在天体物理研究中,准确扣除星际消光与红化的影响对于光学和近红外观测至关重要.恒星的星际红化信息是揭示其内秉性质的关键.中国空间站望远镜(Chinese Space Station Telescope,CSST)的光学巡天项目将为科学家提供海量的恒星无缝光谱数...在天体物理研究中,准确扣除星际消光与红化的影响对于光学和近红外观测至关重要.恒星的星际红化信息是揭示其内秉性质的关键.中国空间站望远镜(Chinese Space Station Telescope,CSST)的光学巡天项目将为科学家提供海量的恒星无缝光谱数据,而基于这些数据测量恒星的红化信息,对于进一步测定恒星参数和理解银河系的性质具有重要意义.提出了一种基于随机森林回归的机器学习方法,该方法以光谱的归一化流量为输入参数来训练恒星的内秉颜色,旨在精确估计CSST低分辨率光谱中的恒星红化值.利用下一代恒星光谱库(Next Generation Stellar Spectral Library,NGSL)模拟CSST低分辨率光谱,并预测了所提方法的精度,同时探讨了不同波段和有效温度对结果精度的影响.基于CSST不同波段的无缝光谱所得到的恒星红化值E((g-i)g i、分别为g、i波段星等)与真实值的比较结果显示,在光谱信噪比为100时,GU波段的平均误差为0.0005 mag,标准差为0.0272 mag;GV波段的平均误差为0.0008 mag,标准差为0.0286 mag;GI波段的平均误差为0.0008 mag,标准差为0.0271 mag;全波段的平均误差为0.0003 mag,标准差为0.0252 mag.此方法作为CSST科学预研究的一部分,未来可直接应用于CSST数据,为CSST的科学研究提供基础支持.展开更多
We present a spectroscopic and photometric study of HIP 12653 to investigate its magnetic cycle and differential rotation.Using HARPS archival spectra matched with MARCS-AMBRE theoretical templates,we derive the stell...We present a spectroscopic and photometric study of HIP 12653 to investigate its magnetic cycle and differential rotation.Using HARPS archival spectra matched with MARCS-AMBRE theoretical templates,we derive the stellar parameters(Teff,logg,FeH,and vsini)of the target.The S-index,an activity indicator based on the emission of the CaⅡH&K lines,is fitted to determine the magnetic cycle and rotation periods.We refine the magnetic cycle period to 5799.20±0.88 days and suggest the existence of a secondary,shorter cycle of674.6922±0.0098 days,making HIP 12653 the youngest star known to exhibit such a short activity cycle.During the minimum activity phase,a rotation period of 4.8 days is estimated.This is notably different from the 7 day period obtained when measurements during minimum activity are excluded,suggesting that these two periods are rotation periods at different latitudes.To explore this hypothesis,we introduce a novel light curve fitting method that incorporates multiple harmonics to model different spot configurations.Applied to synthetic light curves,the method recovers at least two rotation periods close to the true input values(within three times their uncertainties)in 92.1%of cases.The inferred rotation shear shows a median deviation of 0.0011±0.0003 and a standard deviation of 0.0177±0.0002 from the true value.Applying this approach to TESS photometric data from 2018 to2023,we detect three distinct rotation periods—4.8 days,5.7 days,and 7.7 days,(along with a signal at 3.75 days interpreted as its first harmonic)—consistent with spots located at different latitudes.Assuming a solar-like differential rotation,we estimate an inclination of 34.0°±1.8°and a rotational shear ofα=0.38±0.01.These results confirm the 4.8 day period and demonstrate that differential rotation can be constrained by tracking rotation period changes across different phases of the magnetic cycle.展开更多
Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra.However,these methods are sensitive to noise and unsuitable for ...Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra.However,these methods are sensitive to noise and unsuitable for ultra-low-resolution data.Given that the Chinese Space Station Telescope(CSST)will acquire large volumes of ultra-low-resolution spectra,developing effective methods for ultra-low-resolution spectral analysis is crucial.In this work,we investigated the Fully Connected Residual Network(FCResNet)for simultaneously estimating atmospheric parameters(T_(eff),log g,[Fe/H])and elemental abundances([C/Fe],[N/Fe],[Mg/Fe]).We trained and evaluated FCResNet using CSST-like spectra(R~200)generated by degrading LAMOST spectra(R~1800),with reference labels from APOGEE.FCResNet significantly outperforms traditional machine learning methods(KNN,XGBoost,S VR)and CNN in prediction precision.For spectra with the g-band signal-tonoise ratio greater than 20,FCResNet achieves precisions of 78 K,0.15 dex,0.08 dex,0.05 dex,0.10 dex,and0.05 dex for T_(eff),log g,[Fe/H],[C/Fe],[N/Fe]and[Mg/Fe],respectively,on the test set.FCResNet processes one million spectra in only 42 s while maintaining a simple architecture with only 348 KB model size.These results suggest that FCResNet is a practical and promising tool for processing the large volume of ultra-lowresolution spectra that will be obtained by CSST in the future.展开更多
