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5,462 results for “binary”
Binary black-hole simulation SXS:BBH:0259
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:0295
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:0303
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:0299
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
binary_c output files to reproduce "Massive runaway and walkaway stars"
<p>Raw binary_c output for the paper "<a href="https://ui.adsabs.harvard.edu/abs/2019A%26A...624A..66R/abstract">Massive runaway and walkaway stars: A study of the kinematical imprints of the physical processes governing the evolution and explosion of their binary progenitors</a>" by Renzo et al. <br><br>The tarball is ~21GB zipped, and unzips to >250Gb file with all the parameter variations.<br><br></p>
Synthetic Datasets for "Binary Classification Optimisation with AI-Generated Data"
<p>Images of melanomas and Basal Cell Carcinoma generated with a stylegan2. Dataset corresponding to the article "Binary Classification Optimisation with AI-Generated Data"</p>
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </p>
Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures
<p>The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: <br>"Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures".</p> <p>0. DATA</p> <p>FOLDER: 0_Data<br>This folder contains the raw phenotypic and genotypic data.</p> <p>I. FILE PREPARATION</p> <p>FOLDER: 1_FilePrep_Design_Fig<br>The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes.</p> <p>II. PHENOTYPIC ANALYSIS </p> <p>FOLDER: 2_GeneticEffects_PhenoCorr_Residus<br>The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models.</p> <p>III. A. DGE-BASED GWAS</p> <p>FOLDER: 3_DGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P).<br>For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype.</p> <p>III. B. Analysis of GWAS Results for DGE</p> <p>FOLDER: 3_DGE_GWAS<br>Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>IV. A. IGE-BASED GWAS</p> <p>FOLDER: 4_IGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P).<br>For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype.</p> <p>IV. B. Analysis of GWAS Results for IGE</p> <p>FOLDER: 4_IGE_GWAS<br>Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>V. PREPARATION OF FILES FOR CO-GWAS</p> <p>FOLDER: 5_FilePrep_coGWAS<br>The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning.<br>The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning.<br>The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning.</p> <p>VI. CO-GWAS</p> <p>FOLDER: 6_coGWAS<br>This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P). <br>For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype.<br>The other scripts in this folder combine the result files.</p> <p>VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS</p> <p>FOLDER: 7_Heatmaps_qqplots_coGWAS<br>This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P).<br>For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot.</p> <p>VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS</p> <p>FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS<br>This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P).<br>For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions. </p> <p>IX. CIRCULAR PLOTS </p> <p>FOLDER: 9_CircularPlots_coGWAS<br>This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P).<br>For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot. <br>The script circos.conf creates the circular plot. </p> <p>X. GO ENRICHMENT ANALYSIS </p> <p>FOLDER: 10_GOterms_coGWAS<br>This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P).<br>For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.</p>
Inlists used for studying the astrophysical properties of the Sirius binary system
<p>Using mesa-r23.05.1 a study of the sirius system was performed, both as a non-interacting binary, as well as test suites that take sirius b up until the wd cooling phases in an attempt to better understand the age of the system and the limits of the progenitor mass. Additionally, we hope to put constraints on the temperature, luminosity, and white dwarf mass by comparing this simulation to HST data. Lastly, we use these models to calculate an initial to final mass relation for the system.</p> <p>The inlists used have been included as well as some mod files for different metallicities and initial masses. It is split into three sections pertaining to the binary suite, and single star evolutions where we create carbon and oxygen white dwarfs for lower initial masses of sirius b, as well as a test suite that creates a neon oxygen white dwarf for larger initial masses.</p> <p>The mesasdk version is from July of last year (version 23.7.3)</p>
Astrosat/UVIT reduced images and HST/STIS merged spectra for the red-giant binary 2MASSJ05215658+4359220, published by Bianchi, L et al. 2024, ApJ ( DOI: 10.3847/1538-4357/ad712f ; arxiv:2409.06906)
