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5,462 results for “binary”
Table A.4: Spectroscopic binaries, triples, and quadruples
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Table A.2: Bona fide binaries with astrometric anomalies
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Table A.7: Binaries with separations larger than 10000 au
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Alternative Builds dataset for Levels of Binary Equivalence paper
<p>The availability of multiple binaries built from the same sources creates new challenges and opportunities, and raises questions such as: “Does build A confirm the integrity of build B?” or “Can build A reveal a compromised build B?”. We introduce levels of equivalence, inspired by clone detection types, and demonstrate the value of these new levels through several experiments. For this purpose, we construct a dataset consisting of Java binaries (jar files) built from the same sources independently by different providers, resulting in 14,156 pairs of binaries in total. </p>
The Demographics of Giant Exoplanets and Brown Dwarfs in Wide Stellar Binaries
<p>While numerous exoplanets have now been uncovered in stellar binaries, the impact of companion stars on planet formation and evolution is still not understood. In this talk, I will present results of population trends seen among the known sample of planets in wide multiple star systems, which allows us to investigate the effects of stellar binarity on the resulting planetary architectures. In particular, observations of stars hosting close-in giant planets and brown dwarfs find an excess of binary companions on few hundred AU separations, and different planet demographics for these systems, suggesting that such binaries may provide favourable conditions for the formation of the observed inner companions. I will show results from simulations of self-gravitating protoplanetary disks adapted to binary-star environments, which show that certain binary configurations may trigger gravitational fragmentation and lead to the formation of giant planets in otherwise-stable disks.</p>
Applying the metallicity-dependent binary fraction to double white dwarf formation, Implications for LISA, results
This dataset contains data created by post-processing pipeline to produce the figures from Thiele+2021<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/katiebreivik/hush/tree/ae41b3fbc87d9ef411fec9fd464958c6ab367a2e">this GitHub repo</a> using the following command: <pre><code class="language-bash">cd src/figures && python src/figures/pipeline.py</code></pre>
The eclipsing binary systems with delta Scuti component. AB Cas
<p>A set of inlists for MESA v12115, used to compute a grid of binary evolutionary tracks used in the paper "The eclipsing binary systems with delta Scuti component. AB Cas".</p>
Particle tracer and horizon data of equal-mass, binary neutron stars simulations
<p>We provide the particle tracer and black hole (BH) horizon data obtained from two binary neutron star (BNS) simulations performed with IllinoisGRMHD.</p> <p>The first dataset, MissingLink_gammalaw_particle_data.tar.bz2, contains data from a magnetized, equal-mass BNS simulation of the missing link initial data. The equation of state (EOS) used was a simple gamma-law with Gamma=2.</p> <p>The second dataset, Magnetized_equalmass_BNS_LS220_particle_data.tar.bz2, contains data from a magnetized, equal-mass BNS simulation that employs an advanced, tabulated EOS—LS220.</p> <p>Both simulations have gone through inspiral, merger, and the formation of a remnant hypermassive neutron star that eventually collapses to a BH.</p>
Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries
<p>This dataset is for Section 5.4 of the paper. The 32-bit ARM binaries and the objdumps we scraped from the package repositories of three Linux distributions were too large to upload to GitHub, unlike the rest of our data on our submission.</p> <p><strong>Size warning </strong>- when decompressed, this dataset is large. The following are the sizes of each subset before compression:</p> <ul> <li>centos-scrape: 2.2 GB</li> <li>centos-scrape-objdumps: 11.0 GB</li> <li>raspbian-scrape: 10.0 GB</li> <li>raspbian-scrape-objdumps: 52.1 GB</li> <li>ubuntu-scrape: 5.6 GB</li> <li>ubuntu-scrape-objdumps: 34.5 GB</li> </ul> <p>(Total size: 115.4 GB)</p> <p> </p>
Simulated astrometric binary systems
<p>Simulated systems analyzed in Penoyre, Belokurov & Evans 2022.</p> <p>As detailed in the paper we generate 100,000 random binary configurations with 100 pc and simulate each as:</p> <ul> <li>a single star (l=q=0)</li> <li>a stellar binary (l and 1 generated by drawing a second random star, l<1 always)</li> <li>a solar mass black hole (l=0)</li> <li>a three solar mass black hole</li> <li>a ten solar mass black hole</li> </ul>
Networkx graph generation algorithm sample binary graphs
<p>Example graph binary object from Networkx's random graph generation algorithms</p> <ul> <li>gnp: Erdős-Rényi graph</li> <li>gnx: Random graph</li> <li>regular: Random regular graph</li> <li>barabasi: Graph from Barabási-Albert preferential attachment model</li> </ul> <p>Parameters Used:</p> <ul> <li>P = 0.015</li> <li>N = 200</li> <li>DEGREE = 20</li> <li>SEED = 0</li> </ul>
General-relativistic precession in a black-hole binary - data release
<p>This page contains the data release associated with the publication <a href="https://www.nature.com/articles/s41586-022-05212-z">General-relativistic precession in a black-hole binary</a> from Hannam, M. <em>et al.</em> In this publication we show that one of the events in the <a href="https://arxiv.org/abs/2111.03606">most recent LIGO-Virgo-Kagra (LVK) data release</a> exhibits general relativistic orbital precession. The event is GW200129_065458.<br> <br> Here, we release the posterior samples (<code>*.h5</code>) that were obtained in this analysis. The files are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/index.html">PESummary</a>. For information about how to download and read the files, see the <a href="https://data.cardiffgravity.org/GW200129-precession/">public documentation</a>.<br> <br> If you use the material provided here, please cite the paper and this data release.</p>
