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5,462 results for “Binary”
Dataset from the paper: "RR Lyrae From Binary Evolution: Abundant, Young and Metal-Rich"
<p>The two files contain the Tables presented in Appendix B of Bobrick & Iorio et al. (2024, MNRAS, 527, 12196–12218)<br>(https://ui.adsabs.harvard.edu/abs/2024MNRAS.52712196B/abstract) in CSV format.</p> <p># V3 updates</p> <p>- New columns added: GRRL, Gcomp, RRRL, Rcomp, and Teffcomp. These columns are not included in the published paper tables.<br>- The columns G and BP_RP were previously described as the Gaia G magnitude of the RRL, but they actually represent the G magnitude and BP–RP color of the entire system.<br>- The previous description of the columns was missing the PorbRRL entry.<br>- A new file, TableB2_SingleMadeBinaryRRL_V3.csv, has been added to replace the previous version, which had mismatched columns and some empty fields.<br>- The README now reports the format for both tables.</p> <p># Tables</p> <p>There are two tables included:</p> <p>## TableB1_BinaryMadeRRL_V3.csv</p> <p>This table contains the systems reported in Table B1 of the paper, with additional columns (marked with a +).</p> <p>### Columns:</p> <p>- **Age**: Age of the system since the zero-age main sequence [Myr]<br>- **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)<br> - TD1: Thin Disc – Bin 1 <br> - TD2: Thin Disc – Bin 2 <br> - TD3: Thin Disc – Bin 3 <br> - TD4: Thin Disc – Bin 4 <br> - TD5: Thin Disc – Bin 5 <br> - TD6: Thin Disc – Bin 6 <br> - TD7: Thin Disc – Bin 7 <br> - B: Bulge <br> - TKD: Thick Disc <br> - H: Halo <br>- **Mproj**: Progenitor ZAMS mass of the RRL [Msun]<br>- **Mcomp**: Progenitor ZAMS mass of the RRL companion [Msun]<br>- **Porb_init**: Initial orbital period [days]<br>- **feh**: [Fe/H] metallicity<br>- **MRRL**: RRL mass [Msun]<br>- **McompRRL**: Mass of the RRL companion [Msun]<br>- **PorbRRL**: Current orbital period [days]<br>- **McRRL**: Core mass of the RRL [Msun]<br>- **LRRL**: Bolometric luminosity of the RRL [Lsun]<br>- **Teff**: Effective temperature of the RRL [K]<br>- **G**: Gaia G-band magnitude of the system as a whole [mag]<br>- **BP_RP**: Gaia BP–RP color of the system as a whole [mag]<br>- **GRRL**: Gaia G-band magnitude of the RRL [mag] +<br>- **Gcomp**: Gaia G-band magnitude of the companion [mag] +<br>- **RRRL**: Radius of the RRL [Rsun] +<br>- **Rcomp**: Radius of the companion [Rsun] +<br>- **Teffcomp**: Effective temperature of the companion [K] +</p> <p>---</p> <p>## TableB2_SingleMadeBinaryRRL_V3.csv</p> <p>This table contains the systems reported in Table B2 of the paper, with additional columns (marked with a +).</p> <p>### Columns:</p> <p>- **Age**: Age of the system since the zero-age main sequence [Myr]<br>- **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)<br> - TD1: Thin Disc – Bin 1 <br> - TD2: Thin Disc – Bin 2 <br> - TD3: Thin Disc – Bin 3 <br> - TD4: Thin Disc – Bin 4 <br> - TD5: Thin Disc – Bin 5 <br> - TD6: Thin Disc – Bin 6 <br> - TD7: Thin Disc – Bin 7 <br> - B: Bulge <br> - TKD: Thick Disc <br> - H: Halo <br>- **Mproj**: Progenitor ZAMS mass of the RRL [Msun]<br>- **MRRL**: RRL mass [Msun]<br>- **McompRRL**: Mass of the RRL companion [Msun]<br>- **PorbRRL**: Current orbital period [days]<br>- **feh**: [Fe/H] metallicity<br>- **McRRL**: Core mass of the RRL [Msun]<br>- **LRRL**: Bolometric luminosity of the RRL [Lsun]<br>- **Teff**: Effective temperature of the RRL [K]</p>
Database of Accreting Binary Simulations
<p>We present <strong>DABS</strong> (Database of Accreting Binary Simulations), an open-access database of modelled Low Mass X-ray Binaries (LMXBs). DABS has been created using evolutionary tracks of neutron star and black hole LMXBs, spanning a large set of initial conditions for the accretor mass, donor mass, and orbital period. The LMXBs are evolved with the Convection and Rotation Boosted Magnetic Braking prescription. The most important asset of this online database is the tool <strong>PEAS</strong> (Progenitor Extractor for Accreting Systems) <a href="https://github.com/ChatrikMangat/progenlmxb">https://github.com/ChatrikMangat/progenlmxb</a>. This tool can be used to predict the progenitors of any user-entered LMXB system and view their properties before the start of mass transfer. This prediction can facilitate preliminary searches for the progenitors of observed LMXBs, which can help in streamlining further detailed analyses. The PEAS tool can also be used to constrain population synthesis techniques that specialize in supernova kicks in binaries and common envelope outcomes.</p>
