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264 results for “stellarator”
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
First release of PLATO consortium stellar limb-darkening coefficients
<p>First official grid of stellar limb-darkening coefficients and intensity profiles computed by the consortium of the PLAnetary Transits and Oscillations of stars (PLATO) Working Package 122400.</p> <p>Linked to the paper "First release of PLATO consortium stellar limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
JWST convolutions for a modular set of synthetic SEDs for young stellar objects (Robitaille, 2017)
<p>This is a companion to the models released alongside the publication:</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>The models are convolved with JWST filters taken from the SVO’s filter profile service. Some models with rotationally flattened envelopes (i.e. geometries with<strong> u</strong>)<strong> </strong>not present in the original model grid have since been completed; their convolved SEDs are included here.</p> <p>Files unzip to {geometry}/convolved/JWST/{SVO_filtername}.fits.</p> <p>This is a subset of the information included in https://doi.org/10.5281/zenodo.8114592.</p>
THE StellaR PAth WP1: Sun-as-a-star plasma Emission Measure Distributions
<p>This folder contains a set of plasma Emission Measure Distributions (EMDs) vs. temperature, derived from observations of the solar corona with the Soft X-ray Telescope (SXT) on board the solar satellite Yohkoh, and the prescription to build EMDs for coronae of solar-type stars with different activity levels, including both quiescent and flaring components. For details read the Description PDF file.</p>
Tables for: The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog
<p>This deposit contains the full machine-readable tables for the accepted version of the manuscript, "<em>The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog</em>", submitted and accepted to the Astrophysical Journal Supplments. </p> <p>The specific files included in this deposit are the full version of Tables 1-3 in this manuscript:</p> <ul> <li>Table 1: Simplified table of PHAT photometry for easy use;</li> <li>Table 2: Simplified table of artificial star test results for easy use;</li> <li>Table 3: Summary of artificial star statistics as a function of brightness and stellar density.</li> </ul> <p>The *.txt files are formatted according to the machine-readable standards adopted by the AAS Journals and CDS/Vizier. Documentation of this format can be found at these links:</p> <ul> <li><a href="https://journals.aas.org/mrt-overview/">AAS Journals MRT overview</a></li> <li><a href="http://vizier.u-strasbg.fr/doc/catstd.htx">CDS/Vizier standards</a></li> </ul> <p>These files can be read in python using the astropy package or with the most recent version of <a href="https://www.star.bris.ac.uk/~mbt/topcat/">TOPCAT</a> (> Version 4.8). An example script for reading these files in astropy is given here:</p> <pre><code class="language-python"> from astropy.table import Table data = Table.read("datafile3.txt", format="ascii.cds") </code></pre> <p>In addition, a headerless, compressed TeX (&-delimited; "full_table.tex.xz") version of Table 1, and a header-only, dataless version of Table 1 ("datafile1_headeronly.txt") are provided to give users additional tools for dealing with this large dataset. </p> <p>The compression routine was applied using xz <https://tukaani.org/xz/> with the encodings</p> <ul> <li> <p><code>xz -z -7 -T 0 full_table.tex</code></p> </li> <li> <p><code>xz -z -9 -T 0 datafile1.txt</code></p> </li> </ul> <p>The Table 1 files on Zenodo were then split into smaller chunks to upload them to Zenodo using the GNU <a href="https://www.gnu.org/software/coreutils/manual/html_node/split-invocation.html"><code>split</code></a> routine. The full files can be recovered by recombining them before decompressing them. The list of related commands and the expected md5 hexidecimal checksums are given here:</p> <ul> <li> <p><code>split --bytes=512M ../full_table.tex.xz full_table.tex.xz.</code></p> </li> <li> <p><code>split --bytes=512M ../datafile1.txt.xz datafile1.txt.xz.</code></p> </li> <li> <p><code>cat full_table.tex.xz.a* > full_table.tex.xz</code></p> </li> <li> <p><code>cat datafile1.txt.xz.a* > datafile1.txt.xz</code></p> </li> <li> <p><code>MD5 (full_table.tex.xz) = 7f9195210bec61c08d04926ada4a021a</code></p> </li> <li> <p><code>MD5 (datafile1.txt.xz) = 63205fe1051e97f4e04380dd23469893</code></p> </li> </ul> <p>Finally, those interested in generating their own cuts from the DOLPHOT quality parameters can access the full photometry tables, available at MAST as a High Level Science Product via <a href="https://doi.org/10.17909/T91S30">doi.org/10.17909/T91S30</a></p>
The APO-K2 Catalog. I. ~7,500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
