{"title":"机器学习揭示附近星系的合并历史","authors":"","doi":"10.1038/s41550-024-02335-3","DOIUrl":null,"url":null,"abstract":"A probabilistic machine learning method trained on cosmological simulations is used to determine whether stars in 10,000 nearby galaxies formed internally or were accreted from other galaxies during merging events. The model predicts that only 20% of the stellar mass in present day galaxies is the result of past mergers.","PeriodicalId":18778,"journal":{"name":"Nature Astronomy","volume":"8 10","pages":"1218-1219"},"PeriodicalIF":12.9000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning reveals the merging history of nearby galaxies\",\"authors\":\"\",\"doi\":\"10.1038/s41550-024-02335-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A probabilistic machine learning method trained on cosmological simulations is used to determine whether stars in 10,000 nearby galaxies formed internally or were accreted from other galaxies during merging events. The model predicts that only 20% of the stellar mass in present day galaxies is the result of past mergers.\",\"PeriodicalId\":18778,\"journal\":{\"name\":\"Nature Astronomy\",\"volume\":\"8 10\",\"pages\":\"1218-1219\"},\"PeriodicalIF\":12.9000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Astronomy\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s41550-024-02335-3\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Astronomy","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s41550-024-02335-3","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Machine learning reveals the merging history of nearby galaxies
A probabilistic machine learning method trained on cosmological simulations is used to determine whether stars in 10,000 nearby galaxies formed internally or were accreted from other galaxies during merging events. The model predicts that only 20% of the stellar mass in present day galaxies is the result of past mergers.
Nature AstronomyPhysics and Astronomy-Astronomy and Astrophysics
CiteScore
19.50
自引率
2.80%
发文量
252
期刊介绍：
Nature Astronomy, the oldest science, has played a significant role in the history of Nature. Throughout the years, pioneering discoveries such as the first quasar, exoplanet, and understanding of spiral nebulae have been reported in the journal. With the introduction of Nature Astronomy, the field now receives expanded coverage, welcoming research in astronomy, astrophysics, and planetary science. The primary objective is to encourage closer collaboration among researchers in these related areas.
Similar to other journals under the Nature brand, Nature Astronomy boasts a devoted team of professional editors, ensuring fairness and rigorous peer-review processes. The journal maintains high standards in copy-editing and production, ensuring timely publication and editorial independence.
In addition to original research, Nature Astronomy publishes a wide range of content, including Comments, Reviews, News and Views, Features, and Correspondence. This diverse collection covers various disciplines within astronomy and includes contributions from a diverse range of voices.
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