Pub Date : 2024-08-22DOI: 10.3847/1538-4365/ad6261
YiMing He, Zhong Cao, Hui Deng, Feng Wang, Ying Mei, Lei Tan
Carbon stars play a crucial role in astronomical research and are significant for understanding stellar evolution, measuring cosmic distances, and studying galaxy kinematics. In recent years, identifying carbon stars using machine learning methods and traditional line-index methods has become a research hotspot, but there are still limitations regarding accuracy and automation. In this study, we propose to build a five-class model to identify carbon stars using spectral data from LAMOST DR9. The model achieved 99.45% precision and 91.21% recall on the carbon star testing set. We conducted independent tests using a sample of 1333 known carbon stars that were not used in the training and testing phases, and our model ultimately identified 1199 carbon stars. On this basis, we used this model to screen 11,226,252 spectra of LAMOST DR9 and identified 4383 carbon stars, including 1197 newly discovered carbon stars. To gain a more comprehensive understanding of the characteristics of the 4383 carbon stars obtained, further visual inspection of these spectra was performed to provide more detailed carbon star subtypes.
{"title":"Identification of Carbon Stars in LAMOST DR9 Based on Deep Learning","authors":"YiMing He, Zhong Cao, Hui Deng, Feng Wang, Ying Mei, Lei Tan","doi":"10.3847/1538-4365/ad6261","DOIUrl":"https://doi.org/10.3847/1538-4365/ad6261","url":null,"abstract":"Carbon stars play a crucial role in astronomical research and are significant for understanding stellar evolution, measuring cosmic distances, and studying galaxy kinematics. In recent years, identifying carbon stars using machine learning methods and traditional line-index methods has become a research hotspot, but there are still limitations regarding accuracy and automation. In this study, we propose to build a five-class model to identify carbon stars using spectral data from LAMOST DR9. The model achieved 99.45% precision and 91.21% recall on the carbon star testing set. We conducted independent tests using a sample of 1333 known carbon stars that were not used in the training and testing phases, and our model ultimately identified 1199 carbon stars. On this basis, we used this model to screen 11,226,252 spectra of LAMOST DR9 and identified 4383 carbon stars, including 1197 newly discovered carbon stars. To gain a more comprehensive understanding of the characteristics of the 4383 carbon stars obtained, further visual inspection of these spectra was performed to provide more detailed carbon star subtypes.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-22DOI: 10.3847/1538-4365/ad61e4
Jiawei Miao, Liangping Tu, Bin Jiang, Xiangru Li, Bo Qiu
In the past decade, various sky surveys with a wide range of wavelengths have been conducted, resulting in an explosive growth of survey data. There may be overlapping regions between different surveys, but the data quality and brightness are different. The translation of data quality between different surveys provides benefits for studying the properties of galaxies in specific regions that high-quality surveys have not yet covered. In this paper, we create a data set for analyzing the quality transformation of different surveys, AstroSR, using the galaxy images from overlapping regions from the Subaru/Hyper Suprime-Cam (HSC) and the Sloan Digital Sky Survey (SDSS). In addition, we use superresolution (SR) techniques to improve the quality of low-resolution images in the AstroSR and explore whether the proposed data set is suitable for SR. We try four representative models: EDSR, RCAN, ENLCN, and SRGAN. Finally, we compare the evaluation metrics and visual quality of the above methods. SR models trained with AstroSR successfully generate HSC-like images from SDSS images, which enhance the fine structure present in the SDSS images while retaining important morphological information and increasing the brightness and signal-to-noise. Improving the resolution of astronomical images by SR can improve the size and quality of the sky surveys. The data set proposed in this paper provides strong data support for the study of galaxy SR and opens up new research possibilities in astronomy. The data set is available online at https://github.com/jiaweimmiao/AstroSR.
在过去的十年中,人们开展了各种波长的巡天观测,巡天观测数据呈爆炸式增长。不同巡天之间可能存在重叠区域,但数据质量和亮度却各不相同。不同巡天观测之间数据质量的转换,有利于研究高质量巡天观测尚未覆盖的特定区域的星系特性。在本文中,我们利用来自Subaru/Hyper Suprime-Cam(HSC)和Sloan Digital Sky Survey(SDSS)重叠区域的星系图像,创建了一个用于分析不同巡天质量转换的数据集AstroSR。此外,我们还使用超分辨率(SR)技术来提高 AstroSR 中低分辨率图像的质量,并探索所提议的数据集是否适合 SR。我们尝试了四种具有代表性的模型:EDSR、RCAN、ENLCN 和 SRGAN。最后,我们比较了上述方法的评价指标和视觉质量。使用 AstroSR 训练的 SR 模型成功地从 SDSS 图像生成了类似 HSC 的图像,在保留重要形态信息、提高亮度和信噪比的同时,增强了 SDSS 图像中存在的精细结构。利用 SR 提高天文图像的分辨率可以改善巡天的规模和质量。本文提出的数据集为研究星系SR提供了有力的数据支持,为天文学研究开辟了新的可能性。该数据集可在 https://github.com/jiaweimmiao/AstroSR 在线查阅。
{"title":"AstroSR: A Data Set of Galaxy Images for Astronomical Superresolution Research","authors":"Jiawei Miao, Liangping Tu, Bin Jiang, Xiangru Li, Bo Qiu","doi":"10.3847/1538-4365/ad61e4","DOIUrl":"https://doi.org/10.3847/1538-4365/ad61e4","url":null,"abstract":"In the past decade, various sky surveys with a wide range of wavelengths have been conducted, resulting in an explosive growth of survey data. There may be overlapping regions between different surveys, but the data quality and brightness are different. The translation of data quality between different surveys provides benefits for studying the properties of galaxies in specific regions that high-quality surveys have not yet covered. In this paper, we create a data set for analyzing the quality transformation of different surveys, AstroSR, using the galaxy images from overlapping regions from the Subaru/Hyper Suprime-Cam (HSC) and the Sloan Digital Sky Survey (SDSS). In addition, we use superresolution (SR) techniques to improve the quality of low-resolution images in the AstroSR and explore whether the proposed data set is suitable for SR. We try four representative models: EDSR, RCAN, ENLCN, and SRGAN. Finally, we compare the evaluation metrics and visual quality of the above methods. SR models trained with AstroSR successfully generate HSC-like images from SDSS images, which enhance the fine structure present in the SDSS images while retaining important morphological information and increasing the brightness and signal-to-noise. Improving the resolution of astronomical images by SR can improve the size and quality of the sky surveys. The data set proposed in this paper provides strong data support for the study of galaxy SR and opens up new research possibilities in astronomy. The data set is available online at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/jiaweimmiao/AstroSR\" xlink:type=\"simple\">https://github.com/jiaweimmiao/AstroSR</ext-link>.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an inaugural investigation under the X-ray Winds In Nearby-to-distant Galaxies (X-WING) program, we assembled a data set comprising 132 active galactic nuclei (AGNs) spanning redshifts z ∼ 0–4 characterized by blueshifted absorption lines indicative of X-ray winds. Through an exhaustive review of previous research, we compiled the outflow parameters for 583 X-ray winds, encompassing key attributes such as outflow velocities (V