<p><strong>Abstract: </strong>We present a catalog of fundamental stellar properties for ~7,500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically-derived masses and radii. Additionally, we include kinematic information from <em>Gaia</em>. We investigate the multi-dimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low metallicity stars (~288 at [M/H] ≤ -1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α-enhancement and position in the disk and identify those stars in the sample that are candidate components of the <em>Gaia-Enceladus</em> merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax, color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.</p> <p><strong>Included Files:</strong></p> <ul> <li>The publicly available APO-K2 catalog, the is provided in the publication.</li> <li>The APO-K2 catalog without truncation to any numbers. </li> <li>The selection function relative density tables for the mass-radius parameter space. </li> <li>The selection function relative density tables for the metallicity-mass parameter space. </li> <li>The selection function relative density tables for the magnitude-color parameter space. </li> <li>The selection function relative density tables for the ν<sub>max</sub>-mag parameter space. </li> </ul>
The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements
<p>Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press).</p> <p>Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files</p> <p>When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations:</p> <p> SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W</p> <p>Please reach out if you have any questions or concerns.</p>
The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors
<p><strong>The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors</strong></p> <p>This repository contains the Stellar Parameters of Tracks with Starspots (SPOTS) grid of theoretical stellar evolutionary tracks and isochrones, presented in Somers, Pinsonneault, and Cao (2020, in prep). Our models were calculated with the Yale Rotating Evolution Code (e.g. van Saders & Pinsonneault, 2013, ApJ 776, 67), including updated which incorporate a treatment of surface starspots (Somers & Pinsonneault, 2015, ApJ 807, 174S). Modelling details can be found in these references. The purpose of this evolutionary suite is to provide the community with state-of-the-art predictions for the influence of starspots and magnetic activity on the structure of stars.</p> <p>The grid includes both isochrones and tracks. They can be downloaded individually from this repository, or in bulk by downloading the .zip files.</p> <p><strong>Isochrones (.isoc):</strong></p> <p>Each isochrone file contains a series of isochrones (stellar properties for a range of masses at fixed age) for ages between 1 Myrs and 4 Gyrs. Each file contains these isochrones for a different surface starspot covering fraction, given by the name of the file -- f000.isoc = 0% covering fraction, f017.isoc = 17% covering fraction, etc. Each isochrone contains several columns with different information, including,</p> <ol> <li>Fundamental properties: mass, age, luminosity, radius, logg, Teff, convective overturn timescale (TauCZ), lithium abundance relative to initial (Li/Li0).</li> <li>Starspot properties: Covering fraction (Fspot), ratio of spot temperature to ambient temperature (Xspot), the temperatures of hot and cool regions (T_hot, T_cool).</li> <li>Two-temperature colors, including Johnson BV, Cousins RI, 2MASS JHK, WISE W1, and Gaia G, BP, RP.</li> </ol> <p>Colors that fell outside of the calibrated range are listed as -99.0.</p> <p><strong>Tracks (.track):</strong></p> <p>We also include individual tracks for every combination of Fspot and Mass considered in the paper. Each .track file lists the mass and starspot covering fraction in the filename -- i.e. m055_f034.track is the model of mass 0.55Msun and with a 34% surface covering fraction. In addition to all the properties included in the isochrones, the track files also include:</p> <ol> <li>The total moment of interia of the model (total_I) and the moment of interia of the surface convection zone (CZ_I)</li> <li>The central and surface hydrogen abundances (X_cen, X_surf) and the surface metallicity (Z/X_surf)</li> <li>The deuterium abundance relative to initial (H2/H2_0)</li> </ol>
Stellar mass function for galaxies in A133
<p>This table provides the stellar mass functions (dN/dlg(M*)) of A133,<br> as shown in Fig 10 of Starikova et al. 2020, normalized to 1 square<br> degree on the sky.</p> <p> </p>
Simulated Solar Spectra for Testing Stellar Activity Mitigation Strategies
