Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Abstract： The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it is helpful to improve the parameter estimation performance for these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H]) estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30. We proposed a data-driven method based on machine learning techniques. Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data. Secondly, a Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric parameters from the LASSO features. Finally, the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the APOGEE (Apache Point Observatory Galactic Evolution Experiment) high-resolution spectra. Experiments show that the Mean Absolute Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP (137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex), which indicate evident improvements on stellar atmospheric parameter estimation. In addition, this work estimated the stellar atmospheric parameters for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8 using LASSO-MLP, and released the estimation catalog, learned model, experimental code, trained model, training data and test data for scientific exploration and algorithm study.
Peer Review Status:
Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Abstract： We employ Gaia, 2MASS, and ALLWISE photometry, as well as astrometric data from Gaia, to search for relatively bright very metal-poor ([Fe/H] $< -2.0$; VMP) giant star candidates using three different criteria: 1) our derived Gaia photometric metallicities, 2) the lack of stellar molecular absorption near 4.6 microns, and 3) their high tangential velocities. With different combinations of these criteria, we have identified six samples of candidates with $G <$ 15: the Gold sample (24,304 candidates), the Silver GW sample (40,157 candidates), the Silver GK sample (120,452 candidates), the Bronze G sample (291,690 candidates), the Bronze WK sample (68,526 candidates), and the Low $b$ sample (4,645 candidates). The Low $b$ sample applies to sources with low Galactic latitude, $|b| < 10^\circ$, while the others are for sources with $|b| > 10^\circ$. By cross-matching with results derived from medium-resolution ($R \sim$ 1800) from LAMOST DR8, we establish that the success rate for identifying VMP stars is 60.1$\%$ for the Gold sample, 39.2$\%$ for the Silver GW sample, 41.3$\%$ for the Silver GK sample, 15.4$\%$ for the Bronze G sample, 31.7$\%$ for the Bronze WK sample, and 16.6$\%$ for the Low $b$ sample, respectively. An additional strict cut on the quality parameter $RUWE < 1.1$ can further increase the success rate of the Silver GW, Silver GK, and Bronze G samples to 46.9$\%$, 51.6$\%$, and 29.3$\%$, respectively. Our samples provide valuable targets for high-resolution follow-up spectroscopic observations, and are made publicly available.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Abstract： We report the detection of a large sample of high-$\alpha$-metal-rich stars
on the low giant branch with $2.6
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Abstract： We employ Gaia, 2MASS, and ALLWISE photometry, as well as astrometric data from Gaia, to search for relatively bright very metal-poor ([Fe/H] $< -2.0$; VMP) giant star candidates using three different criteria: 1) our derived Gaia photometric metallicities, 2) the lack of stellar molecular absorption near 4.6 microns, and 3) their high tangential velocities. With different combinations of these criteria, we have identified six samples of candidates with $G <$ 15: the Gold sample (24,304 candidates), the Silver GW sample (40,157 candidates), the Silver GK sample (120,452 candidates), the Bronze G sample (291,690 candidates), the Bronze WK sample (68,526 candidates), and the Low $b$ sample (4,645 candidates). The Low $b$ sample applies to sources with low Galactic latitude, $|b| < 10^\circ$, while the others are for sources with $|b| > 10^\circ$. By cross-matching with results derived from medium-resolution ($R \sim$ 1800) from LAMOST DR8, we establish that the success rate for identifying VMP stars is 60.1$\%$ for the Gold sample, 39.2$\%$ for the Silver GW sample, 41.3$\%$ for the Silver GK sample, 15.4$\%$ for the Bronze G sample, 31.7$\%$ for the Bronze WK sample, and 16.6$\%$ for the Low $b$ sample, respectively. An additional strict cut on the quality parameter $RUWE < 1.1$ can further increase the success rate of the Silver GW, Silver GK, and Bronze G samples to 46.9$\%$, 51.6$\%$, and 29.3$\%$, respectively. Our samples provide valuable targets for high-resolution follow-up spectroscopic observations, and are made publicly available.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Haining Li
Wako Aoki
Tadafumi Matsuno
Qianfan Xing
Takuma Suda
Nozomu Tominaga
Yuqin Chen
Satoshi Honda
Miho N. Ishigaki
Jianrong Shi
Jingkun Zhao
Gang Zhao
