Spectral classification plays a crucial role in the analysis of astronomical data.Currently,stellar spectral classification primarily relies on one-dimensional(1D)spectra and necessitates a sufficient signal-to-noise ...Spectral classification plays a crucial role in the analysis of astronomical data.Currently,stellar spectral classification primarily relies on one-dimensional(1D)spectra and necessitates a sufficient signal-to-noise ratio(S/N).However,in cases where the S/N is low,obtaining valuable information becomes impractical.In this paper,we propose a novel model called DRC-Net(Double-branch celestial spectral classification network based on residual mechanisms)for stellar classification,which operates solely on two-dimensional(2D)spectra.The model consists of two branches that use 1D convolutions to reduce the dimensionality of the 2D spectral composed of both blue and red arms.In the following,the features extracted from both branches are fused,and the fused result undergoes further feature extraction before being fed into the classifier for final output generation.The data set is from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,comprising 15,680 spectra of F,G,and K types.The preprocessing process includes normalization and the early stopping mechanism.The experimental results demonstrate that the proposed DRC-Net achieved remarkable classification precision of 93.0%,83.5%,and86.9%for F,G,and K types,respectively,surpassing the performance of 1D spectral classification methods.Furthermore,different S/N intervals are tested to judge the classification ability of DRC-Net.The results reveal that DRC-Net,as a 2D spectral classification model,can deliver superior classification outcomes for the spectra with low S/Ns.These experimental findings not only validate the efficiency of DRC-Net but also confirm the enhanced noise resistance ability exhibited by 2D spectra.展开更多
In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking ...In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.展开更多
基金supported by the Natural Science Foundation of Tianjin Municipality(22JCYBJC00410)the National Natural Science Foundation of China-Chinese Academy of Sciences Joint Fund for Astronomy(U1931134)。
文摘Spectral classification plays a crucial role in the analysis of astronomical data.Currently,stellar spectral classification primarily relies on one-dimensional(1D)spectra and necessitates a sufficient signal-to-noise ratio(S/N).However,in cases where the S/N is low,obtaining valuable information becomes impractical.In this paper,we propose a novel model called DRC-Net(Double-branch celestial spectral classification network based on residual mechanisms)for stellar classification,which operates solely on two-dimensional(2D)spectra.The model consists of two branches that use 1D convolutions to reduce the dimensionality of the 2D spectral composed of both blue and red arms.In the following,the features extracted from both branches are fused,and the fused result undergoes further feature extraction before being fed into the classifier for final output generation.The data set is from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,comprising 15,680 spectra of F,G,and K types.The preprocessing process includes normalization and the early stopping mechanism.The experimental results demonstrate that the proposed DRC-Net achieved remarkable classification precision of 93.0%,83.5%,and86.9%for F,G,and K types,respectively,surpassing the performance of 1D spectral classification methods.Furthermore,different S/N intervals are tested to judge the classification ability of DRC-Net.The results reveal that DRC-Net,as a 2D spectral classification model,can deliver superior classification outcomes for the spectra with low S/Ns.These experimental findings not only validate the efficiency of DRC-Net but also confirm the enhanced noise resistance ability exhibited by 2D spectra.
基金supported by the NSF of China(Nos.11971208,11601197)the NSSF of China(Grant No.21&ZD152)+2 种基金the China Postdoctoral Science Foundation(Nos.2016M600511,2017T100475)the NSF of Jiangxi Province(Nos.2018ACB21002,20171ACB21030)the Post graduate Innovation Project of Jiangxi Province(No.YC2021CB124)。
文摘In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.