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264 results for “stellarator”
WP2: Photochemical model of planetary atmospheres driven by high-energy stellar irradiation
<p>Modeling results of irradiated planetary atmospheres (Locci et al. 2021, <em>Extreme Ultraviolet and X-ray Driven Photochemistry of Gaseous Exoplanets</em>, PSJ, submitted). See <em>Introduction</em> and <em>Readme_Reference_model</em> for more details.</p>
Dataset from: Rapid stellar and binary population synthesis with COMPAS
<p>This data set is used to produce Figure 16 of the COMPAS Paper (<a href="https://arxiv.org/abs/2109.10352">Team COMPAS et al. 2021</a>).</p> <p>The file contains the output of 10,000,000 binaries evolved using COMPAS v02.21.00, using adaptive importance sampling (STROOPWAFEL) , sampling from a metallicity uniform in <span class="math-tex">\(\log(Z) \in [10^{-4},0.03]\)</span>. More details can be found in the `Run_Details` group.</p> <p>*****************************<br> Contents<br> *****************************</p> <ol> <li>COMPAS simulation output:<br> The hdf5 file Includes necessary information about the simulated binaries in the following groups:<br> <br> <strong>BSE_Double_Compact_Objects<br> BSE_Common_Envelopes<br> BSE_RLOF<br> BSE_Supernovae<br> BSE_System_Parameters<br> Run_</strong><strong>Details</strong><br> <br> We point the reader to the documentation at <a href="https://compas.science/docs">https://compas.science/docs</a>, for information and instructions regarding the contained data.<br> </li> <li><strong>Rate Data</strong><br> The hdf5 file contains 1 groups with BBH merger rate data for every BBH with an inspiral time that is smaller than the age of the Universe. This group contains a dataset "DCOmask" which can be used to point from the "DoubleCompactObjects" to the data contained in the respective rates datasets.<br> <br> We include the rates for one realization of the metallicity-dependent SFRD as discussed in the manuscript.<br> The metallicity-dependent SFRD used is the phenomenological metallicity-dependent SFRD as described in Neijssel et al. 2019, this contained in:<br> <strong>'Rates_mu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0'</strong></li> </ol> <p>*****************************</p> <p>The rates were calculated by running ```FastCosmicIntegration.py``` from COMPAS's post-processing tools.<br> For a more detailed explanation of the workings of COMPAS's post-processing tools, please the docs <a href="https://compas.science/docs">https://compas.science/docs</a></p>
All mixed up: investigating the impact of internal mixing on stellar evolution
<p>The main-sequence evolution of intermediate- and high-mass stars is driven by the mass of their convective core and the amount of hydrogen that can take part in the nuclear burning. Both of these quantities are controlled by physical mixing processes at work in the deep stellar interior. Due to computational limitations, the implementations of these 3D time-dependent processes into 1D stellar evolution codes are based on unconstrained time-independent free parameters which represent individual mixing efficiencies. Traditional observational methods relying on global stellar characteristics, such as the surface gravity and effective temperature, have limited capacity in constraining the efficiencies of these mixing efficiencies and their impact on stellar evolution. Here, we combine the highly precise interior modelling enabled by asteroseismology with the sub-percent level precision on fundamental stellar properties such as masses and radii dictated by eclipse modelling of binary systems to rigorously calibrate stellar evolution models. Relying on model-independent masses and radii allows us to investigate how the internal mixing process alter the convective core masses of intermediate and high-mass stars. We demonstrate that populations of pulsating stars, multiple systems, and multiple systems with pulsating components all indicate that stellar evolution models without enhanced mixing in the core-boundary layers predict under-massive convective cores. We compare our binary asteroseismic results with a growing sample of magnetic stars in eclipsing binaries to investigate the impact of a measurable magnetic field on internal mixing in stellar evolution models.</p>
The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit
