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5,462 results for “binary”

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zenodo40/100

Gaia DR3 detectability of unresolved binary systems

This upload contains the necessary data to construct the selection of unresolved binary systems in Gaia DR3 based on RUWE. The tutorial showing how to work with these data can be found here:  https://gaiaunlimited.readthedocs.io/en/latest/notebooks/dr3-unresolved-binaries.html The paper describing the data and methods is: <h2>Gaia DR3 detectability of unresolved binary systems</h2> <div>Alfred Castro-Ginard, Zephyr Penoyre, Andrew R. Casey, Anthony G.A. Brown, Vasily Belokurov, Tristan Cantat-Gaudin, Ronald Drimmel, Morgan Fouesneau, Shourya Khanna, Evgeny P. Kurbatov, Adrian M. Price-Whelan, Hans-Walter Rix and Richard L. Smart</div> <div> </div> <div>https://ui.adsabs.harvard.edu/abs/2024arXiv240414127C/abstract</div>

opencc-by-4.0May 2024View details →

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Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening

This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset. The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain)  calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format. The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF). A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts: 1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx). 2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).   The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.   If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.

opencc-by-4.0May 2024View details →

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The Santa Barbara Binary Code Comparison

We have performed numerical calculations of a binary interacting with a gas disk, using eleven different numerical methods and a standard binary-disk setup.  The goal of this study is to determine whether all codes agree on a numerically converged solution, and to determine the necessary resolution for convergence and the number of binary orbits that must be computed to reach an agreed-upon relaxed state of the binary-disk system. Title format: authorname_codename_resolution.dat "resolution" was user-defined; some used the number of zones along a single dimension, others used number of particles.  Because it is difficult to directly compare resolutions, we did not attempt a uniform definition for resolution in the file names. Data Format: ascii tables with columns including the following information 1   Time (in binary orbits, so be sure to divide by 2pi) 2   Total Gravitational Torque (r x F_grav). 3   Gravitational Torque outside r>a 4   PsiR  (This and following column are obsolete and not mentioned in the paper) 5   PsiI  (obsolete) 6   <e_x> (Eccentricity Vector) 7   <e_y>  8   Mdot onto Primary 9   Mdot onto Secondary

opencc-by-4.0Jan 2024View details →

zenodo40/100

Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift

Database of catalogs and numerical results for the paper: Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift.   ABSTRACT Hierarchical black hole (BH)  mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution.  We find that the evolution of the host GC  quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1 > 50 \, \msun$ regardless of the assumed BH pairing function.  At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density.  We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \msun$ for $z\geq{}2$. Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very  sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs in order to reproduce misaligned spin observations.   FILES DESCRIPTION: Files Catalogs.zip contain the data used in this paper.  The directory Metallicities contains the outputs of the Fastcluster runs at Z=0.0002. For each model and for each GC evolutionary case, we report the populations of BBHs at first ("first_generation.csv") and nth ("nth_generation.csv") generation.  The directory Merger_Rate_Density contains the catalogs from Cosmorate+Fastcluster at redshift 0 to 4 ("redshift_*.csv") and the merger rate density as a funcion of redshift ("merger_rate_density.csv"), for different GC models. Also, it contains the mixing fractions for all the models presented in this paper ("mixing_fractions.csv"). The Jupyter notebooks generate the Figures in the main body of the paper.

opencc-by-4.0May 2024View details →

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DF Tau: 1.3mm ALMA observations of the young binary DF Tau

The self-calibrated ALMA observations at 1.3mm wavelengths of the young binary system DF Tau, published in Grant et al., (2024). These observations are separated by epoch, and classified with the sufix LB or SB for "Long Baselines" and "Short Baselines". SB1 and LB1 are the observations from ALMA project ID:2019.1.01739.S (PI: Tofflemire, Benjamin), while SB2 and LB2 are from ALMA Project ID:2021.1.00854.S (PI: Long, Feng).

opencc-by-4.0Jun 2024View details →

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Binary data file needed for the Shen et al. (2011) equation of state implemented in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code

** this file is downloaded automatically from this repository on running Phantom ** Contains information needed to load the <a href="http://adsabs.harvard.edu/abs/2011PhRvC..83c5802S">Shen, Horowitz & Teige (2011)</a> equation of state for nuclear matter in Phantom simulations The data file is a binary data file that enables a fast read of the information listed in the ascii tables given in the supplementary material of the Shen et al paper. The original ascii data files can be found here: <a href="https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802">https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802</a> For information on how to read this file, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_shen.f90">eos_shen.f90</a>)

opencc-by-4.0Oct 2018View details →

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BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 3. Binary input stimulus dataset

If the input value is 1, then the neuron is likely to be part of the cohesive set and would update the weight and also the global and local counts each event. If the input value is 0, then the neuron is not likely to be part of the cohesive set. For this case, it would not update the weight value as it is 0. It does update the global count as that uses unit increments, but only updates the local count when an earlier trigger switch tells it to. Each update event is also counted, so that averaged totals can be produced.

opencc-by-4.0Aug 2016View details →

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Results from "Binary Reduction of Dependency Graphs"

The raw data of the reduction of the 238 bugs as extracted from the runs of the four algorithms ddmin, verify, closure, binary. An extra column also exists that describes the run of Binary Reduction on the list of classes directly. The columns in `deliverable.csv` are predicate (the name of the decompiler), name (the name of the project we ran on), size (number of classes), scc (number of strongly connected components). Then for each tool we have size, scc (final size and scc), iters (iterations to last success), total-iters (iterations before finishing), time (time to last success (s)), total-time (time before finishing), timeout (did the predicate timeout, or succeed), check (did the bug still exist after reduction) The `benchmarks.csv` covers basic statistics about the programs used in the results. Lib is the number of library classes, LOC is lines of source code in the program, classes are the number of classes, edges are the edges in the dependency graph, degree is the average in and out degree in the graph, scc is the number of strongly connected components, out_degree and in_degree is the median in and out degree in the graph.

opencc-by-4.0Feb 2019View details →

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gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples

This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>. The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> . The changes in this release are <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> The data and configuration files included remain the same as in the v2.1 release. This release includes : <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>

opencc-by-4.0Apr 2019View details →

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