Processed Synthetic Real-World Data for binary modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
Catalogue of Wide Binaries from GAIA EDR3
<p>This dataset contains candidate wide binary systems from Gaia EDR3, used to perform tests of Modified Gravity theories in the low acceleration regime; as shown in the paper "Wide Binaries from GAIA EDR3: preference for GR over MOND ?" by Charalambos Pittordis & Will Sutherland. Accepted by Open Journal of Astrophysics, 31 Jan 2023.</p> <p>There are two files:</p> <p><strong>CleanedWB_EDR3_Prlx300pc_Gmag20_20230111_Size73087_ZenodoSample.csv</strong>: .CSV table with 73087 rows and 230 columns.</p> <p><br> <strong>00README_WideBinaries_EDR3_PS2023.txt : </strong>README file, describing the columns within the above dataset.</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>
Dijkstra's Algorithm using a Fibonacci Heap, Binary Heap and Self-balancing Binary Tree
<p>Efficient C++ implementation of Dijkstra's algorithm using Fibonacci Heaps, Binary Heaps and Self-balancing Binary Trees. Also contains two .csv data sets from expeiments using directed planar graphs and random graphs of varying densities.</p> <p>Paper is published at </p> <p>Lewis, R. (2023), "A Comparison of Dijkstra's Algorithm Using Fibonacci Heaps, Binary Heaps, and Self-Balancing Binary Trees", <a href="https://arxiv.org/abs/2303.10034">arXiv:2303.10034</a>, <a href="https://doi.org/10.48550/arXiv.2303.10034">https://doi.org/10.48550/arXiv.2303.10034</a></p>
Major South African highway binary crack dataset
<p>Binary crack dataset prepared using flexible road pavement images from a major South African Highway.The dataset has been used in the publication titled: Convolutional Neural Networks for Crack Detection on Flexible Road Pavements which was presented at the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) and forms part of the conference proceedings published by Springer.</p>
Psychophysical thresholds for figure-ground discrimination driven by binary textures
<p>Documentation and matlab data files containing psychophysical data for figure-ground segregation driven by binary textures. Methodological details of measurements and analyses (along with interpretation) are provided in Victor, J.D., and Conte. M.M. (2022) Functional recursion of orientation cues in figure-ground separation. Vision Research 197, 108047, doi.org/10.1016/j.visres.2022.108047.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O3 search sensitivity estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Observing Run 3 (O3) Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' third observing run (O3).</p> <p>Details of the individual files can be found in</p> <ul> <li> o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the injected distributions and the HDF file format adopted.</p> <p>Separate files are provided for the two parts of the run, O3a and O3b, specified by the GPS start times and durations in the filenames, and for the entire O3 run (filename with no times specified).</p> <p>Separate files are also provided for subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. The subpopulations are combined into a single file (mixture) containing a mixture model that spans the union of all subpopulation.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546675 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O1+O2+O3 Search Sensitivity Estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>O1 + O2 + O3 Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' first (O1), second (O2), and third (O3) observing runs.</p> <p>Details of individual files can be found in</p> <ul> <li> o1+o2+o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the HDF file format adopted.