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264 results for “stellarator”
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
BASE-9 binarity and stellar masses from Gaia DR3, 2MASS, and Pan-STARRS data for six open clusters: NGC 2168, NGC 7789, NGC 6819, NGC 2682, NGC 188, NGC 6791
<h2>Data sets as described in "Goodbye to Chi-by-Eye: A Bayesian Analysis of Photometric Binaries in Six Open Clusters", Childs et al. 2023 <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract">https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract</a></h2>
A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data
Open the record for dataset details and reuse information.
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
A single-field-period quasi-isodynamic stellarator
<p>Stellarator Equilibrium - Quasi-isodynamic, 1 field period</p> <p>Designed using the near-axis expansion (codes provided)</p> <p>Details of the design in https://arxiv.org/abs/2205.05797</p>
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
Flavor-violating Higgs decays and stellar cooling anomalies in axion models
<p>We study a class of DFSZ-like models for the QCD axion that can address observed anomalies in stellar cooling. Stringent constraints from SN1987A and neutron stars are avoided by suppressed couplings to nucleons, while axion couplings to electrons and photons are sizable. All axion couplings depend on few parameters that also control the extended Higgs sector, in particular lepton flavor-violating couplings of the Standard Model-like Higgs boson h. This allows us to correlate axion and Higgs phenomenology, and we find that BR(h → τe) can be as large as the current experimental bound of 0.22%, while BR(h → μμ) can be larger than in the Standard Model by up to 70%. Large parts of the parameter space will be tested by the next generation of axion helioscopes such as</p>
MESA model files and data for: 'Stellar Neutrino Emission Across The Mass-Metallicity Plane'
<p>Example MESA model files and stellar evolution tracks for download from "Stellar Neutrino Emission Across The Mass-Metallicity Plane".</p>
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
A join of the Huber et al. (2014) catalog of stellar parameters and the Kepler Input Catalog
<p>This a join of the <a href="http://arxiv.org/abs/1312.0662">Huber et al. (2014)</a> and the <a href="http://arxiv.org/abs/1102.0342">Kepler Input Catalog</a></p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
Confusion matrices for theoretical young stellar object models
<p>This is a data table supplementing the following publication: </p> <p><em><strong>A framework for modeling the evolution of young stellar objects </strong></em>(Richardson et al. 2025, accepted to ApJ).</p> <p>It contains a set of confusion matrices comparing the evolutionary stages and classes of radiative transfer YSO models selected by proximity to protostellar evolutionary tracks. Models are included in a matrix based on their correspondence to particular modeled accretion histories, zero-age stellar masses, ages, mass accretion efficiencies, and levels of detectability (defined using flux in the ALMA Band 6 wavelength range). Details on the construction and use of the table are contained in the accompanying README file, and more information about the matrices is contained in Section 4.2 of the companion paper.</p> <p>The YSO models populating these matrices are from Richardson et al. (2024); information on them is contained in the <a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">companion work</a> and <a href="https://zenodo.org/records/10522816" target="_blank" rel="noopener">data release</a>.</p>
Data and scripts for the publication "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations"
<p>Coils and VMEC configurations for the three stellarator configurations presented in "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations", including the optimization scripts used to find the coils and plot scripts used to produce the figures in the text.</p>
An updated modular set of synthetic spectral energy distributions for young stellar objects
<p>These are the models released with the following publication:</p> <p><strong><em>An updated modular set of synthetic spectral energy distributions for young stellar objects</em></strong> (<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">Richardson et al. 2024</a>).</p> <p>This is a set of young stellar object (YSO) models with associated spectral energy distributions (SEDs) calculated through radiative transfer. It is a significant update to the data published alongside Robitaille (2017, R17). It contains the parameters shaping each model and adds the newly calculated parameters of envelope mass, average dust temperature, disk stability, and line-of-sight extinction. It also makes explicit quantities, such as source luminosity, that were left implicit in the previous release. This set also convolves the SEDs with several new filters, primarily those on the James Webb Space Telescope, and adds a script to facilitate convolution of these models with additional filters as desired by users. All data included in Version 1.1 of the R17 set (the most recent) are included here.