分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars by Data Release 9 (DR9), which can significantly increase the sample size of barium stars. In this paper, we used machine learning algorithms to search for barium stars from low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting Machine (LGBM) algorithm to build classifiers of barium stars based on different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of barium candidates. The classification with features in the whole spectrum performs best: for the sample with strontium enhancement, Precision = 97.81%, and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03% and Recall = 97.70%. In prediction, [Ba/Fe] estimated from BaII line at 4554 \r{A} has smaller dispersion than that from BaII line at 4934 \r{A}: MAE$_{4554 \r{A}}$ = 0.07, $\sigma_{4554 \r{A}}$ = 0.12. [Sr/Fe] estimated from SrII line at 4077 \r{A} performs better than that from SrII line at 4215 \r{A}: MAE$_{4077 \r{A}}$ = 0.09, $\sigma_{4077 \r{A}}$ = 0.16. A comparison of the LGBM and other popular algorithms shows that LGBM is accurate and efficient in classifying barium stars. This work demonstrated that machine learning can be used as an effective means to identify chemically peculiar stars and determine their elemental abundance.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： A small fraction of giants possess photospheric lithium(Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giants sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A(Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE=0.15 dex, and {\sigma}=0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7,768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A(Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： A small fraction of giants possess photospheric lithium(Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giants sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A(Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE=0.15 dex, and {\sigma}=0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7,768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A(Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars by Data Release 9 (DR9), which can significantly increase the sample size of barium stars. In this paper, we used machine learning algorithms to search for barium stars from low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting Machine (LGBM) algorithm to build classifiers of barium stars based on different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of barium candidates. The classification with features in the whole spectrum performs best: for the sample with strontium enhancement, Precision = 97.81%, and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03% and Recall = 97.70%. In prediction, [Ba/Fe] estimated from BaII line at 4554 \r{A} has smaller dispersion than that from BaII line at 4934 \r{A}: MAE$_{4554 \r{A}}$ = 0.07, $\sigma_{4554 \r{A}}$ = 0.12. [Sr/Fe] estimated from SrII line at 4077 \r{A} performs better than that from SrII line at 4215 \r{A}: MAE$_{4077 \r{A}}$ = 0.09, $\sigma_{4077 \r{A}}$ = 0.16. A comparison of the LGBM and other popular algorithms shows that LGBM is accurate and efficient in classifying barium stars. This work demonstrated that machine learning can be used as an effective means to identify chemically peculiar stars and determine their elemental abundance.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Zhenping Yi
Jia Li
Wei Du
Meng Liu
Zengxu Liang
Yongguang Xing
Jingchang Pan
Yude Bu
Xiaoming Kong
Hong Wu
摘要： Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD), which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 mag arcsec $^ {- 2} $ to 24 mag arcsec $^ {- 2} $, quite consistent with the surface brightness distribution of the standard sample. 96.46\% of LSB galaxy candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them have $fracDev\_r$\textless 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next generation observatories.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Very metal-poor (VMP, [Fe/H]-2.0) from LAMOST DR8 for the experiment and make comparisons. All spectra are reduced to R~200 to match the resolution of the CSST and are preprocessed and collapsed into two-dimensional spectra for input to the CNN model. The results show that the MAE values are 99.40 K for $T_{eff}$, 0.22 dex for $\log g$, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe], respectively. Besides, the CNN model efficiently identifies VMP stars with a precision of 94.77%. The validation and practicality of this model are also tested on the MARCS synthetic spectra. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra (R~200) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Very metal-poor (VMP, [Fe/H]-2.0) from LAMOST DR8 for the experiment and make comparisons. All spectra are reduced to R~200 to match the resolution of the CSST and are preprocessed and collapsed into two-dimensional spectra for input to the CNN model. The results show that the MAE values are 99.40 K for $T_{eff}$, 0.22 dex for $\log g$, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe], respectively. Besides, the CNN model efficiently identifies VMP stars with a precision of 94.77%. The validation and practicality of this model are also tested on the MARCS synthetic spectra. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra (R~200) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Hot subdwarf stars are very important for understanding stellar evolution, stellar astrophysics, and binary star systems. Identifying more such stars can help us better understand their statistical distribution, properties, and evolution. In this paper, we present a new method to search for hot subdwarf stars in photometric data (b, y, g, r, i, z) using a machine learning algorithm, graph neural network, and Gaussian mixture model. We use a Gaussian mixture model and Markov distance to build the graph structure, and on the graph structure, we use a graph neural network to identify hot subdwarf stars from 86 084 stars, when the recall, precision, and f1 score are maximized on the original, weight and synthetic minority oversampling technique datasets. Finally, from 21 885 candidates, we selected approximately 6 000 stars that were the most similar to the hot subdwarf star.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Hot subdwarf stars are very important for understanding stellar evolution, stellar astrophysics, and binary star systems. Identifying more such stars can help us better understand their statistical distribution, properties, and evolution. In this paper, we present a new method to search for hot subdwarf stars in photometric data (b, y, g, r, i, z) using a machine learning algorithm, graph neural network, and Gaussian mixture model. We use a Gaussian mixture model and Markov distance to build the graph structure, and on the graph structure, we use a graph neural network to identify hot subdwarf stars from 86 084 stars, when the recall, precision, and f1 score are maximized on the original, weight and synthetic minority oversampling technique datasets. Finally, from 21 885 candidates, we selected approximately 6 000 stars that were the most similar to the hot subdwarf star.
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