</p> <p>Separate files are provided for individual subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. Additionally, a single file spanning the union of those mass ranges (mixture) is provided.</p> <p>Sensitivity estimates for O1 and O2 are available via semi-analytic methods (estimates of the optimal network signal-to-noise ratio). Sensitivity estimates for O3 are available from real search results. Analysts should specify detection thresholds separately for each type of sensitivity estimate (e.g., a signal-to-noise cut for O1+O2 and a false alarm rate cut for O3).</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636815 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
A highly magnetized environment in a pulsar binary system
<p>A highly magnetized environment in a pulsar binary system</p> <p><strong>Data availability:</strong></p> <ul> <li>IPV and DM, RM variations used for Fig 1 (Demo how to read the code and plot: plot_Fig1.py) <p>DM_Icut_60s_140114.txt, DM_Icut_60s_200621.txt, </p> <p>RM_140114_20s_rmfit.txt,RM_200621_20s_rmfit.txt, </p> <p>ILV_140114_20s.npy, ILV_200621_20s.npy</p> </li> <li>A full table of DM and RM variations (duplicated with the txt files, but with better readability): Li_DM_RM_Ter5A.xlsx</li> <li>Calibrated full stokes data with frequency information for Fig 2 (in psrfit format, can be read in with standard psrchive commands): VEGAS_Ter5A_200621_0001.zap.E2.calib.rot.f32</li> </ul> <p><strong>Code availability</strong></p> <ul> <li><em>The basic process of the raw data and DM, RM fitting:</em></li> </ul> <p>DSPSR (http://dspsr.sourceforge.net) PSRCHIVE (http://psrchive.sourceforge.net)</p> <ul> <li><em>Modeling and fitting:</em></li> </ul> <p>https://github.com/dongzili/Pol_prop_fitting</p> <p>NOTICE: If there are updated data due to new demands, the information or new URL will be posted in https://github.com/dongzili/Pol_prop_fitting</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Parameter estimation data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates from the second part of the third observing run (O3b).We provide results for the 35 candidates that have a probability of astrophysical origin of over 0.5, plus <a href="https://doi.org/10.3847/2041-8213/ac082e">GW200105_162426</a>, which is a clear outlier from the noise background. There are two .h5 files per event</p> <ul> <li>Cosmologically reweighted (*cosmo.h5)</li> <li>Not cosmologically reweighted (*nocosmo.h5)</li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. See the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analysed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><strong>A note about mixed samples:</strong> The samples provided here are produced using different waveform approximants. The Mixed label indicates that equal numbers of samples have been included from two different waveform approximants. For the binary black holes, these are IMRPhenomXPHM and SEOBNRv4PHM (for more details, see GWTC3p0PEDataReleaseExample.ipynb included in this data release and the paper). As different waveforms were analysed with different codes, there are sometimes differences in some parameters due to conventions in the codes. For example:</p> <ul> <li>As RIFT does not sample over time of coalescence as Bilby does, the RIFT time of coalescence results have a posterior distribution with a single spike, whereas the Bilby results have a distribution of peaks representing different sky positions for the source.</li> <li>There are different conventions for the range of the polarization angle (either 0 to π or 0 to 2 π). The parameter psi_wrapped maps all results to the range 0 to π, should consistency be important.</li> <li>The likelihood may show small differences when different sampling rates were used for Bilby and RIFT. The log-likelihood is expected to have a relative shift between the two runs of a few nats.</li> </ul> <p>Due to these differences, care must be taken when using Mixed samples, which will contain results using both codes' conventions. This should not impact the most interesting quantities, such as the masses, and so should only be rarely an issue.