<p>Recent and upcoming stabilized spectrographs are pushing the frontier for Doppler spectroscopy to detect and characterize low-mass planets. Specifications for these instruments are so impressive that intrinsic stellar variability is expected to limit their Doppler precision for most target stars (Fischer et al. 2016). In order to realize their full potential, astronomers must develop new strategies for distinguishing true Doppler shifts from intrinsic stellar variability. Stellar variability due to starspots, faculae, and other rotationally-linked variability is particularly concerning, as the stellar rotation period is often included in the range of potential planet orbital periods. In order to robustly detect and accurately characterize low-mass planets via Doppler planet surveys, the exoplanet community must develop statistical models capable of jointly modeling planetary perturbations and intrinsic stellar variability. Towards this effort, we present simulations of extremely high-resolution solar-like spectra created with SOAP 2.0(Dumusque et al. 2014) that includes multiple evolving starspots to aid in developing and testing future statistical methods.</p>
The Complex Geometry and Dynamical Role of Stellar Wind Bubbles in Turbulent Molecular Clouds
<p>Research Data Management Package for paper in Monthly Notices of the Royal Astronomical Society with same title and author list</p>
Dartmouth Stellar Evolution Grids
<p>Dartmouth stellar model grids, used by the 'isochrones' python package (github.com/timothydmorton/isochrones). Update from previous grids; these now include WISE bands, ages < 1Gyr, and have coarser Fe/H sampling.</p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1 [Hyperion files]
<p>These are the input and output files for the radiative transfer code (Hyperion) for the model sets presented in</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>Each model set is provided as a single tar file. Each tar file expands to <strong>grids-1.1/<set name></strong>, so if you expand all tar files in the same folder, you will end up with a single <strong>grids-1.1</strong> folder with 18 sub-folders, one for each model set.</p> <p>For a given model set, the files are as follows:</p> <ul> <li>grids-1.1/<set name>/input - input Hyperion files</li> <li>grids-1.1/<set name>/log - log files from Hyperion</li> <li>grids-1.1/<set name>/output - output Hyperion files</li> <li>grids-1.1/<set name>/par - parameters for each model</li> <li>grids-1.1/<set name>/ranges.conf - ranges of parameters varied in the model set</li> <li>grids-1.1/<set name>/parameters.hdf5 - table of parameters for all models</li> <li>grids-1.1/<set name>/d03_5.5_3.0_A_sub.hdf5 - dust file used for the models</li> </ul> <p>Given the large number of models for some of the model sets, the models are not all stored directly inside the par, input, output or log directories - instead these directories contain folders formed from the first two characters (forced to lowercase) of the names of the models they contain. For example, a3 contains all models whose name starts with a3 or A3. This was done to avoid having too many files in a single folder which can cause issues on certain file systems.</p> <p>For the Hyperion input and output files, in some cases an _sed file is present. In these cases, the output SEDs (and polarization spectra) should be read from the _sed file, not the original output file. This is the case for all models that are in a set for which the ambient medium was present, as described in §4.2.3 of Robitaille (2017). Furthermore, in some cases the SED file is called _sed_noscat to indicate that scattering was not included, as described in §5.1 of Robitaille (2017).</p> <p>To avoid taking up too much disk space, the Hyperion HDF5 input/output files use external links to refer to each other and to the dust file. To make sure the links work, you should do all operations with the input/output files from the directory containing <strong>grids-1.1</strong>. For example, to open a Hyperion output file, you would need to do (in Python):</p> <p> In [1]: from hyperion.model import ModelOutput</p> <p> In [2]: mo = ModelOutput('grids-1.1/s---s-i/output/a3/A3kQmQtj.rtout')</p> <p>A notebook with examples of reading in the output files can be found here:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook_raw/reading_raw_files.ipynb</p> <p>More information on using Hyperion, including reading input/output files, can also be found at http://docs.hyperion-rt.org</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p>
Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267
<p>supplementary data products, including all sky-subtracted spectra from individual galaxies, as well as random draws from posterior PDFs for model parameters (see included README file)</p>
Contact tracing of binary stars: Pathways to stellar mergers (online data)