</p> <p>Like their predecessors, these models are versioned. Updates will be released as more models are completed or other changes are made.</p> <p>Files unzip to r+24_models-{version}/{geometry}. "files.tar.gz" contains scripts for SED convolution and main sequence comparison, the opacity to absorption of dust used in the radiative transfer calculations, main sequence T/L values used for results in the accompanying work, and reference material for the contents of the dataset and latest version.</p> <p>The primary use of these models is as templates for SED fitting. The R17 models were structured for use with the <a href="https://sedfitter.readthedocs.io/en/stable/" target="_blank" rel="noopener">sedfitter</a> python package, which enables fitting and analysis of the fit results. For a version of sedfitter which accommodates the new additions, use <a href="https://github.com/richardson-t/sedfitter/tree/dev" target="_blank" rel="noopener">this fork</a>.</p>
Parameter catlogs of "Photometric Stellar Parameters for 195,478 KIC Stars"
<h1>Data Abstract</h1> <p>Here are the parameter catalogs of KIC (Kepler Input Catalog) stars from the paper "Photometric Stellar Parameters for 195,478 KIC Stars." The paper includes two separate catalogs: one for A, F, G, and K type stars, and another for M type stars. Please note that the reliability of M-type star parameters is lower compared to that of AFGK stars, and caution is advised when using these values for further analysis. For this reason, we do not provide isochrone-fitting parameters for M-type stars. For detailed information on each catalog, please refer to the "Catalog Description" section. Each catalog is available in both FITS and CSV formats, with identical data content across these formats.</p> <h1>Article Abstract</h1> <p>The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1\,dex on metallicity [Fe/H], 100\,K on effective temperature $T_{\rm eff}$, and 0.2\,dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. The resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.</p> <h1>Status of Paper Publication</h1> <p>Paper ‘Photometric Stellar Parameters for 195,478 KIC Stars’ is published by <em>ApJS</em>.</p> <h1>Notes of the Data</h1> <div>Please note the following two points:</div> <ol> <li> <div>The A, F, G and K-type star parameter table contains the complete rows of KIC, with a total of 200,038 rows. The M-type star table includes the stars that have been filtered with $(BP-RP)_0$ > 1.8.</div> </li> <li> <div> <p>The total number of stars with parameter measurements is determined as follows:</p> <p>1) For A-, F-, G-, and K-type stars, we include those with metallicity measurements derived from either photometric data synthesized from Gaia XP spectra or KIS photometry, resulting in a total of 190,226 stars.</p> <p>2) For M-type stars, only 5,252 stars with metallicity measurements based on Gaia XP spectra are considered, as the UV radiation of M-type stars is weak, making KIS-based measurements unreliable. Although these KIS-derived values are listed in the table, they are not included in the final count.</p> <p>Thus, the total number of stars with reliable parameter measurements presented in this article is 195,478.</p> </div> </li> </ol> <h1>Catalog Description</h1> <table> <tbody> <tr> <td>Column Name</td> <td>Description</td> </tr> <tr> <td>kepid</td> <td>ID of the KIC star</td> </tr> <tr> <td>degree$\_$ra</td> <td>RA of KIC star</td> </tr> <tr> <td>degree$\_$dec</td> <td>DEC of KIC star</td> </tr> <tr> <td>$G$</td> <td>$G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$G$$\_$err</td> <td>Uncertainty of $G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$</td> <td>$BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$$\_$err</td> <td>Uncertainty of $BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$</td> <td>$RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$$\_$err</td> <td>Uncertainty of $RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP-RP$</td> <td>$BP-RP$ color from Gaia DR3</td> </tr> <tr> <td>$v$</td> <td>$v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$v$$\_$err</td> <td>Uncertainty of $v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$</td> <td>$b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$$\_$err</td> <td>Uncertainty of $b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$</td> <td>$y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$$\_$err</td> <td>Uncertainty of $y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$U$</td> <td>$U$-band photometry from KIS DR2</td> </tr> <tr> <td>$U$$\_$err</td> <td>Uncertainty of $U$-band photometry from KIS DR2</td> </tr> <tr> <td>$E(BP-RP)$</td> <td>$BP-RP$ excess from our 3-D extinction map</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT/MH$\_$KIS$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT$\_$err/MH$\_$KIS$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT/MH$\_$GaiaSyn$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT$\_$err/MH$\_$GaiaSyn$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT</td> <td>Photometric Effective temperature</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT$\_$err</td> <td>Uncertainty of photometric effective temperature</td> </tr> <tr> <td>log $g$$\_$PHOT</td> <td>Photometric Surface gravity</td> </tr> <tr> <td>log $g$$\_$PHOT$\_$err</td> <td>Photometric