strong></p> <p>For usage, see the Git-hub. </p> <blockquote> <p><strong>NOTE)</strong> I will keep the content for the previous version available in my Github as well. It is still a "nice exercise" to do all that is done in this new version in numpy, as done there. <strong><em>(Or, I should say, they should be studied as a cautionary tale of what to avoid.)</em></strong></p> </blockquote> <p> </p> <p><strong>For version 0.0.1 (from 2020) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>Therein you will find</p> <ul> <li>Examples</li> <li>1D toy model examples</li> <li>Computational statistics</li> <li>Several trained PSBC on MNIST dataset, with different parameter configurations</li> <li>Extra simulations, investigating normalization properties, low dimensional models that fail due to "too much" model compression, and comparison among ANNs, KNNs, and the PSBC in 1D</li> </ul> <p>If you want to know</p> <ol> <li>how to read the data</li> <li>how to access computational statistics, raw data, and examples</li> <li>how to use the data stored in this data repository</li> </ol> <p>see the guide README.pdf on GitHub page at <a href="https://github.com/rafael-a-monteiro-math/Binary_Classification_Phase_Separation">Binary_Classification_Phase_Separation</a>, where a script that downloads (and organizes) all this data is also available ("download_PSBC.sh).</p> <p>I did not include a copy of the train-test set (0-1dubset of the MNIST database) in every folder with simulations. But you can find a copy of the normalized dataset in the tar ball "PSBC_Examples.tar.gz" as</p> <p>data_test_normalized_MNIST.csv and data_train_normalized_MNIST.csv.</p> <p> </p>
KU Car, a young massive eclipsing binary system in NGC 3324
<p>Massive stars are needed engines in the galactic clockwork for the chemical evolution of the Galaxy and the energetic input to the ISM. These stars suffer endemic multiplicity, and thus, studies of the origin and evolution of massive stars is tied to the study of binaries. Therefore, eclipsing binary systems are indispensable objects because absolute parameters (e.g. masses, radii, etc.) can be derived combining the study and modeling of radial velocity and light curves. Such absolute parameters derived from direct observations help to test models about the structure and evolution of massive stars. In this talk, we are presenting an analysis of the light and radial velocity curves of the eclipsing binary system KU Car (P=2.96 d). This binary, composed by a B0.5-0.7 V primary and an unseen companion, shows a remarkable light-curve with a sharp and deep primary minimum and a shallow secondary minimum with strong "reflection effect". Using PHOEBE 2.1 code for modeling, we have derived absolute parameters for both stars proposed the evolutionary stage. This Algol-like system dominated by a young early-B type star, is located in the center of NGC 3324, a bubble-like star-forming region also known as "Gabriela Mistral" Nebula. The photometric analysis is based on multi-band observations obtained using LCOGTN telescopes, and data gathered from ASAS-3, ASAS-SN, TESS, and GDS projects spanning a period of about 18 years. Radial velocities were determined using spectroscopic observations obtained at LCO/duPont and ESO-LSO/2p2 telescopes in the framework of "OWN Survey" and "B-SUGOS" projects.</p>
Massive overcontact binaries as progenitors of magnetic massive stars
<p>Recent MHD simulations have demonstrated that magnetic massive stars can be formed via mergers of massive binary systems. This coalescence is preceded by a contact phase, which, while expected to be common, is poorly understood due to a lack of observational constraints: less than ten O-type overcontact binaries are currently known. The nature and degree of internal mixing during the contact phase is extremely important to the final evolutionary outcome of these objects. If the mixing is efficient enough, the stars will enter the Chemically Homogeneous Evolution regime and, instead of expanding as they evolve, the stars may shrink. This pathway has been proposed as a way to form gravitational wave progenitors. If the mixing is less efficient, then these systems may merge, forming objects such as magnetic massive stars, Be stars, LBVs etc. By studying the temperature and chemical abundances, we can determine the degree of internal mixing during this phase, and thus constrain the future evolution. Here, we present a study of several massive overcontact systems in different metallicity regimes. Our findings indicate that, while these systems are rapidly rotating, there is no strong evidence of chemical adjustments on the surface. In fact, the abundances all appear to be consistent with expectations for non-rotating stars. Interestingly, however, all systems show elevated temperatures, and the components of each system lay very close to each other on the HR diagram. This is true even for the unequal mass systems, indicating that the more massive component drives the observed temperature for both components in the system. We show that these results are robust by demonstrating that they are reproducible by a more representative three-dimensional spectroscopic analysis approach as well. Finally, we discuss the implications of our findings for the formation of magnetic massive stars and for massive star evolution in general.</p>
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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.
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.