<p>Reduced and calibrated datasets used in the paper: </p> <p>Bianchi, Luciana; Hutchings, John; Bohlin, Ralph; Thilker, David; and Berti, Emanuele 2024, ApJ, in press; DOI: 10.3847/1538-4357/ad712f ; <a href="https://arxiv.org/abs/2409.06906" target=" ">arxiv:2409.06906</a><br>Title:<em> Revealing the elusive companion of the red-giant binary 2MASSJ05215658+4359220 from UV HST and Astrosat/UVIT data.</em></p> <p>See<em> </em>the original paper for details on the data reduction. Please reference the original paper if using these datasets. </p> <p><em>1) </em>STIS_2mj05215.mrg</p> <p>The file includes the three-ranges spectra extracted and combined: G230L+G430L+G750L. Ascii file. Columns are: WAVELENGTH (˚A), COUNT-RATE (counts cm−2 s−1 pixel−1), FLUX (ergs cm−2 s−1 ˚A−1), STAT-ERROR (ergs cm−2 s−1 ˚A−1), SYS-ERROR (ergs cm−2 s−1 ˚A−1), NPTS (the number of co-added spectra for that pixel), TIME (total exposure time at that pixel in seconds), QUAL ( 1=good data, 0=possibly poor data quality). The DOI: 10.17909/tvvw-4e47 refers to the original HST data, not to te reduced and combined dataset.</p> <p>The file is also available from the author's website at : http:/dolomiti.pha.jhu.edu/uvsky/RGbinary_2MASSJ05215658+4359220/STISspectra/STIS_2mj05215.mrg</p> <p>2) UVITimages.tar. Tar file containing the UVIT images, reconstructed from the photon-counting archival data, calibrated accounting for dead-time corrections, in .fits format</p> <p>The tar file contains the following .fits files of the reconstructed images, where the “NORM EXPARRAY” files are the normalized relative response maps across the field, expressed as 0 to 1. So, the effective exposure time at a pixel position is RDCDTIME multiplied by this image. The ”NORMEXP IMAGE” is the science image, that must be divided by the product of RDCDTIME and the NORM EXPARRAY image on a pixel-by-pixel basis, to obtain counts s−1 pixel−1</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORM_EXPARRAY_A10_093T01_9000004128.fits</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORM_EXPARRAY_A10_093T01_9000004266.fits</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORM_EXPARRAY_Merged.fits</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORMEXP_IMAGE_A10_093T01_9000004128.fits</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORMEXP_IMAGE_A10_093T01_9000004266.fits</p> <p>2MASSJ05215658+4359220_FUV_CaF2___MASTER_NORMEXP_IMAGE_Merged.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORM_EXPARRAY_A10_093T01_9000004128.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORM_EXPARRAY_A10_093T01_9000004266.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORM_EXPARRAY_Merged.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORMEXP_IMAGE_A10 093T01_9000004128.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORMEXP_IMAGE_A10_093T01_9000004266.fits</p> <p>2MASSJ05215658+4359220_FUV_Sapphire___MASTER_NORMEXP_MAGE_Merged.fits</p> <p>The tar file is also available from the author's website at: http:/dolomiti.pha.jhu.edu/uvsky/RGbinary_2MASSJ05215658+4359220/UVITimages/UVITimages.tar</p>
Data for "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system"
<p>Crystal structures, high-throughput calculations and trained machine learning models presented in the paper "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system".</p> <ul> <li><em>crystal_datasets </em>contains the input/output data sets of crystal structures for high-throughput calculations and ML models.</li> <li><em>aiida_ht_calculations </em>contains the data regarding the high-throughput DFT calculations.</li> <li><em>ml_models</em> contains the trained ML models.</li> </ul> <p>Eeach zip-archive contains a jupyter-notebook examplifying how the data can be accessed and reused.</p>
Iron Bonding with Light Elements: Implications for Planetary Cores Beyond the Binary System
<p>X-ray Absorption Spectroscopy and DFT calculation dataset for Iron alloys at high pressures. <br><br></p> <p><strong>Abstract: </strong>Light element alloying in iron is required to explain density deficit and seismic wave velocities in Earth’s core. However, the light element composition of the Earth’s core seems hard to constrain as nearly all light element alloying would reduce the density and sound velocity (elastic moduli). The alloying light elements include oxidizing elements like oxygen and sulfur, and reducing elements like hydrogen and carbon, yet their chemical effects in alloy system are less discussed. Moreover, Fe-X-ray Absorption Near Edge Structure (Fe-XANES) fingerprints have been studied for silicate materials with ferrous and ferric ions, while not many X-ray absorption spectroscopy (XAS) studies have focused on iron alloys, especially at high pressures. To investigate the bonding nature of iron alloys in planetary interiors, we presented X-ray absorption spectroscopy of iron-nitrogen and iron-carbon alloys at high pressure up to 50 GPa. Together with existing literature on iron-carbon, -hydrogen alloys, we analyzed their edge positions and found no significant difference of degree of oxidation among these alloys. Pressure effects on edge positions were also found negligible. Our theoretical simulation of the valence state of iron, alloyed with S, C, O, N, P also showed nearly unchanged behavior under pressures up to 300 GPa. This indicates the high pressure bonding of iron alloyed with light elements closely resembles bonding at the ambient conditions. We suggest that the chemical properties of light elements constrain which ones can coexist within iron alloys.<span> </span></p>
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