Uncertainty of surface gravity</td> </tr> <tr> <td>Age$\_$ISO$^b$</td> <td>Age by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$low$^b$</td> <td>16th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$up$^b$</td> <td>84th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$^b$</td> <td>Mass by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of mass fitted by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$^b$</td> <td>Surface gravity by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of surface gravity by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$^b$</td> <td>Effective temperature by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of effective temperature by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$^b$</td> <td>Luminosity by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of luminosity by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$^b$</td> <td>Radius by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of radius fitted by isochrone-fitting method</td> </tr> <tr> <td>FeH$\_$LAMOST/MH$\_$LAMOST$^{a}$</td> <td>[Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$LAMOST$\_$err/MH$\_$LAMOST$\_$err$^{a}$</td> <td>Uncertainty of [Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST</td> <td>Effective temperature from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST$\_$err</td> <td>ncertainty of effective temperature from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST</td> <td>Surface gravity from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST$\_$err</td> <td>Uncertainty of surface gravity from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$APOGEE</td> <td>[Fe/H] from APOGEE DR17</td> </tr> <tr> <td>FeH$\_$APOGEE$\_$err</td> <td>Uncertainty of [Fe/H] from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE</td> <td>Effective temperature from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE$\_$err</td> <td>Uncertainty of effective temperature from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE</td> <td>Surface gravity from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE$\_$err</td> <td>Uncertainty of surface gravity from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE</td> <td>[M/H] from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE$\_$err</td> <td>Uncertainty of [M/H] from APOGEE DR17</td> </tr> <tr> <td>pmra</td> <td>Proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmra$\_$err</td> <td>Uncertainty of proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmdec</td> <td>Proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>pmdec$\_$err</td> <td>Uncertainty of proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>rgeo</td> <td>Geometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$low</td> <td>16th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$up</td> <td>84th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo</td> <td>Photogeometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$low</td> <td>16th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$up</td> <td>84th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>StarType</td> <td>Type of KIC star, including TO (turn-off), Giant, MS (main sequence), Binary, HS (hot star), Mgaint (M-type giant) and Mdwarf (M-type dwarf)</td> </tr> </tbody> </table> <p><br>a: [Fe/H] for A, F, G and K type stars and [M/H] for M type stars.<br>b: Only for A, F, G and K type stars.</p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Data and Code for: 'Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907', Huber et al. 2024.
<p>Data and code to reproduce plots for the paper '<em>Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907'</em>, Huber et al. 2024.</p> <p>Descriptions of the enclosed data files are as follows:</p> <div> <ul> <li>Freqs_best_fit.dat: Best-fitting GARSTEC model frequencies (Figure 3, right)</li> <li>KIC10006158_spec.txt: Reduced and normalized HDS spectrum of KIC10006158 (Figure 1)</li> <li>KIC8144907_ps.txt: Power spectrum of the Kepler light curve of KIC8144907 (Figure 3, left)</li> <li>KIC8144907_spec.txt: Reduced and normalized HDS spectrum of KIC8144907 (Figure 1)</li> <li>apokasc2.tsv: APOKASC sample from Pinsonneault+ 2014 (Figure 2)</li> <li>dwarfs.csv: Asteroseismic sample from Serenelli+ 2017 (Figure 2) </li> <li>freqs.csv: Data in Table 1</li> <li>hosts.tsv: Asteroseismic ages from Silva Aguirre+ 2015 (Figure 4) </li> <li>legacy-t1.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>legacy-t2.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>li-2020.csv: Asteroseismic ages from Li+ 2020 (Figure 4) </li> <li>matsuno.txt: Asteroseismic sample from Matsuno+ 2021 (Figure 2) </li> </ul> </div>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
Aquarius stellar halo simulations (Pu et al. 2024, Cooper et al. 2010)
<p>Aquarius stellar halo simulations produced with the STINGS particle tagging technique and the Galform semi-analytic model, with additional satellite progenitor labels.</p> <p>Please cite Pu et al. (2024) and Cooper et al. (2010) for use of these data, and Cooper et al. (2017) for details of the STINGS method.</p> <p>For a data model description and further details see https://github.com/nthu-ga/aquarius-halos. </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.