<p><strong># Data for Henneco et al. (2024)</strong></p> <p>This repository contains the input files required to reproduce the MESAbinary models from Henneco et al. (2024). It also contains the full machine-readable version of Table G.1. For an overview of the quantities in each column, we refer to the notes underneath Table G.1 in the paper.</p> <p>MESA r12778<br>MESA SDK 20.3.2</p> <p><strong>## MESA_inlists</strong></p> <p>- <strong>inlist1</strong>: inlist for the initially more massive primary star</p> <p>- <strong>inlist2</strong>: inlist for the initially less massive secondary star</p> <p>-<strong> inlist_project</strong>: inlist for the binary system</p> <p> </p> <p><strong>## run_extras</strong></p> <p>- <strong>run_star_extras.f</strong>: subroutines and functions for the individual stars</p> <p>- <strong>run_binary_extras.f</strong>: subroutines and functions for the binary system</p> <p> </p> <p><strong>## MESA_ZAMS_models</strong></p> <p>Precomputed ZAMS models read in through <strong>inlist1</strong> and <strong>inlist2</strong>.</p> <p> </p> <p><strong>## table_G1_full.txt</strong></p> <p>Full machine-readable version of Table G.1.<br> </p> <p><strong>## MESA_models_output</strong></p> <p>Detailed output of the MESAbinary calculations. <em>Will be added in due time.</em></p>
A Study of Primordial Very Massive Star Evolution II: Stellar Rotation and Gamma-Ray Burst Progenitors
<p>Wind ejecta tables of rotating very massive stars from the paper:</p> <p><a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad1185">A Study of Primordial Very Massive Star Evolution II: Stellar Rotation and Gamma-Ray Burst Progenitors</a></p>
Stellar properties of observed stars stripped in binaries in the Magellanic Clouds - Observations and Results
<p>This Zenodo repository is one of three Zenodo repositories related to the article "Stellar properties of observed stars stripped in binaries in the Magellanic Clouds" by Y. Götberg, M.R. Drout, A.P. Ji, J.H. Groh, B.A. Ludwig, P.A. Crowther, N. Smith, A. de Koter, and S.E. de Mink. In the article, we analyze the optical spectra of ten stars and measure their stellar properties using spectral fitting. This repository contains observational data and the resulting measurements for the stellar properties of the stars analyzed in the article, along with best fit spectral models and spectral models used to estimate the wind mass loss rate of the stars. Below, we describe the content in more detail:</p> <ul> <li><strong>0_ReadMe.txt</strong>: A text file where we describe some more details regarding the content.</li> <li><strong>S2_stacked_stellar_spectra.tar.gz (4.6 MB):</strong> The spectra we use for obtaining stellar properties for the observed stars.</li> <li><strong>Table2.txt</strong>: the apparent AB magnitudes with associated errors for the stars we analyze in the paper.</li> <li><strong>Table3.txt</strong>: the stellar properties for the stars we analyze in the paper. These parameters are obtained by spectral fitting.</li> <li><strong>S5_spectra_best_fit_models.tar.gz (14 MB):</strong> The spectral energy distributions and normalized spectra for the best-fit models, recomputed such that the radius and bolometric luminosity also matches. This tarball contains best-fit spectra for all of the stars in text-files labeled with the format SED_StarX_xxx.txt (~70 MB when inflated). The stellar parameters are presented in Section 5.</li> <li><strong>S5_full_best_fit_models.tar.gz (1.9 GB):</strong> The full CMFGEN version of the best-fit models for each individual star. This tarball contains a tarball for each star, which when inflated becomes ~250-550 MB each. </li> <li><strong>S7_spectra_mdot_models (31 MB): </strong>The spectral energy distributions and normalized spectra for the models used in the mass-loss analysis that we present in Section 7.</li> <li><strong>S7_full_mdot_models (3.5 GB):</strong> The full CMFGEN version of the spectral models used for the mass-loss rate variation presented in section 7 (excluding the best-fit models, which are provided separately).</li> </ul>
THE NATURE OF X-RAYS FROM YOUNG STELLAR OBJECTS IN THE ORION NEBULA CLUSTER - A Chandra HETGS Legacy Project
<p><span>This first release provides the community with a first cut of confusion cleaned X-ray spectra of the Orion Nebula Cluster observed with the HETG onboard the Chandra X-ray Observatory. The data were taken starting in 1999 until 2021. <br><br>The confusion cleaning is based on several aspects of sources for confusion, which includes cluster point sources intersecting with grating dispersions, grating arms intersecting each other in CCD space, as well as grating dispersion overlaps prohibiting proper order sorting. The latter is a major effect and resulted in sometimes severe data losses. In the first release, our automated procedure took care of the vast majority of point sources and grating arm intersections. With respect to the dispersion arm overlaps, in this release we took a statistical approach optimizing the agreement of all four grating dispersion arms in the merged data to agree within a 1 sigma statistical uncertainty over 90% of the bandpass between 2 and 15 Angstrom. For that we used the zero order flux fractions of the interfering sources as the driving parameter. <br><br>There are still many caveats and rooms for improvement, which we will address in upcoming releases, which include the treatment of the increasing