<p>Data repository for the paper "Beating classical heuristics for the binary paint shop problem with the quantum" by Michael Streif, Sheir Yarkoni, Andrea Skolik, Florian Neukart, and Martin Leib.</p> <p> </p>
Novel gyrochronology tests with wide-separation binaries
<p>Gyrochronology is the technique that uses rotation periods to derive stellar ages via age-rotation relations. If properly calibrated, its potential applications to Galactic, stellar and exoplanetary astrophysics are far-reaching. Thanks to the exquisite high-precision photometry provided by space-based missions such as Kepler, TESS, and the upcoming PLATO, the number of stars with measured rotation periods will continue to drastically increase in the coming years. Accordingly, it has become imperative to comprehensively test gyrochronology, so its remarkable applications can be fully exploited, and its limits well understood. This work presents a novel technique that tests the state-of-the-art age-rotation relations in under-explored domains using wide binary stars. The components of a given binary are co-eval and taken together they can provide exquisite assessments of gyrochronology. The results of a dedicated test on a sample of Kepler-field binaries and star clusters show that: 1) statistically, the commonly used age-rotation relations do have predictive power in identifying co-eval populations of field stars; 2) they achieve the best results when used in the members of young clusters (ages < 1 Gyr); 3) their performance decreases when used in field wide binaries, as well as in older clusters. All in all, the existing relations need better calibrations, particularly in the regime of ages older than a few Gyr. This work sets up the stage for decisive gyrochronology assessments in the era of ever-expanding space-based photometry.</p>
Evolutionary Origins of Binary Neutron Star Mergers
<p>Input and data files required to reproduce Figures 1 and 2 from Gallegos-Garcia et al 2022, Evolutionary Origins of Binary Neutron Star Mergers.</p>
Evolution of eccentric High-mass X-ray binaries. The case of GX 301-2
<p>Input files with chosen parameters necessary to reproduce the results from the work: Evolution of eccentric High-mass X-ray binaries. The case of GX 301-2</p>
Test sets and results for paper 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning'
<p>This folder contains the input data (signal-to-noise ratio time series for gravitational wave detections) and results (sky localization areas) obtained from the deep learning based sky localization model 'GW-SkyLocator', and the rapid online gravitational wave sky localization tool 'BAYESTAR' on a set of injections of gravitational wave signals from compact binary mergers.</p> <p>For details of the work, please refer to the paper, 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning' (https://arxiv.org/abs/2207.14522).</p>
Detection and seismic study of gravity and Rossby mode pulsations in four contact binaries
<p>MESA module and inlist files used in the paper “Detection and seismic study of gravity and Rossby mode pulsations in four contact binaries“</p>
Artifact Evaluation for "ARMore: Pushing Love Back Into Binaries"
<p>Artifact Evaluation for "ARMore: Pushing Love Back Into Binaries".</p> <p>Please see the relevant Artifact Appendix pdf from Usenix for instructions on how to run it.</p>
Nucleosynthesis of binary-stripped stars.
<p>The cosmic origin of the elements, the fundamental chemical building blocks of the Universe, is still uncertain. Binary interactions play a key role in the evolution of many massive stars, yet their impact on chemical yields is poorly understood. Using the MESA stellar evolution code we predict the chemical yields ejected in wind mass loss and the supernovae of single and binary-stripped stars. We do this with a large 162 isotope nuclear network at solar-metallicity. We find that binary-stripped stars are more effective producers of the elements than single stars, due to their increased mass loss and an increased chance to eject their envelopes during a supernova. This increased production by binaries varies across the periodic table, with \fluorine[] and \potassium[] being more significantly produced by binary-stripped stars than single stars. We find that the \carbon[12]/\carbon[13] could be used as an indicator of the conservativeness of mass transfer, as \carbon[13] is preferentially ejected during mass transfer while \carbon[12] is preferentially ejected during wind mass loss. We identify a number of gamma-ray emitting radioactive isotopes that may be used to help constrain progenitor and explosion models of core-collapse supernovae with next-generation gamma-ray detectors. For single stars we find \vanadium[44] and \manganese[52] are strong probes of the explosion model, while for binary-stripped stars it is \chromium[48]. Our findings highlight that binary-stripped stars are not equivalent to two single stars and that detailed stellar modelling is needed to predict their final nucleosynthetic yields.</p><p> </p><p>MESA version: 12115</p><p> </p><p>This dataset contains the inlists, output from MESA, scripts, and data tables used in this publication.</p><p> </p><p>The tables folder contains the chemical yields for all models, isotopes, and mass loss processes.</p><p> </p><p>Accepeted for publication in ApJ</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.