分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Hot subdwarf star is a particular type of star that is crucial for studying binary evolution and atmospheric diffusion processes. In recent years, identifying Hot subdwarfs by machine learning methods has become a hot topic, but there are still limitations in automation and accuracy. In this paper, we proposed a robust identification method based on the convolutional neural network (CNN). We first constructed the dataset using the spectral data of LAMOS DR7-V1. We then constructed a hybrid recognition model including an 8-class classification model and a binary classification model. The model achieved an accuracy of 96.17% on the testing set. To further validate the accuracy of the model, we selected 835 Hot subdwarfs that were not involved in the training process from the identified LAMOST catalog (2428, including repeated observations) as the validation set. An accuracy of 96.05% was achieved. On this basis, we used the model to filter and classify all 10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 Hot subdwarf candidates, of which 2067 have been confirmed. We found 25 new Hot subdwarfs among the remaining candidates by manual validation. The overall accuracy of the model is 87.42%. Overall, the model presented in this study can effectively identify specific spectra with robust results and high accuracy, and can be further applied to the classification of large-scale spectra and the search of specific targets.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
YanJun Guo
Bo Zhang
Chao Liu
Jiao Li
JiangDan Li
LuQian Wang
ZhiCun Liu
YongHui Hou
ZhanWen Han
XueFei Chen
摘要： Massive stars play key roles in many astrophysical processes. Deriving atmospheric parameters of massive stars is important to understand their physical properties and thus are key inputs to trace their evolution. Here we report our work on adopting the data-driven technique Stellar LAbel Machine ({\tt SLAM}) with the non-LTE TLUSTY synthetic spectra as the training dataset to estimate the stellar parameters of LAMOST optical spectra for early-type stars. We apply two consistency tests to verify this machine learning method and compare stellar labels given by {\tt SLAM} with that in literature for several objects having high-resolution spectra. We provide the stellar labels of effective temperature ($T_\mathrm{eff}$), surface gravity ($\log{g}$), metallicity ([M/H]), and projected rotational velocity ($v\sin{i}$) for 3,931 and 578 early-type stars from LAMOST Low-Resolution Survey (LAMOST-LRS) and Medium-Resolution Survey (LAMOST-MRS), respectively. To estimate the average statistical uncertainties of our results, we calculated the standard deviation between the predicted stellar label and the pre-labeled published values from the high-resolution spectra. The uncertainties of the four parameters are $\sigma(T_\mathrm{eff}) = 2,185 $K, $\sigma(\log{g}) = 0.29$ dex, and $\sigma(v\sin{i}) = 11\, \rm km\,s^{-1}$ for MRS, and $\sigma(T_\mathrm{eff}) = 1,642 $K, $\sigma(\log{g}) = 0.25$ dex, and $\sigma(v\sin{i}) = 42\, \rm km\,s^{-1}$ for LRS spectra, respectively. We notice that parameters of $T_\mathrm{eff}$, $\log{g}$ and [M/H] can be better constrained using LRS spectra rather than using MRS spectra, most likely due to their broad wavelength coverage, while $v\sin{i}$ is constrained better by MRS spectra than by LRS spectra, probably due to the relatively accurate line profiles of MRS spectra.
点击量
待评议
点击量
下载量
评论