background at high dispersion, improve extraction efficiency, exclude observations with non-detections before confusion cleaning, include possible new detections, investigate the 5 A excess we observe in the HEG, though at low statistics, spotcheck individual observations for any residual issues. <br><br>Release 1 provides the community with an excellent starting point for addressing our identified science projects. Out of the 46 sources that were extracted, 37 resulted in valid spectral data. 7 sources have less than 1000 counts in 1st order, some of those may not yet be very useful. <br></span></p> <p> </p> <p>Each directory contains the merged cleaned spectrum and responses for<br>one source. The file "pha2" is a Type II PHA file (multiple spectra)<br>containing the four first order spectra, HEG -1, HEG +1, MEG -1, and<br>MEG +1. Headers have been edited indicate the object (OBJECT), and<br>start and stop times for the set of observations. Since the exposure<br>depends on order, due to the cleaning process, EXPOSURE is a column in<br>the data table. Some other keywords now say "MERGED" since they can<br>vary with observation.</p> <p>There is one effective area file per order (".arf" files). These have<br>also been merged by zeroing out the same regions as excluded in the<br>count spectra, and summed weighting by exposure. They also have<br>similar header edits as for the spectra.</p> <p>While the exposures in headers may say 2 Ms, the actual exposure at<br>any wavelength may be much less. This is not explicitly known, but is<br>implicit in the ignored wavelength regions in the merged counts and<br>responses.</p> <p>There is one grating response matrix (".rmf" files) per order. Since<br>all spectral extractions of all sources used the same cross-dispersion<br>region, there is no change in these files between sources. One set<br>suffices for all extractions. These are in the directory "RMFs", and<br>also for convenience have symbolic links in each source directory.</p> <p>HETG background files have also been provided, one PHA file per first<br>order, in directory HETG_Background. These have been derived from<br>long observations of blank fields. Details are provided in the<br>accompanying memo, hetg_background.pdf.</p> <p>Headers have not been designed for auto-loading of responses (that is<br>CORRFILE, RESPFILE, and BACKFILE are set to 'none').</p>
Supplementary data to "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE"
<p>Supplementary material to DESI's publication "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE" to comply with the data management plan. Data associated with revised version of paper submitted to The Astrophysical Journal. Directory contains data points for all figures with file names indicating the figure to which the data corresponds as well as a FITS file for Table 4. Data for 13 figures and one table (46 FITS files) included in .zip directory</p>
Kiauhoku Stellar Evolutionary Model Grids
<p>Stellar evolutionary model grids for use with Python Kiauhoku package (presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. 2020</a>). This dataset contains models from MIST, YREC, GARSTEC, and Dartmouth projects.</p> <p><strong>Model Grids</strong></p> <p><em>fastlaunch</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...776...67V/abstract">van Saders and Pinsonneault (2013)</a> under "fast launch" condition of <em>P</em><sub>init </sub>~ 8 days.</p> <p><em>slowlaunch</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...776...67V/abstract">van Saders and Pinsonneault (2013)</a> under "slow launch" condition of <em>P</em><sub>init </sub>~ 13 days.</p> <p><em>rocrit</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the stalled magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2016Natur.529..181V/abstract">van Saders et al. (2016)</a> under "fast launch" condition of <em>P</em><sub>init </sub>~ 8 days.</p> <p><em>mist</em></p> <p>Evolutionary tracks from the MESA Isochrones and Stellar Tracks (MIST, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C/abstract">Choi et al. 2016</a>). Computed using Modules for Experiments in Stellar Astrophysics (MESA, <a href="https://ui.adsabs.harvard.edu/abs/2010ascl.soft10083P/abstract">Paxton et al. 2010</a>).</p> <p><em>yrec</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv201207957T/abstract">Tayar et al. (2022)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>).</p> <p><em>dartmouth</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2008ApJS..178...89D/abstract">Dotter et al. (2008)</a>. Models from the Dartmouth Stellar Evolution Program (DSEP).</p> <p><em>garstec</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2013MNRAS.429.3645S/abstract">Serenelli et al. (2013)</a>. Computed using the Garching Stellar Evolution Code (GARSTEC, <a href="https://ui.adsabs.harvard.edu/abs/2008Ap%26SS.316...99W/abstract">Weiss & Schlattl 2008</a>).</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.