利用疏散星团NGC 188所在天区的1046颗恒星样本的高精度3维(3D)运动学数据(自行和视向速度)测试了DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法的成员判定效果.为了避免自行和视向速度的单位不一致带...利用疏散星团NGC 188所在天区的1046颗恒星样本的高精度3维(3D)运动学数据(自行和视向速度)测试了DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法的成员判定效果.为了避免自行和视向速度的单位不一致带来的影响,在数据预处理阶段将3个分量的数据统一标准化至[0,1]区间.利用第k个最近邻点距离方法分析了1046颗恒星样本在标准化无量纲3D速度空间的分布特征,再根据第k个最近邻点距离随k值的变化趋势确定了DBSCAN聚类算法的输入参数(Eps,MinPts),最后利用DBSCAN聚类算法分离出497颗3D运动学成员星.分析结果表明得到的3D运动学成员星是可靠的.展开更多
Compared to high-resolution spectra,low-resolution spectra offer higher observational efficiency and broader sky coverage,making them especially valuable for large-scale stellar surveys.The Large Sky Area Multi-Object...Compared to high-resolution spectra,low-resolution spectra offer higher observational efficiency and broader sky coverage,making them especially valuable for large-scale stellar surveys.The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)survey alone has collected tens of millions of low-resolution stellar spectra,providing an unprecedented opportunity for large-scale stellar parameter estimation.However,a substantial portion of these spectra suffer from low signal-to-noise ratio(low-SNR),which poses significant challenges for accurate parameter determination.Accurately extracting stellar atmospheric parameters from such data can significantly enhance the utility of spectral observations.However,these low-SNR spectra often introduce considerable uncertainty in parameter estimation.To address this issue,we propose a novel method based on the Cycle-Consistent Convolutional Neural Network(Cycle-CNN)for predicting key stellar atmospheric parameters,including effective temperature(T_(eff)),surface gravity(log g),and metallicity([Fe/H]).This method integrates the cycle-consistency learning mechanism of Cycle-GAN with the strong modeling capability of CNNs,thereby improving model robustness and reducing prediction uncertainty under low-SNR conditions.We train and evaluate the model on spectra from LAMOST DR9 across different SNR intervals(2-15).For spectra with SNR between 10 and 15,the model achieves prediction accuracies of 63.22 K for T_(eff),0.11 dex for log g,and 0.07 dex for[Fe/H].For the spectra with SNR between 5 and 10,the prediction accuracies are 89.45 K,0.17 dex,and 0.11 dex,respectively.Even under extreme conditions with SNR between 2 and 5 and limited data availability,the model maintains good performance,achieving accuracies of 145.36 K,0.29 dex,and 0.18 dex.Furthermore,we validate our predictions against reference parameters from high-resolution surveys,and the results demonstrate good consistency with other large-scale spectroscopic surveys.These findings indicate that the proposed Cycle-CNN method can provide stable and accurate predictions of atmospheric parameters even under low-quality spectral conditions,offering a reliable solution to improve the scientific utilization of low-quality spectra.展开更多
SS 433是目前为止唯一一个被同时检测到轨道周期、超轨道周期和章动周期且存在双向螺旋状喷流的X射线双星系统,通过研究它的X射线光变将有助于理解系统的动力学过程及与其他波段的相关性.利用Lomb-Scargle周期图法(简称LS周期图)和加权...SS 433是目前为止唯一一个被同时检测到轨道周期、超轨道周期和章动周期且存在双向螺旋状喷流的X射线双星系统,通过研究它的X射线光变将有助于理解系统的动力学过程及与其他波段的相关性.利用Lomb-Scargle周期图法(简称LS周期图)和加权小波Z变换法(Weighted Wavelet Z-transform,WWZ)对SS 433的Swift/BAT(Burst Alert Telescope)(15–50 ke V)和RXTE/ASM(Rossi X-Ray Timing Explorer/All-Sky Monitor)(1.5–3,3–5和5–12 ke V)光变曲线进行周期提取,并对得到的周期成分进行蒙特卡洛仿真.其中15–50 ke V能段:检测到5个较强的周期成分P_1(～6.29 d)、P_2(～6.54 d)、P_3(～13.08 d)、P_4(～81.50 d)和P_5(～162.30 d);3–5和5–12 ke V能段:都检测到P_3(～13d)和P_5(～162 d)的周期成分;1.5–3 ke V能段:未检测到任何明显的周期存在.3–5、5–12和15–50 ke V能段的功率谱上最强的周期信号均为P_5,且P_5与之前对光学光变曲线研究得到的结果一致,结合SS 433的螺旋形射电喷流,推测周期为～162 d的X射线和光学波段光变与相对论性喷流的进动有关,X射线与光学光变周期的一致性也表明两个波段的辐射机制有内秉联系.P_3与之前研究中检测到的系统轨道周期(～13.07 d)一致,P_2和P_4则分别为P_3和P_5的一个高频谐波成分.P_1成分仅在15–50 ke V能段的功率谱中被检测到,且它与系统的章动周期一致.随着能段能量的降低(硬X射线到软X射线),所检测到的周期成分却越来越少,这一结果很好地印证了高能段(硬X射线)辐射主要来自于喷流,低能段(软X射线)辐射则可能是由双星系统周围的介质主导.通过分析得到的多个X射线光变周期,为今后SS 433的多波数据分析、系统的动力学机制等研究提供有力的参考依据.展开更多