<p>Input files and simulation results for stellar evolution tracks computed for the paper "The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit", as well as synthetic populations generated and catalogues of observed cool supergiants in the Magellanic Clouds used in the analysis. Version 10398 of MESA was used for the simulations. More details in the README.txt file and in the paper.</p>
LAMOST–Gaia–Kepler Stellar Kinematic catalog
<p>In the second part of the Planets Across the Space and Time (PAST) series, by combining the data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR4 and Gaia DR2 and then applying the revised kinematic methods from PAST I, we present a catalog of kinematic properties (i.e., Galactic positions, velocities, and the relative membership probabilities among the thin disk, thick disk, Hercules stream, and the halo) as well as other basic stellar parameters for 35,835 Kepler stars.</p> <p>The new version combing LAMOST DR8 and Gaia eDR3 will be released soon.</p>
Stellar Population Spectra from Bruzual & Charlot (2003)
<p>Models from <a href="http://adsabs.harvard.edu/abs/2003MNRAS.344.1000B">Bruzual & Charlot (2003; MNRAS; 344; 1000)</a>. Data was downloaded from <a href="http://www2.iap.fr/users/charlot/bc2003/">http://www2.iap.fr/users/charlot/bc2003/</a></p> <ul> <li> <p>SSP_Spectra_BC2003_highResolution_imfChabrier.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a></p> </li> <li> <p>High resolution spectra</p> </li> </ul> </li> <li> <p>SSP_Spectra_BC2003_highResolution_imfSalpeter.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a></p> </li> <li> <p>High resolution spectra</p> </li> </ul> </li> <li> <p>SSP_Spectra_BC2003_lowResolution_imfChabrier.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a></p> </li> <li> <p>Low resolution spectra</p> </li> </ul> </li> <li> <p>SSP_Spectra_BC2003_lowResolution_imfSalpeter.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a></p> </li> <li> <p>Low resolution spectra</p> </li> </ul> </li> </ul>
Stellar Population Spectra from BPASS v2.0
<p>The BPASS models of <a href="http://adsabs.harvard.edu/abs/2012MNRAS.419..479E">Eldridge & Stanway (2012, MNRAS, 419, 479)</a>; <a href="http://adsabs.harvard.edu/abs/2011MNRAS.414.3501E">Eldridge, Langer & Tout (2011, MNRAS, 414, 3501)</a>; <a href="http://adsabs.harvard.edu/abs/2009MNRAS.400.1019E">Eldridge & Stanway (2009, MNRAS, 400, 1019)</a>; <a href="http://adsabs.harvard.edu/abs/2008MNRAS.384.1109E">Eldridge, Izzard & Tout (2008, MNRAS, 384, 1109)</a>. Data was downloaded from <a href="http://flexiblelearning.auckland.ac.nz/bpass/2/files/">http://flexiblelearning.auckland.ac.nz/bpass/2/files/</a>.</p> <ul> <li> <p>SSP_Spectra_BPASS_binary.hdf5</p> <ul> <li> <p>Canonical BPASS IMF</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.0 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASS_single.hdf5</p> <ul> <li> <p>Canonical BPASS IMF</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Use v2.0 of BPASS</p> </li> </ul> </li> </ul>
Stellar Population Spectra from BPASS v2.2.1
<p>The BPASS models of <a href="https://ui.adsabs.harvard.edu/abs/2017PASA...34...58E">Eldridge, Stanway et al. (2017)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479...75S">Stanway & Eldridge et al. (2018)</a>. Data was downloaded from <a href="https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl?usp=sharing">https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl?usp=sharing</a>.</p> <ul> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf100_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.00 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf100_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.00 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf135_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf135_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf135all_100.hdf5</p> <ul> <li> <p>Power law IMF (α=-2.35 from 0.1M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf170_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.70 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf170_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.70 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf_chab100.hdf5</p> <ul> <li> <p>Chabrier IMF (α=-2.00; maximum mass 100M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_bin-imf_chab300.hdf5</p> <ul> <li> <p>Chabrier IMF (α=-2.00; maximum mass 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf100_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.00 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf100_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.00 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf135_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf135_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf135all_100.hdf5</p> <ul> <li> <p>Power law IMF (α=-2.35 from 0.1M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf170_100.