Abstract： We present homogeneous abundance analysis of over 20 elements for 385 very metal-poor (VMP) stars based on the LAMOST survey and follow-up observations with the Subaru Telescope. It is the largest high-resolution VMP sample (including 363 new objects) studied by a single program, and the first attempt to accurately determine evolutionary stages for such a large sample based on Gaia parallaxes. The sample covers a wide metallicity range from [Fe/H]=-1.7 down to [Fe/H]=-4.3, including over 110 objects with [Fe/H]<-3.0. The expanded coverage in evolutionary status makes it possible to define abundance trends respectively for giants and turn-off stars. The newly obtained abundance data confirm most abundance trends found by previous studies, but also provide useful update and new sample of outliers. The Li plateau is seen in -2.5 < [Fe/H] <-1.7 in our sample, whereas the average Li abundance is clearly lower at lower metallicity. Mg, Si, and Ca are overabundant with respect to Fe, showing decreasing trend with increasing metallicity. Comparisons with chemical evolution models indicate that the over-abundance of Ti, Sc, and Co are not well reproduced by current theoretical predictions. Correlations are seen between Sc and alpha-elements, while Zn shows a detectable correlation only with Ti but not with other alpha-elements. The fraction of carbon-enhanced stars ([C/Fe]> 0.7) is in the range of 20-30% for turn-off stars depending on the treatment of objects for which C abundance is not determined, which is much higher than that in giants (~8%). Twelve Mg-poor stars ([Mg/Fe] < 0.0) have been identified in a wide metallicity range from [Fe/H] =-3.8 through -1.7. Twelve Eu-rich stars ([Eu/Fe]> 1.0) have been discovered in -3.4 <[Fe/H]< -2.0, enlarging the sample of r-process-enhanced stars with relatively high metallicity.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Jian Ge
Hui Zhang
Weicheng Zang
Hongping Deng
Shude Mao
Ji-Wei Xie
Hui-Gen Liu
Ji-Lin Zhou
Kevin Willis
Chelsea Huang
Steve B. Howell
Fabo Feng
Jiapeng Zhu
Xinyu Yao
Beibei Liu
Masataka Aizawa
Wei Zhu
Ya-Ping Li
Bo Ma
Quanzhi Ye
Abstract： We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Abstract： This work presents a first attempt to apply fuzzy cluster analysis (FCA) to analyzing stellar spectra. FCA is adopted to categorize line indices measured from LAMOST low-resolution spectra, and automatically remove the least metallicity-sensitive indices. The FCA-processed indices are then transferred to the artificial neural network (ANN) to derive metallicities for 147 very metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy. The FCA-ANN method could derive robust metallicities for VMP stars, with a precision of 0.2 dex compared with high-resolution analysis. The recommended FCA threshold value \lambda for this test is between 0.9965 and 0.9975. After reducing the dimension of the line indices through FCA, the derived metallicities are still robust, with no loss of accuracy, and the FCA-ANN method performs stably for different spectral quality from [Fe/H] from -1.8 down to -3.5. Compared with traditional classification methods, FCA considers ambiguity in groupings and noncontinuity of data, and is thus more suitable for observational data analysis. Though this early test uses FCA to analyze low-resolution spectra, and feeds the input to the ANN method to derive metallicities, FCA should be able to, in the large data era, also analyze slitless spectroscopy and multiband photometry, and prepare the input for methods not limited to ANN, in the field of stellar physics for other studies, e.g., stellar classification, identification of peculiar objects. The literature-collected high-resolution sample can help improve pipelines to derive stellar metallicities, and systematic offsets in metallicities for VMP stars for three published LAMOST catalogs have been discussed.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Hongliang Yan
Haining Li
Song Wang
Weikai Zong
Haibo Yuan
Maosheng Xiang
Yang Huang
Jiwei Xie
Subo Dong
Hailong Yuan
Shaolan Bi
Yaoquan Chu
Xiangqun Cui
Licai Deng
Jianning Fu
Zhanwen Han
Jinliang Hou
Guoping Li
Chao Liu
Jifeng Liu
Abstract： The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), also known as the Guoshoujing Telescope, is a major national scientific facility for astronomical research located in Xinglong, China. Beginning with a pilot survey in 2011, LAMOST has been surveying the night sky for more than 10 years. The LAMOST survey covers various objects in the Universe, from normal stars to peculiar ones, from the Milky Way to other galaxies, and from stellar black holes and their companions to quasars that ignite ancient galaxies. Until the latest data release 8, the LAMOST survey has released spectra for more than 10 million stars, ~220,000 galaxies, and ~71,000 quasars. With this largest celestial spectra database ever constructed, LAMOST has helped astronomers to deepen their understanding of the Universe, especially for our Milky Way galaxy and the millions of stars within it. In this article, we briefly review the characteristics, observations, and scientific achievements of LAMOST. In particular, we show how astrophysical knowledge about the Milky Way has been improved by LAMOST data.
Peer Review Status:Awaiting Review
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