Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method
In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([ α /Fe], [C...
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| Veröffentlicht in: | The Astrophysical journal 2024-05, Vol.967 (1), p.37 |
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| container_title | The Astrophysical journal |
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| creator | Wang, Hai-Feng Carraro, Giovanni Li, Xin Li, Qi-Da Spina, Lorenzo Chen, Li Wang, Guan-Yu Deng, Li-Cai |
| description | In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([
α
/Fe], [C/Fe],
T
eff
, [N/Fe], [C/H], log
g
) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age. |
| doi_str_mv | 10.3847/1538-4357/ad3b90 |
| format | Article |
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α
/Fe], [C/Fe],
T
eff
, [N/Fe], [C/H], log
g
) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age.</description><identifier>ISSN: 0004-637X</identifier><identifier>EISSN: 1538-4357</identifier><identifier>DOI: 10.3847/1538-4357/ad3b90</identifier><language>eng</language><publisher>Philadelphia: The American Astronomical Society</publisher><subject>Age determination ; Algorithms ; Apogees ; Catalogues ; Chronology ; Datasets ; Decision trees ; Iron ; Milky Way disk ; Open clusters ; Parameters ; Red giant branch ; Red giant stars ; Spatial distribution ; Stars ; Stellar age ; Stellar ages ; Stellar seismology</subject><ispartof>The Astrophysical journal, 2024-05, Vol.967 (1), p.37</ispartof><rights>2024. The Author(s). Published by the American Astronomical Society.</rights><rights>2024. The Author(s). Published by the American Astronomical Society. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c398t-5bb05554dc7a3d2319f80810c437775838e881e136b82ebde8ceb8e532e3046a3</cites><orcidid>0000-0001-8459-1036 ; 0000-0002-0155-9434 ; 0000-0002-9760-6249</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.3847/1538-4357/ad3b90/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,860,2096,27901,27902,38867,53842</link.rule.ids></links><search><creatorcontrib>Wang, Hai-Feng</creatorcontrib><creatorcontrib>Carraro, Giovanni</creatorcontrib><creatorcontrib>Li, Xin</creatorcontrib><creatorcontrib>Li, Qi-Da</creatorcontrib><creatorcontrib>Spina, Lorenzo</creatorcontrib><creatorcontrib>Chen, Li</creatorcontrib><creatorcontrib>Wang, Guan-Yu</creatorcontrib><creatorcontrib>Deng, Li-Cai</creatorcontrib><title>Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method</title><title>The Astrophysical journal</title><addtitle>APJ</addtitle><addtitle>Astrophys. J</addtitle><description>In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([
α
/Fe], [C/Fe],
T
eff
, [N/Fe], [C/H], log
g
) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age.</description><subject>Age determination</subject><subject>Algorithms</subject><subject>Apogees</subject><subject>Catalogues</subject><subject>Chronology</subject><subject>Datasets</subject><subject>Decision trees</subject><subject>Iron</subject><subject>Milky Way disk</subject><subject>Open clusters</subject><subject>Parameters</subject><subject>Red giant branch</subject><subject>Red giant stars</subject><subject>Spatial distribution</subject><subject>Stars</subject><subject>Stellar age</subject><subject>Stellar ages</subject><subject>Stellar