Astrometrically identified nearby binary stars
<p>22,699 astrometric binary candidates identified via Gaia astrometry from the <a href="https://ui.adsabs.harvard.edu/abs/2021A%26A...649A...6G/abstract">Gaia Catalog of Nearby Stars</a>.</p> <p>Details of the specific data products and how the sample was selected can be found in <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.513.5270P/abstract">Penoyre, Belokurov & Evans 2022</a>, with a description of the specific data included here provided in Table 2.</p>
Light Axion Emission and the Formation of Merging Black Holes Binaries
<p># Reproduction Package for the Paper "Light axion emission and the formation of merging binary black holes"</p> <p>## Authors:<br> Djuna Croon (djuna.l.croon@durham.ac.uk) <br> Jeremy Sakstein (sakstein@hawaii.edu) </p> <p>## Software</p> <p>MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0 </p> <p>## Example Directories</p> <p>**work:** Contains inlists, run\_star\_extras, and run\_binary_\extras needed to reproduce our results. These were adapted from the reproduction package of A&A 650, A107 (2021). A small number of models did not converge using the default controls. These can be made to complete by either setting the control make\_gradr\_sticky\_in\_solver\_iters = .true. (see inlist1, this works for models with small initial periods) or by relaxing delta\_HR\_limit and delta\_HR\_hard\_limit (see run\_binary\_extras line 900).</p> <p>## Citation Policy</p> <p>If you use any part of this reproduction package for independent work we recommend you cite the following papers:</p> <p>- <a href="https://arxiv.org/abs/2208.01110">https://arxiv.org/abs/2208.01110</a><br> - Phys.Dark Univ. 32 (2021) 100801<br> - Phys.Rev.D 102 (2020) 11, 115024<br> - Phys.Rev.Lett. 125 (2020) 26, 261105<br> - Astrophys.J.Lett. 916 (2021) 2, L16<br> - Phys.Rev.D 105 (2022) 095038<br> - A&A 650, A107 (2021)<br> - Astrophys. J. Suppl. 192, 3 (2011)<br> - Astrophys. J. Suppl. 208, 4 (2013)<br> - Astrophys. J. Suppl. 234, 34 (2018)<br> - ApJS 243, 10 (2019) </p>
Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?
<p>Input files and data used in the paper "Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?". MESA models were computed with version 12115 of MESA. COSMIC models new computed with version 3.4. README file included. </p>
Double-lined spectroscopic binaries in the open cluster M 11 (NGC 6705)
<p>Figures with spectral fits for 265 Gaia-ESO spectra for open cluster M 11 (NGC 6705), analysed by single-star and binary spectroscopic models. </p> <p>Kovalev M., Straumit I., 2022, MNRAS, 510, 1515</p> <p>also used in arxiv 2207:06996</p>
Optical polarimetric observations of black hole binary Cyg X-1 with DIPol-2
<p>The dataset contains raw polarimetric FITS images of black hole X-ray binary <a href="https://en.wikipedia.org/wiki/Cygnus_X-1">Cyg X-1</a> (and surrounding field), obtained with the DIPol-2 optical CCD polarimeter in three (<em>BVR</em>) filters while mounted on the remotely operated Tohoku 60 cm (T60) telescope at the Haleakala Observatory, Hawaii. The data were collected over 5 observing nights during the week 2022 May 15–21 for about 4 hours each night. During each observing night, a set of calibration images were also obtained. These typically include 7 dark and 7 bias images per filter per night. Bias and dark FITS files have `bias` or `dark` labels in their names.</p>
Evolution of binary gender and geographic diversity in the editorial boards of Geochimica et Cosmochimica Acta and Chemical Geology
<p>Dataset from article</p> <p><strong>Evolution of diversity in the editorial boards of <em>Geochimica et Cosmochimica Acta </em>and <em>Chemical Geology</em> </strong></p> <p>by Pourret et al. published in European Science Editing</p>
Benchmarking Dynamic Binary Value Problems with IOHprofiler -- Reproducibility files
<div> <div>This repository contains the code, data and scripts to reproduce the results from the paper "<span>Empirical Analysis of the Dynamic Binary Value Problem </span><span>with IOHprofiler</span>".</div> <br> <div>The files are split into 3 parts:</div> </div> <h3><br>Part 1: Data collection</h3> <div> <div>The data can be collected using the data_collection.py file. In its default state, this will collect the full set of experimental data used in the paper. This can be modified by adjusting the main to only execute the relevant parts of the experiments. Note that the data location and number of parallell processes can be adjusted at the top of this file.</div> <br> <div>To run the data collection, the only requirement is to install multiprocessing (any version from pip) and iohexperimenter. The version of ioh should be installed from github https://github.com/IOHprofiler/IOHexperimenter/tree/dynamic-bin-val (installation instructions can be found on the github page). Alternatively, the future pip-release 0.3.16 will include these changes as well.</div> </div> <div><br> <h3>Part 2: Data</h3> <div>Instead of rerunning the full experimental data collection, the results of this have also been made available. Each set of experiments is provided as a 'SetX.zip' file.</div> <br> <h3>Part 3: Visualization</h3> <div>To analyze and visualize the data, the provided notebook can be used. This notebook takes the data from either step 1 or 2 and uses it to create the figures presented in the paper (plus some variations).</div> <div>The resulting figures are also provided on Figshare: doi.org/10.6084/m9.figshare.25592904</div> </div> <p> </p>
Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries
<p>This is the data repository for the paper "Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries"</p>
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.