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5,462 results for “binary”
Inlists for "An Alternative Channel to Black Hole Low-Mass X-ray Binaries: Dynamical Friction of Dark Matter? "
<p>In this work, we employ MESA code to diagnose whether the dynamical friction between dark matter and the companion stars can drive BH binaries to evolve toward the observed BH LMXBs and alleviate the effective temperature problem. Assuming that there exists a density spike of dark matter around BH, the dynamical friction can produce an efficient angular momentum loss, driving BH binaries with an intermediate-mass companion star to evolve into BH LMXBs for a spike index higher than $\gamma = 1.58$.</p> <p>MESA version: r12115; SDK compiler: mesasdk-x86_64-linux-20190830</p> <p> </p>
Population synthesis data of massive binaries: NS/BH-core mergers and double compact objects formation and mergers
<p><span>Binary population synthesis data generated using the rapid population synthesis code COMPAS.</span></p> <p><span>Contains:</span></p> <p><span>1. Full COMPAS output in (.csv format) for running massive binaries varying the common envelope efficiency parameter and the metallicity.</span></p> <p><span>2. Identifying seeds of systems that result in neutron star (NS)/black hole (BH)-core mergers, binary NSs, binary BHs and NS-BH binaries. </span></p>
Reconstruction of eccentric binary black hole gravitational wave signals using deep learning
<p>Dataset of deep learning-based reconstruction of simulated binary black hole gravitational wave signals with non-zero eccentricity. The details of the deep learning models and studies conducted can be found in the following papers: 1. https://arxiv.org/abs/2403.01559 and 2. https://arxiv.org/abs/2406.06324.<br><br>The data consists of 1 sec long whitened BBH injections ('Injection_signal') sampled at 2048 Hz, and their corresponding reconstructions ('Mean_reconstruction'), alongwith 50% ('Lower_50, Upper_50') and 90% ('Lower_90, Upper_90') C.I. of the reconstructions. The eccentricity values are labelled 'Eccentricity'. The masses and SNR of these signals are 30+30 solar mass and 12 respectively.</p>
Artifact for "Towards Deterministic Compilation of Binary Decision Diagrams From Feature Models"
<p>This artifact supplies the tooling and original data used in the evaluation of the corresponding submission #52 `Towards Deterministic Compilation of Binary Decision Diagrams From Feature Models'' accepted at SPLC'24. The artifact allows reproducing all the figures and tables of the submission. In addition, the artifact allows for easy replicating of the results for additional input models.</p>
Equipment Design for a Small Binary System Power Plant Using Geothermal Heating Fluid A Case Study
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Benchmarks for Comparing Binary-Level Symbolic Execution Speed
<p>This dataset contains a reproducible computing environment as well as a set of synthetic benchmark applications for comparing symbolic execution speed at the binary level of several symbolic execution engines supporting the 32-bit RISC-V architecture. Specifically, the dataset compares and supports the following engines:</p> <ul> <li><a href="https://github.com/binsec/binsec">BinSec</a></li> <li><a href="https://github.com/angr/angr">angr</a></li> <li><a href="https://github.com/agra-uni-bremen/symex-vp">SymEx-VP</a></li> <li><a href="https://github.com/agra-uni-bremen/binsym">BinSym</a></li> </ul> <p>The benchmarks are designed to be easily extendable through the utilization of <a href="https://guix.gnu.org">Guix</a> (a functional package manager for <a href="https://doi.org/10.1038/s41597-022-01720-9">long-term reproducible research</a>). Currently, the supported engines perform IR-based symbolic execution using binary lifting (BinSec & angr) and direct symbolic execution of RISC-V machine code (SymEx-VP & BinSym). The dataset compares these approaches by fully exploring synthetic benchmark applications (e.g. sorting algorithms) with unconstrained symbolic input values and calculating an arithmetic mean over multiple executions, which is then represented as a table and a plot.</p> <p>More information on using the dataset is available in the supplementary README file.</p>
Stability and electronic properties of K-Sb and Na-Sb binary crystals from high-throughput ab initio calculations
<p>This is the dataset to the identically named publication. All data is stored in an AiiDA archive.</p> <p>In a high-throughput analysis based on density-functional theory (DFT) with the advanced r2SCAN functional, we focus on the two families of binary crystals with K-Sb and Na-Sb compositions that are expected to form during evaporation growth of multi-alkali antimonide photocathodes. Starting from an initial pool of structures mined from existing computational databases, we employed automatized routines included in the in-house developed library aim2dat to determine the stability and the electronic properties of the aforementioned systems. We analyze the formation energy, the band structure and the projected density of states of selected stable compounds.</p>
Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)
<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>
Binary black-hole simulation SXS:BBH:1757
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1706
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:2035
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:2076
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1905
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1961
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1937
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1645
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1526
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1617
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1550
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:1805
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
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.