</p> <p>A <a href="https://doi.org/10.5281/zenodo.5117702">similar parameter-estimation release has been made to accompany GWTC-2.1</a> for results from the first part of the third observing run.</p> <p><strong>Sky localization data release</strong></p> <p>The sky localization tar file (IGWN-GWTC3p0-v2-PESkyLocalizations.tar.gz) contains candidate sky localizations corresponding to different parameter estimation configurations (.fits). Two waveforms are used for the majority of targets (IMRPhenomXPHM and SEOBNRv4PHM) and additional waveforms are used for possible neutron star--black hole mergers (see the <a href="https://dcc.ligo.org/LIGO-P2000318/public">paper</a> for further information). If you do not mind which waveform, the sky localizations labelled "Mixed" include posterior samples from both waveforms used. A machine readable list (skyLocalizationFileList.csv) of sky localization files is included within the .tar.gz file for ease of use, where the Mixed results are indicated as Default=True.</p> <p><strong>Contour data release</strong></p> <p>The contour tar file (IGWN-GWTC3p0-v2-PEContours.tar.gz) contains the contour files used to produce Figures 8 and 9 in the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a>. The python notebook (GWTC3p0PEPlotContourData.ipynb) explains how to reproduce these figures (and an interactive version of these plots can be accessed at <a href="https://gwtc3-contours.streamlit.app/">gwtc3-contours.streamlit.app/</a>).</p> <p><strong>Python notebook</strong></p> <p>The Python notebook (GWTC3p0PEDataReleaseExample.ipynb) explains how to read and use the posterior samples with a selection of examples.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546662 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
Individual datasets investigating combined toxicity of binary mixtures in bees from laboratory tests
<p>This excel file (DOI: https://doi.org/10.5281/zenodo.3383713) provides the individual datasets on binary mixture toxicity (mortality) in bees classified according to route and exposure patterns (i.e. oral, contact, acute and chronic) and mortality endpoints (e.g.LD<sub>50</sub>, LC<sub>50</sub>) for the honeybee (<em>Apis mellifera</em>) and wild bee species (<em>Osmia bicornis</em>, <em>Bombus terrestris</em>). 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e.g. LD<sub>50</sub>, LC<sub>50</sub>,) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i.e. topical application), chronic oral toxicity and acute oral toxicity, respectively. Combined toxicity data for binary mixtures were available as dose response data in honeybees for acute contact toxicity (n=92) and acute oral toxicity.</p> <p>The full data collection and analysis of binary mixtures are described in Carnesecchi et al., 2019 (DOI: 10.1016/j.envint.2019.105256)</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>
Basic Considerations for the Observability of Kinematically Offset Binary AGN
<p>KDE resampled massive black-hole (MBH) binary population used for the analysis in Kelley 2020, <em>Considerations for the Observability of Kinematically Offset Binary AGN</em> (2005.10255).</p> <p>We start with a population of MBH binaries derived from the Illustris cosmological simulations, and evolved using semi-analytic post-processing models. These methods are described in Kelley et al. 2016 (1606.01900) and Kelley et al. 2017a,b (1702.02180 and 1711.00075). We then use the kernel density estimation (KDE) technique to resample this population from the Illustris volume, into a sample of binaries from a full, simulated light-cone of observations. The binaries are resampled in bins of observed orbital period. The number of resampled binaries in a given period bin are restricted to 500,000. When this occurs, the 'weight' of those binaries is assigned a value greater than one, while the 'weight' of binaries in unrestricted period bins is set to unity. For example, if the 'true' number of binaries in a bin should have been 1e6, the number of samples would be restricted to 5e5, and the weight of each of those binaries would be set to 2.0. If the number of binaries in a bin were instead 2e5, which is less than the limit, then each of those binaries is given a weight of 1.0. Only a single realization of this sample is included, but additional realizations can be produced if needed and on request.