Cataloging Accreted Stars within Gaia DR2 Using Deep Learning

Name: Cataloging Accreted Stars within Gaia DR2 Using Deep Learning
Creator: Zenodo
Published: 2020-03-10T18:05:23+08:00

Zenodo2020-08-01 更新2026-05-25 收录

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In [1907.06652], 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 [1907.07190] and [1907.07681]. In this release of the catalog, the data is in the HDF5 file format. We recommended using the pandas package within python (with pytables installed). Then the DataFrame can be loaded with <pre><code class="language-python">Stars = pd.read_hdf('Public_AllStars_6D.h5')</code></pre> We have included 4 different files. The scores for all of the stars with $\delta \varpi / \varpi < 0.1$ 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 source_id, int64 l, float64 b, float64 ra, float64 dec, float64 parallax, float64 pmra, float64 pmdec, float64 phot_g_mean_mag, float32 phot_bp_mean_mag, float32 phot_rp_mean_mag, float32 radial_velocity, float64 Score, float32 PhotometricScores, float32 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, these results are less robust than the first network. If you use our catalog in any of your works, please cite @article{Ostdiek:2019gnb, author = "Ostdiek, Bryan and Necib, Lina and Cohen, Timothy and Freytsis, Marat and Lisanti, Mariangela and Garrison-Kimmel, Shea and Wetzel, Andrew and Sanderson, Robyn E. and Hopkins, Philip F.", title = "{Cataloging Accreted Stars within Gaia DR2 using Deep Learning}", year = "2019", eprint = "1907.06652", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.06652;%%” } and @article{Necib:2019zka, author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea", title = "{Chasing Accreted Structures within Gaia DR2 using Deep Learning}", year = "2019", eprint = "1907.07681", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.07681;%%” }. Also, please cite @article{Necib:2019zbk, author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea and Hopkins, Philip F. and Wetzel, Andrew and Sanderson, Robyn", title = "{Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity}", year = "2019", eprint = "1907.07190", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.07190;%%" } for any follow up study of Nyx.

在预印本[1907.06652]中，研究人员采用神经网络（neural network），仅利用5维相空间（5D phase space）信息，判定恒星是被吸积至银河系（Milky Way），还是原位形成（in situ）。该神经网络的输入数据来自盖亚DR2星表（Gaia DR2 catalog）的[l, b, parallax, pmra, pmdec]。得分高于0.75的恒星被标记为吸积恒星。在文献[1907.07190]与[1907.07681]中，对包含6维相空间信息的恒星子集开展了更细致的研究。本版本的星表数据采用HDF5文件格式（HDF5 file format）存储，我们推荐在Python环境中使用pandas包（需预先安装pytables）加载数据，可通过如下代码读取DataFrame： <pre><code class="language-python">Stars = pd.read_hdf('Public_AllStars_6D.h5')</code></pre> 本数据集共包含4个不同文件： 1. 所有满足$delta varpi / varpi < 0.1$的恒星的得分数据存储于`Public_AllStars_5D.h5`； 2. 上述恒星中同时具备视向速度测量数据的子集存储于`Public_AllStars_6D.h5`； 3. 文件名带有`SelectedByNetwork`的文件仅包含通过神经网络筛选、被判定为吸积恒星的样本，其5维、6维数据字段与全样本文件一致。每个文件均包含以下字段： - `source_id`（int64类型）：可与盖亚DR2星表匹配，便于跨星表交叉检索 - `l`（float64）：银经 - `b`（float64）：银纬 - `ra`（float64）：赤经 - `dec`（float64）：赤纬 - `parallax`（float64）：视差 - `pmra`（float64）：赤经方向自行 - `pmdec`（float64）：赤纬方向自行 - `phot_g_mean_mag`（float32）：G波段平均星等 - `phot_bp_mean_mag`（float32）：BP波段平均星等 - `phot_rp_mean_mag`（float32）：RP波段平均星等 - `radial_velocity`（float64）：视向速度 - `Score`（float32）：仅使用5维运动学信息的神经网络输出得分，若判定为吸积恒星，该得分需大于0.75 - `PhotometricScores`（float32）：结合测光波段与5维运动学信息的神经网络输出得分，其最优截断阈值为0.9，但如论文所述，**该结果的鲁棒性弱于前述神经网络**。若您在研究中使用本星表，请引用以下两篇文献： bibtex @article{Ostdiek:2019gnb, author = "Ostdiek, Bryan and Necib, Lina and Cohen, Timothy and Freytsis, Marat and Lisanti, Mariangela and Garrison-Kimmel, Shea and Wetzel, Andrew and Sanderson, Robyn E. and Hopkins, Philip F.", title = "{Cataloging Accreted Stars within Gaia DR2 using Deep Learning}", year = "2019", eprint = "1907.06652", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.06652;%%" } 以及： bibtex @article{Necib:2019zka, author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea", title = "{Chasing Accreted Structures within Gaia DR2 using Deep Learning}", year = "2019", eprint = "1907.07681", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.07681;%%" } 此外，若开展关于Nyx的后续研究，请引用： bibtex @article{Necib:2019zbk, author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea and Hopkins, Philip F. and Wetzel, Andrew and Sanderson, Robyn", title = "{Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity}", year = "2019", eprint = "1907.07190", archivePrefix = "arXiv", primaryClass = "astro-ph.GA", SLACcitation = "%%CITATION = ARXIV:1907.07190;%%" }

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Zenodo

创建时间：

2020-03-10

Cataloging Accreted Stars within Gaia DR2 Using Deep Learning

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