基金Funding for the TESS mission is provided by the NASA Explorer Program。
文摘We present a spectroscopic and photometric study of HIP 12653 to investigate its magnetic cycle and differential rotation.Using HARPS archival spectra matched with MARCS-AMBRE theoretical templates,we derive the stellar parameters(Teff,logg,FeH,and vsini)of the target.The S-index,an activity indicator based on the emission of the CaⅡH&K lines,is fitted to determine the magnetic cycle and rotation periods.We refine the magnetic cycle period to 5799.20±0.88 days and suggest the existence of a secondary,shorter cycle of674.6922±0.0098 days,making HIP 12653 the youngest star known to exhibit such a short activity cycle.During the minimum activity phase,a rotation period of 4.8 days is estimated.This is notably different from the 7 day period obtained when measurements during minimum activity are excluded,suggesting that these two periods are rotation periods at different latitudes.To explore this hypothesis,we introduce a novel light curve fitting method that incorporates multiple harmonics to model different spot configurations.Applied to synthetic light curves,the method recovers at least two rotation periods close to the true input values(within three times their uncertainties)in 92.1%of cases.The inferred rotation shear shows a median deviation of 0.0011±0.0003 and a standard deviation of 0.0177±0.0002 from the true value.Applying this approach to TESS photometric data from 2018 to2023,we detect three distinct rotation periods—4.8 days,5.7 days,and 7.7 days,(along with a signal at 3.75 days interpreted as its first harmonic)—consistent with spots located at different latitudes.Assuming a solar-like differential rotation,we estimate an inclination of 34.0°±1.8°and a rotational shear ofα=0.38±0.01.These results confirm the 4.8 day period and demonstrate that differential rotation can be constrained by tracking rotation period changes across different phases of the magnetic cycle.
基金supported by the National Astronomical Observatories of Chinese Academy of Sciences(No.E4ZR0516)the National Natural Science Foundation of China(12273078,12273075 and 12411530071)+2 种基金support from Royal Society IECNSFC233140 exchange grantFunding for the Project has been provided by the National Development and Reform CommissionFunding for the Sloan Digital Sky Survey IV has been provided by the Alfred P.Sloan Foundation,the U.S.Department of Energy Office of Science,and the Participating Institutions。
文摘Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra.However,these methods are sensitive to noise and unsuitable for ultra-low-resolution data.Given that the Chinese Space Station Telescope(CSST)will acquire large volumes of ultra-low-resolution spectra,developing effective methods for ultra-low-resolution spectral analysis is crucial.In this work,we investigated the Fully Connected Residual Network(FCResNet)for simultaneously estimating atmospheric parameters(T_(eff),log g,[Fe/H])and elemental abundances([C/Fe],[N/Fe],[Mg/Fe]).We trained and evaluated FCResNet using CSST-like spectra(R~200)generated by degrading LAMOST spectra(R~1800),with reference labels from APOGEE.FCResNet significantly outperforms traditional machine learning methods(KNN,XGBoost,S VR)and CNN in prediction precision.For spectra with the g-band signal-tonoise ratio greater than 20,FCResNet achieves precisions of 78 K,0.15 dex,0.08 dex,0.05 dex,0.10 dex,and0.05 dex for T_(eff),log g,[Fe/H],[C/Fe],[N/Fe]and[Mg/Fe],respectively,on the test set.FCResNet processes one million spectra in only 42 s while maintaining a simple architecture with only 348 KB model size.These results suggest that FCResNet is a practical and promising tool for processing the large volume of ultra-lowresolution spectra that will be obtained by CSST in the future.
文摘利用疏散星团NGC 188所在天区的1046颗恒星样本的高精度3维(3D)运动学数据(自行和视向速度)测试了DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法的成员判定效果.为了避免自行和视向速度的单位不一致带来的影响,在数据预处理阶段将3个分量的数据统一标准化至[0,1]区间.利用第k个最近邻点距离方法分析了1046颗恒星样本在标准化无量纲3D速度空间的分布特征,再根据第k个最近邻点距离随k值的变化趋势确定了DBSCAN聚类算法的输入参数(Eps,MinPts),最后利用DBSCAN聚类算法分离出497颗3D运动学成员星.分析结果表明得到的3D运动学成员星是可靠的.