seismology</subject><issn>0004-637X</issn><issn>1538-4357</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>DOA</sourceid><recordid>eNp1kc9vFCEYhonRxHX17pFEj07LDDDAcdvq2mSbJnZNvBF-fLPLph1GoAf_e5mOqRc9ET6e9-FLXoTet-SMSibOW05lwygX58ZTq8gLtHoevUQrQghreip-vEZvcj7N106pFRo2B8BXUCA9hNGUEEccB7zb3Nze7fE38HgbzFjwRTKjO-K7YlLGFybXh0qWI-BtMj7AjMSYSxgP1eZCnkX7BIBvoByjf4teDeY-w7s_5xp9__J5f_m12d1ury83u8ZRJUvDrSWcc-adMNR3tFWDJLIljlEhBJdUgpQttLS3sgPrQTqwEjjtgBLWG7pG14vXR3PSUwoPJv3S0QT9NIjpoE0qwd2DJkoYUDWkesJsx6Q1dmDWcqmc9FW4Rh8W15Tiz0fIRZ_iYxrr-prWLRXtBe8rRRbKpZhzguH515bouRk916DnGvTSTI18XCIhTn-dZjpp1VdcU6EnP1Ts0z-w_1p_A0OBma4</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Wang, Hai-Feng</creator><creator>Carraro, Giovanni</creator><creator>Li, Xin</creator><creator>Li, Qi-Da</creator><creator>Spina, Lorenzo</creator><creator>Chen, Li</creator><creator>Wang, Guan-Yu</creator><creator>Deng, Li-Cai</creator><general>The American Astronomical Society</general><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8459-1036</orcidid><orcidid>https://orcid.org/0000-0002-0155-9434</orcidid><orcidid>https://orcid.org/0000-0002-9760-6249</orcidid></search><sort><creationdate>20240501</creationdate><title>Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method</title><author>Wang, Hai-Feng ; Carraro, Giovanni ; Li, Xin ; Li, Qi-Da ; Spina, Lorenzo ; Chen, Li ; Wang, Guan-Yu ; Deng, Li-Cai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-5bb05554dc7a3d2319f80810c437775838e881e136b82ebde8ceb8e532e3046a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Age determination</topic><topic>Algorithms</topic><topic>Apogees</topic><topic>Catalogues</topic><topic>Chronology</topic><topic>Datasets</topic><topic>Decision trees</topic><topic>Iron</topic><topic>Milky Way disk</topic><topic>Open clusters</topic><topic>Parameters</topic><topic>Red giant branch</topic><topic>Red giant stars</topic><topic>Spatial distribution</topic><topic>Stars</topic><topic>Stellar age</topic><topic>Stellar ages</topic><topic>Stellar seismology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Hai-Feng</creatorcontrib><creatorcontrib>Carraro, Giovanni</creatorcontrib><creatorcontrib>Li, Xin</creatorcontrib><creatorcontrib>Li, Qi-Da</creatorcontrib><creatorcontrib>Spina, Lorenzo</creatorcontrib><creatorcontrib>Chen, Li</creatorcontrib><creatorcontrib>Wang, Guan-Yu</creatorcontrib><creatorcontrib>Deng, Li-Cai</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>The Astrophysical journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Hai-Feng</au><au>Carraro, Giovanni</au><au>Li, Xin</au><au>Li, Qi-Da</au><au>Spina, Lorenzo</au><au>Chen, Li</au><au>Wang, Guan-Yu</au><au>Deng, Li-Cai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method</atitle><jtitle>The Astrophysical journal</jtitle><stitle>APJ</stitle><addtitle>Astrophys. J</addtitle><date>2024-05-01</date><risdate>2024</risdate><volume>967</volume><issue>1</issue><spage>37</spage><pages>37-</pages><issn>0004-637X</issn><eissn>1538-4357</eissn><abstract>In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([
α
/Fe], [C/Fe],
T
eff
, [N/Fe], [C/H], log
g
) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age.</abstract><cop>Philadelphia</cop><pub>The American Astronomical Society</pub><doi>10.3847/1538-4357/ad3b90</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-8459-1036</orcidid><orcidid>https://orcid.org/0000-0002-0155-9434</orcidid><orcidid>https://orcid.org/0000-0002-9760-6249</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age determination Algorithms Apogees Catalogues Chronology Datasets Decision trees Iron Milky Way disk Open clusters Parameters Red giant branch Red giant stars Spatial distribution Stars Stellar age Stellar ages Stellar seismology |
| title | Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method |
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