</p> <p>The data are presented in hdf5 format and contain the following arrays:</p> <pre><code># Meta-Data `periods`: shape: (201,) units: seconds descr: the edges of the bins in observed orbital period from which the population was sampled. `counts`: shape: (200,) units: None descr: The number of binaries produced in each period bin. This number for each bin is capped at 500,000. The sum of these values equals the total number of samples (70622013). # Data In this particular realization of data, the number of sampled binaries is `S = 70622013`. `mtot`: shape: (S,) units: grams descr: the total mass of each binary (`M = M1 + M2`). `mrat`: shape: (S,) units: None descr: The mass ratio of each binary, `q = M2/M1 <= 1.0`, where M2 is the mass of the secondary (lower mass) component. `pobs`: shape: (S,) units: seconds descr: The orbital period of each binary in the observer's rest frame (at redshift, `z = 0`). `redz`: shape: (S,) units: None descr: The redshift of each binary. `weights`: shape: (S,) units: None descr: The number of 'true' binaries (i.e. the number that are expected to occur in this realization of an observed universe) represented by this individual 'sample', and having similar binary parameters.</code></pre> <p> </p>
Results for 2,230 UK Biobank binary and continuous traits
<p>Results for 2,230 UK Biobank binary and continuous traits. </p> <p>We applied the gene-based tests (Gene1D, Gene3D, GeneScan1D and GeneScan3D) to 1,403 UK Biobank binary phecodes and 827 continuous phenotypes (797 continuous traits + 30 biomarkers) using GWAS summary statistics on 28 million imputed variants. </p> <p>The results are in 3 different zipped folders: 'GeneScan3D_UKBB_1403binary_results.zip', 'GeneScan3D_UKBB_797continuous_results.zip' and 'GeneScan3D_UKBB_30biomarkers_results.zip'. A list of all 2,230 binary and continuous phenotypes is available in excel file 'UKBB_phenotype_description.xlsx'.</p> <p>Reference: Ma, S., Dalgleish, J. L ., Lee, J., Wang, C., Liu, L., Gill, R., Buxbaum, J. D., Chung, W., Aschard, H., Silverman, E. K., Cho, M. H., He, Z. and Ionita-Laza, I. "Improved gene-based testing by integrating long-range chromatin interactions and knockoff statistics", 2021</p>
An observed population of intermediate-mass helium stars that have been stripped in binaries - theoretical, computational and observational data
<p>This Zenodo repository contains the observational and computational data presented in the manuscript "An observed population of intermediate-mass helium stars stripped in binaries" by Drout, Götberg, Ludwig, Groh, de Mink, O'Grady and Smith.</p><p>We organize the data as follows:</p><ul><li>The stacked spectra presented in Figures S16-S21 are located in stacked_spectra.tar.gz, which contains a text file for each star. The text files have three columns that correspond to wavelength in Angstrom, normalized counts, and errors, respectively.<br> </li><li>The spectral model grid computed based on binary evolutionary model output and presented in detail in the Supplementary information section S1.2.1, is labeled with names starting S121. The file S121_evol_based_006_absolute_magnitudes.txt contains the absolute AB magnitudes for the models in UV and optical filters. The .tar.gz S121_evol_based_006_spectra.tar.gz contains files with the full spectral energy distribution and normalized spectra of each model. The .tar.gz S121_evol_based_006_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>For the stellar atmosphere model grid presented in Supplementary material section S1.2.2, we refer to the Zenodo repository 10.5281/zenodo.7976200, which is made available in association with the second paper in our series. We note that we used a subset of that grid in the article associated with this Zenodo repository. We refer to section S1.2.2 for more details.