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7 results for “Gaia DR2”
Various Gaia DR2 subsets
<p>This dataset contains various subsets of DR2 data;</p> <p>- within 20 pc<br> - within 25 pc<br> - within 100 pc<br> - within 125 pc<br> - within 200 pc<br> - between 200 and 300 pc<br> - "clean" version of 100 pc (several astrometric quality flag filters applied for cleaner color-mag diagrams)<br> - "clean" version of 200 pc<br> - All white dwarfs within 100 pc<br> - All white dwarfs within 300 pc<br> - All white dwarfs in the Limoges et al. 2015 sample<br> - Rough cut around NGC 6774<br> - Rough cut around M67</p> <p>The file names should be self-explanatory</p>
Simple distance estimates for Gaia DR2 stars with radial velocities
<p>Bayesian distance estimates for stars with radial velocities and parallaxes published in <em>Gaia</em> DR2. Our method and prior is designed to apply to this specific subset of stars in <em>Gaia</em> DR2.</p> <p>The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426</p> <p>The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548</p>
wide binaries from Gaia DR2
<p>This is a catalog of 378,488 wide binary candidates selected using data from Gaia DR2. The catalog construction procedure is similar to that outlined in <a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.4884E/abstract">this</a> paper, but the search is extended to larger distances (d < 1 kpc), lower astrometric SNR (parallax_over_error > 10), and wider separations (s < 1 pc). </p> <p>The included pdf shows the separation distribution, and the red dashed line shows an estimate of the contamination rate from chance alignments. For the full catalog, contamination is negligible at s < 50,000 AU, but it becomes dominant at wider separations, such that it completely overwhelms the real binaries at s ~ 1 pc. It is, however, possible to construct subsamples that are pure out to wider separations by selecting binaries with (a) larger parallaxes, (b) higher astrometric SNR, or (c) larger plane-of-the-sky velocities with respect to the Sun. </p> <p>If you use this catalog for your research, please cite this dataset and the paper linked above. For questions or comments, contact Kareem El-Badry. </p>
Hot subdwarf stars observed in Gaia DR2 and LAMOST DR5
<p>Table2.txt: Atmospheric parameters and space positions for 892 non-composite spectra hot subdwarf stars selected from Gaia DR2 and LAMOST DR5.</p> <p>Table3.txt: Orbital parameters, galactic velocities for 747 hot subdwarf stars selected from Gaia DR2 and LAMOST DR5</p>
Cataloging Accreted Stars within Gaia DR2 Using Deep Learning
<p>In [<a href="http://arxiv.org/abs/1907.06652">1907.06652</a>], a neural network is used to determine whether a star was accreted onto the Milky Way or was born in situ using only 5D phase space information. The inputs to the network are [l, b, parallax, pmra, pmdec] from the Gaia DR2 catalog. Stars with a score greater than 0.75 are marked as accreted. The subset of stars with 6D phase space information was studied in more detail in [<a href="https://arxiv.org/abs/1907.07190">1907.07190</a>] and [<a href="http://arxiv.org/abs/1907.07681">1907.07681</a>].</p> <p>In this release of the catalog, the data is in the HDF5 file format. We recommended using the pandas package within python (with <a href="https://www.pytables.org/usersguide/installation.html">pytables</a> installed). Then the DataFrame can be loaded with </p> <pre><code class="language-python">Stars = pd.read_hdf('Public_AllStars_6D.h5')</code></pre> <p>We have included 4 different files. The scores for all of the stars with <span class="math-tex">\(\delta \varpi / \varpi < 0.1\)</span> are given in Public_AllStars_5D.h5. The subset of these stars which also have line-of-sight velocity measurements are given in Public_AllStars_6D.h5. The files denoted with SelectedByNetwork have only stars which pass the neural network selection as being accreted, with 5D and 6D the same as for AllStars. Each file include the columns</p> <ol> <li><strong>source_id, int64</strong></li> <li><strong>l, float64</strong></li> <li><strong>b, float64</strong></li> <li><strong>ra, float64</strong></li> <li><strong>dec, float64</strong></li> <li><strong>parallax, float64</strong></li> <li><strong>pmra, float64</strong></li> <li><strong>pmdec, float64</strong></li> <li><strong>phot_g_mean_mag, float32</strong></li> <li><strong>phot_bp_mean_mag, float32</strong></li> <li><strong>phot_rp_mean_mag, float32</strong></li> <li><strong>radial_velocity, float64</strong></li> <li><strong>Score, float32</strong></li> <li><strong>PhotometricScores, float32</strong></li> </ol> <p>The "source_id" should match with Gaia DR2, allowing for easy cross referencing. The "Score" column is the output of the network using only 5D kinematic information, to be considered as accreted, the score needs to be larger than 0.75. The "PhotometricScores" column is the network output for the neural network which uses the "phot" columns along with the 5D kinematics. The optimal cut for this is 0.9, but as shown in the paper, <em>these results are less robust than the first network</em>.</p> <p> </p> <p>If you use our catalog in any of your works, please cite <br> @article{Ostdiek:2019gnb,<br> author = "Ostdiek, Bryan and Necib, Lina and Cohen, Timothy and<br> Freytsis, Marat and Lisanti, Mariangela and<br> Garrison-Kimmel, Shea and Wetzel, Andrew and Sanderson,<br> Robyn E. and Hopkins, Philip F.",<br> title = "{Cataloging Accreted Stars within Gaia DR2 using Deep<br> Learning}",<br> year = "2019",<br> eprint = "1907.06652",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.06652;%%”<br> }<br> and <br> @article{Necib:2019zka,<br> author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela<br> and Cohen, Timothy and Freytsis, Marat and<br> Garrison-Kimmel, Shea",<br> title = "{Chasing Accreted Structures within Gaia DR2 using Deep<br> Learning}",<br> year = "2019",<br> eprint = "1907.07681",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.07681;%%”<br> }.<br> Also, please cite <br> @article{Necib:2019zbk,<br> author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela<br> and Cohen, Timothy and Freytsis, Marat and<br> Garrison-Kimmel, Shea and Hopkins, Philip F. and Wetzel,<br> Andrew and Sanderson, Robyn",<br> title = "{Evidence for a Vast Prograde Stellar Stream in the Solar<br> Vicinity}",<br> year = "2019",<br> eprint = "1907.07190",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.07190;%%"<br> }<br> for any follow up study of Nyx.</p>
Gaia Source Catalogue DR2
The Gaia DR2 Source Catalogue contains positions and brightnesses for 1.693 billion stars, including distances and proper motions for more than 1.3 billion stars. For more details, see the Gaia documentation, particularly the Source Catalogue columns description.
GAIA DR2 ConeSearch
All MAST catalog holdings are available via Cone Search endpoints. This service provides access to the MAST mirror of the GAIA catalog data release 2.All available missions are listed at http://archive.stsci.edu/vo/mast_services.html.
ScienceDex guides
Understand access before you commit
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OpenNeuro
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