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.70 from 0.5M<sub>☉</sub> to 100M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf170_300.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.70 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf_chab100.hdf5</p> <ul> <li> <p>Chabrier IMF (α=-2.00; maximum mass 100M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.2.1_sin-imf_chab300.hdf5</p> <ul> <li> <p>Chabrier IMF (α=-2.00; maximum mass 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>Uses v2.2.1 of BPASS</p> </li> </ul> </li> </ul>
Stellar Population Spectra from Conroy et al. (2010)
<p>Models from <a href="http://adsabs.harvard.edu/abs/2010ascl.soft10043C">Conroy & Gunn (2010; ascl:1010.043)</a>.</p> <ul> <li> <p>SSP_Spectra_Conroy-et-al_v2.5_imfChabrier.hdf5</p> <ul> <li> <p>Computed with v2.5 of FSPS</p> </li> <li> <p><a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a></p> </li> </ul> </li> <li> <p>SSP_Spectra_Conroy-et-al_v2.5_imfKroupa.hdf5</p> <ul> <li> <p>Computed with v2.5 of FSPS</p> </li> <li> <p><a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a></p> </li> </ul> </li> <li> <p>SSP_Spectra_Conroy-et-al_v2.5_imfSalpeter.hdf5</p> <ul> <li> <p>Computed with v2.5 of FSPS</p> </li> <li> <p><a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a></p> </li> </ul> </li> </ul>
Stellar Population Spectra from Maraston et al. (2005)
<p>Models from <a href="http://adsabs.harvard.edu/abs/2005MNRAS.362..799M">Maraston (2005, MNRAS, 362, 799)</a>; <a href="http://adsabs.harvard.edu/abs/1998MNRAS.300..872M">Maraston (1998, MNRAS, 300, 872)</a>. Data downloaded from <a href="http://web.archive.org/web/20220121155147/http://www.icg.port.ac.uk/~maraston/Claudia's_Stellar_Population_Model.html">http://web.archive.org/web/20220121155147/http://www.icg.port.ac.uk/~maraston/Claudia's_Stellar_Population_Model.html</a>.</p> <ul> <li> <p>SSP_Spectra_Maraston_hbMorphologyRed_imfKroupa.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a></p> </li> <li> <p>“Red” horizontal branch morphology</p> </li> </ul> </li> <li> <p>SSP_Spectra_Maraston_hbMorphologyRed_imfSalpeter.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a></p> </li> <li> <p>“Red” horizontal branch morphology</p> </li> </ul> </li> <li> <p>SSP_Spectra_Maraston_hbMorphologyBlue_imfKroupa.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a></p> </li> <li> <p>“Blue” horizontal branch morphology</p> </li> </ul> </li> <li> <p>SSP_Spectra_Maraston_hbMorphologyBlue_imfSalpeter.hdf5</p> <ul> <li> <p><a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a></p> </li> <li> <p>“Blue” horizontal branch morphology</p> </li> </ul> </li> </ul>
Stellar Population Spectra from Venkatesan et al. (2003)
<p>Simple stellar population spectra from <a href="http://adsabs.harvard.edu/abs/2003ApJ...584..621V">Venkatesan et al. (2003; ApJ; 584; 621)</a> in the Galacticus format. Two models are provided. Both assume a <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a>, one from 10-140M<sub>☉</sub> and the other from 1-100M<sub>☉</sub>.</p> <p> </p>
An extended stellar halo discovered in Fornax dwarf spheroidal using Gaia EDR3
<p>We provide the catalog of Fornax member candidate, including the three final samples, as well as the data of surface density profiles, see the example python code of loading the data, as well as the explanation of each quantity.</p>
Stellar Population Spectra from BPASS v2.3