基金supported by the Natural Science Foundation of Shandong Province(Nos.ZR2022MA076 and ZR2024MA063)the National Natural Science Foundation of China(NSFC,grant Nos.11873037,U1931209,11803016)+2 种基金the science research grants from the China Manned Space Project with No.CMSCSST-2021-B05 and CMS-CSST-2021-A08supported by the Doctoral Research Foundation of Shandong Technology and Business University(grant No.306519)the Young Scholars Program of Shandong University,Weihai(2016WHWLJH09)。
文摘Compared to high-resolution spectra,low-resolution spectra offer higher observational efficiency and broader sky coverage,making them especially valuable for large-scale stellar surveys.The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)survey alone has collected tens of millions of low-resolution stellar spectra,providing an unprecedented opportunity for large-scale stellar parameter estimation.However,a substantial portion of these spectra suffer from low signal-to-noise ratio(low-SNR),which poses significant challenges for accurate parameter determination.Accurately extracting stellar atmospheric parameters from such data can significantly enhance the utility of spectral observations.However,these low-SNR spectra often introduce considerable uncertainty in parameter estimation.To address this issue,we propose a novel method based on the Cycle-Consistent Convolutional Neural Network(Cycle-CNN)for predicting key stellar atmospheric parameters,including effective temperature(T_(eff)),surface gravity(log g),and metallicity([Fe/H]).This method integrates the cycle-consistency learning mechanism of Cycle-GAN with the strong modeling capability of CNNs,thereby improving model robustness and reducing prediction uncertainty under low-SNR conditions.We train and evaluate the model on spectra from LAMOST DR9 across different SNR intervals(2-15).For spectra with SNR between 10 and 15,the model achieves prediction accuracies of 63.22 K for T_(eff),0.11 dex for log g,and 0.07 dex for[Fe/H].For the spectra with SNR between 5 and 10,the prediction accuracies are 89.45 K,0.17 dex,and 0.11 dex,respectively.Even under extreme conditions with SNR between 2 and 5 and limited data availability,the model maintains good performance,achieving accuracies of 145.36 K,0.29 dex,and 0.18 dex.Furthermore,we validate our predictions against reference parameters from high-resolution surveys,and the results demonstrate good consistency with other large-scale spectroscopic surveys.These findings indicate that the proposed Cycle-CNN method can provide stable and accurate predictions of atmospheric parameters even under low-quality spectral conditions,offering a reliable solution to improve the scientific utilization of low-quality spectra.
文摘SS 433是目前为止唯一一个被同时检测到轨道周期、超轨道周期和章动周期且存在双向螺旋状喷流的X射线双星系统,通过研究它的X射线光变将有助于理解系统的动力学过程及与其他波段的相关性.利用Lomb-Scargle周期图法(简称LS周期图)和加权小波Z变换法(Weighted Wavelet Z-transform,WWZ)对SS 433的Swift/BAT(Burst Alert Telescope)(15–50 ke V)和RXTE/ASM(Rossi X-Ray Timing Explorer/All-Sky Monitor)(1.5–3,3–5和5–12 ke V)光变曲线进行周期提取,并对得到的周期成分进行蒙特卡洛仿真.其中15–50 ke V能段:检测到5个较强的周期成分P_1(～6.29 d)、P_2(～6.54 d)、P_3(～13.08 d)、P_4(～81.50 d)和P_5(～162.30 d);3–5和5–12 ke V能段:都检测到P_3(～13d)和P_5(～162 d)的周期成分;1.5–3 ke V能段:未检测到任何明显的周期存在.3–5、5–12和15–50 ke V能段的功率谱上最强的周期信号均为P_5,且P_5与之前对光学光变曲线研究得到的结果一致,结合SS 433的螺旋形射电喷流,推测周期为～162 d的X射线和光学波段光变与相对论性喷流的进动有关,X射线与光学光变周期的一致性也表明两个波段的辐射机制有内秉联系.P_3与之前研究中检测到的系统轨道周期(～13.07 d)一致,P_2和P_4则分别为P_3和P_5的一个高频谐波成分.P_1成分仅在15–50 ke V能段的功率谱中被检测到,且它与系统的章动周期一致.随着能段能量的降低(硬X射线到软X射线),所检测到的周期成分却越来越少,这一结果很好地印证了高能段(硬X射线)辐射主要来自于喷流,低能段(软X射线)辐射则可能是由双星系统周围的介质主导.通过分析得到的多个X射线光变周期,为今后SS 433的多波数据分析、系统的动力学机制等研究提供有力的参考依据.