<br> </li><li>The spectral models demonstrating the mass loss rate variations in Supplementary information section S1.2.3 are presented here with names starting with S123. There is one file containing the absolute magnitudes (S123_mdot_variation_absolute_magnitudes.txt), the S123_mdot_variation_spectra.tar.gz contains the spectral energy distributions and normalized spectra for each of the models, and the S123_mdot_variation_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>The spectral model grid computed based on main-sequence evolutionary model output and presented in detail in the Supplementary information section S1.3.1, is labeled with names starting S131. The file S131_MS_evol_based_006_absolute_magnitudes.tar.gz contains three files with the absolute AB magnitudes for the models in the UV and optical filters, each file corresponding to either 20%, 60%, or 90% through the main-sequence evolution and labeled f20, f60, and f90, respectively. S131_MS_evol_based_006_spectra.tar.gz contains three folders labeled f20, f60 and f90, which each contains the SEDs (in Flambda and ABmag) and normalized spectra for the corresponding models. The files S131_MS_evol_based_006_complete_models_fX0.tar.gz contain the complete CMFGEN models.<br> </li><li>The custom index files we use for astrometry.net in section S3.1.1 are located in the zip file called S311_astrometry_index_files.zip. This information was used to recalculate the astrometry on the Swift UVOT images of the Magellanic Clouds.<br> </li><li>To make Figure 2B, we calculated the equivalent widths for a set of models assuming a signal-to-noise ratio of 35. This procedure is described in Section S3.7.2. In Figure2B_Model_EWs.zip, we provide three files that each contain these modeled equivalent widths for (1) stripped star models, (2) OB star models, and (3) composite models. <br> </li><li>To make Figure S7 (see also Sections S1.2.3 and S2.2), which is similar to Figure 2B, but presents the effects of varying the wind mass loss of stripped stars, we used a similar set of modeled equivalent widths as when we produced Figure 2B. These modeled equivalent widths are provided in FigureS7_Model_EWs.zip. <br> </li><li>To make Figure 3, we calculated equivalent widths for the model grid described in Section S1.2.2 and the TLUSTY OB star grids (see Section S1.3.2) assuming a signal-to-noise ratio of 100. These model equivalent widths are provided in the file called Figure3_Model_EWs.zip. </li></ul>
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>
Phase Diagram of Kob-Andersen-Type Binary Lennard-Jones Mixtures
<p>This data repository contains data related to the paper Phase Diagram of Kob-Andersen-Type Binary Lennard-Jones Mixtures, Phys. Rev. Lett. 120, 165501 (2018), DOI: <a href="https://doi.org/10.1103/PhysRevLett.120.165501">10.1103/PhysRevLett.120.165501 </a>by Ulf R. Pedersen, Thomas B. Schrøder, and Jeppe C. Dyre.</p><p> </p><p>Abstract of the paper:</p><p>The binary Kob-Andersen (KA) Lennard-Jones mixture is the standard model for computational studies of viscous liquids</p><p>and the glass transition. For very long simulations, the viscous KA system crystallizes, however, by phase separating</p><p>into a pure A particle phase forming a fcc crystal. We present the thermodynamic phase diagram for KA-type mixtures</p><p>consisting of up to 50% small (B) particles showing, in particular, that the melting temperature of the standard KA</p><p>system at liquid density 1.2 is 1.028(3) in A particle Lennard-Jones units. At large B particle concentrations, the</p><p>system crystallizes into the CsCl crystal structure. The eutectic corresponding to the fcc and CsCl structures is cutoff</p><p>in a narrow interval of B particle concentrations around 26% at which the bipyramidal orthorhombic PuBr3 structure is</p><p>the thermodynamically stable phase. The melting temperature's variation with B particle concentration at two constant</p><p>pressures, as well as at the constant density 1.2, is estimated from simulations at pressure 10.19 using isomorph</p><p>theory. Our data demonstrate approximate identity between the melting temperature and the onset temperature below which</p><p>viscous dynamics appears. Finally, the nature of the solid-liquid interface is briefly discussed.</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.