<p>The BPASS models of <a href="https://ui.adsabs.harvard.edu/abs/2017PASA...34...58E">Eldridge, Stanway et al. (2017)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479...75S">Stanway & Eldridge et al. (2018)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.512.5329B">Byrne et al. (2022, MNRAS, 512, 5329)</a>. Data was downloaded from <a href="https://livewarwickac.sharepoint.com/sites/Physics-BinaryPopulationandSpectralSynthesisBPASS/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2FPhysics%2DBinaryPopulationandSpectralSynthesisBPASS%2FShared%20Documents%2FPaper%20Data%20Packages%2FByrne23&p=true&ga=1">here</a>.</p> <ul> <li> <p>SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha-02.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=-0.2</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+00.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.0</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+02.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.2</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+04.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.4</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+06.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Does not include binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.6</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha-02.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=-0.2</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+00.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.0</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+02.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.2</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+04.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.4</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> <li> <p>SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+06.hdf5</p> <ul> <li> <p>Broken power law IMF (α=-1.30 from 0.1M<sub>☉</sub>-0.5M<sub>☉</sub>, α=-2.35 from 0.5M<sub>☉</sub> to 300M<sub>☉</sub>)</p> </li> <li> <p>Includes binary stars</p> </li> <li> <p>α-alement enhancement of Δlog₁₀(α/Fe)=+0.6</p> </li> <li> <p>Uses v2.3 of BPASS</p> </li> </ul> </li> </ul>
Stellar luminosity tracks from Baraffe 1998
<p>Stellar luminosity tracks from Baraffe 1998 as compiled by Tim Lichtenberg. Zenodo dataset created for 'showyourwork' integration and automated online paper build</p>
Dartmouth stellar evolution models
<p>Dartmouth stellar evolution models 2012 version.</p> <p>HST_ACSWF.tgz<br> HST_WFPC2.tgz<br> PanSTARRS.tgz<br> SDSSugriz.tgz<br> UBVRIJHKsKp.tgz</p>
Associated data for "The Roasting Marshmallows Program with IGRINS on Gemini South II -- WASP-121 b has super-stellar C/O and refractory-to-volatile ratios" Published in The Astronomical Journal
<table> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>w121_1DRC_H2O_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only H2O opacity.</td> </tr> <tr> <td>w121_1DRC_OH_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only OH opacity.</td> </tr> <tr> <td>w121_1DRC_CO_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only CO opacity.</td> </tr> <tr> <td>w121_1DRC_EVERYTHING.txt</td> <td>Self consistent, solar composition model spectrum with all sources of opacity.</td> </tr> <tr> <td>pre_.pic</td> <td>Pre-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_variance.pic</td> <td>Associated per-pixel variance for the pre-eclipse data.</td> </tr> <tr> <td>post_cube.pic</td> <td>Post-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_time_BJD.pic</td> <td>Average frame time in BJD for the pre-eclipse sequence.</td> </tr> <tr> <td>pre_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the pre-eclipse sequence.</td> </tr> <tr> <td>post_ph.pic</td> <td>Orbital phase per frame for the post-eclipse sequence.</td> </tr> <tr> <td>post_time_BJD.pic</td> <td>Average frame time in BJD for the post-eclipse sequence.</td> </tr> <tr> <td>post_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the post-eclipse sequence</td> </tr> </tbody> </table>
Output Dataset from "Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks"
<p>Output dataset of stellar parameters and associated uncertainties derived by the machine learning approach, as decribed in <a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener"><em>"Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks", Fallows & Sanders</em> (</a><em><a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">2024)</a>. </em></p> <p>Included parameters: [Fe/H] (fe_h), effective temperature (teff), surface gravity (logg), [C/Fe] (c_fe), [N/Fe] (n_fe), and [a/M] (a_m). Gaia DR3 source ids are included for matching purposes, alongside Gaia bp_rp_excess_factor and ruwe metrics for quality filtering.</p> <p><em>We include predictions for only Gaia objects with radial velocity measurements ('output_0.0_360.0_v2.txt'), and for all* Gaia objects with XP spectra ('gaiaFull_output_0.0_360.0.zip'). Note our full catalogue has a total size of 30.9GB once uncompressed.</em></p> <p> </p> <p><em>* Not truly all objects with XP spectra; stars with spurious or unreliable measurements in our requred inputs (Gaia, 2MASS, WISE) have been removed.</em></p>
Data set, Model, and Catalog for: Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data
<p>Intrinsic colors of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the stellar intrinsic colors with the atmospheric parameters, Teff , log g, and [M/H], which is an improvement of the widely used blue-edge method. The dust reddening toward each line-of-sight can then be calculated by the observed colors minus the derived intrinsic colors.</p> <p>Here we provide the related data sets:</p> <ul> <li>xgb_model.pkl: the trained XGBoost model.</li> <li>use_xgb.py: a simple script showing how to use the XGBoost model to predict intrinsic colors.</li> <li>data_set.fits: this fits file contains the training and test sets, separated into four data arrays: <ul> <li>X_train: X-data (teff,logg,mh) of the training set.</li> <li>X_test: X-data (teff,logg,mh) of the test set.</li> <li>y_train: y-data (BP-RP, BP-Ks, J-Ks) of the training set.</li> <li>y_test: y-data (BP-RP, BP-Ks, J-Ks) of the test set.</li> </ul> </li> <li>IC_catalog.csv: a catalog containing a representative set of intrinsic colors at three bands ('BPRP0', 'BPK0', and 'JK0' in columns) as a function of typical Teff, logg, and [M/H] ('teff', 'logg', and 'mh' in columns).</li> </ul> <p>With the above data and model, users can apply the trained XGBoost to new sources to predict their intrinsic colors and calculate their dust reddenings. One can further control the quality of the prediction by selecting training-like sources with the training set and estimating the <a>generalization error by the test set.</a></p>
Data supporting "Universality of Free Fall Shown by Orbital Motion of a Pulsar in a Stellar Triple System"
<p>This is a collection of data supporting the research paper "Orbital Motion of a Pulsar in a Stellar Triple System Shows Universality of Free Fall".</p> <p>The main data content (the ".tim" file) is a set of pulse arrival times, the primary input to the fitting process, in tempo2 format. These times are annotated with post-fit residuals and derivatives with respect to fit parameters using the tempo2 format's per-TOA flags.</p> <p>Additional files include the data that went into several figures, plus the above converted to CSV format - but note that the CSV format does not record the pulse arrival times with sufficient precision to permit fitting. Derivative and uncertainty information may permit reanalysis of the systematics.</p>
Observing substructure in circumstellar discs around massive young stellar objects
<p>Synthetic dust continuum and molecular line Atacama Large Millimetre Array (ALMA) observations of massive, self-gravitating disc models surrounding massive young stellar objects are presented here. Semi-analytic models of self-gravitating discs with spiral density waves and clumps/fragments are combined with radiative transfer models, and synthetic observations are produced using CASA software. Models presented here have different disc masses, distances, inclinations, thermal structures, dust distributions, number and orientation of spirals and fragments.<br> <br> Data is in the FITS format, with filenames starting either with 'line' (synthetic molecular line datacube) or 'cont' (synthetic continuum images), and each filename contains the model ID. Tables of model IDs and model parameters are given in files models_table_spiral.dat and models_table_spiral_fragments.dat for models without and with fragments, respectively. Starting from a fiducial disc model, model parameters were varied one by one, with the exception of disc inclination which is separately set in each model.<br> <br> For details about the model parameters, detailed presentation of methods, proposed substructure-enhancing filtering methods, discussion and predictions for the upcoming ALMA observations, see Jankovic et al. 2018 (accepted for publication in MNRAS, arxiv.org/abs/1810.11398).</p> <p> </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.