We present the timing analysis of the nonlinear variability in two black hole low mass X-ray binaries MAXI J1820+070 and MAXI J1535-571 by using the bicoherence,a measure of phase coupling at different Fourier frequen...We present the timing analysis of the nonlinear variability in two black hole low mass X-ray binaries MAXI J1820+070 and MAXI J1535-571 by using the bicoherence,a measure of phase coupling at different Fourier frequencies.We found different patterns,e.g.,“cross”and“hypotenuse,”for LFQPOs in different outburst states.When they can be clearly distinguished,bicoherence patterns are similar over a wide energy range of 1–100 keV.It is intriguing that in some type-C QPOs we found the patterns that are normally observed in type-B QPOs.On the contrary,the“hypotenuse”pattern,a characteristic of type-C QPOs,was detected in a type-B QPO.This suggests that different types of QPOs may originate from similar underlying mechanisms.In addition,we speculate that the nonlinear variability may be a promising approach to disentangle distinct QPO models which assume different interactions between the broadband noise and QPO components.展开更多
We conduct a statistical analysis of the hardness ratio(HR)for bright sources in the 4 yr Galactic Plane Scanning Survey catalog of Insight-HXMT.Depending on the stable(variable)flux F_(s)(F_(v))or spectrum S_(s)(S_(v...We conduct a statistical analysis of the hardness ratio(HR)for bright sources in the 4 yr Galactic Plane Scanning Survey catalog of Insight-HXMT.Depending on the stable(variable)flux F_(s)(F_(v))or spectrum S_(s)(S_(v))of each source,the bright sources are classified into three groups:F_(v)&S_(v),F_(v)&S_(s),and F_(s)&_(s).Our study of the HR characteristics in different types of sources reveals that accretion-powered neutron star(NS)low-mass X-ray binaries(LMXBs)exhibit softer energy spectra than NS high-mass X-ray binaries(HMXBs),but harder energy spectra than black hole binaries in most cases.This difference is probably due to their different magnetic field strengths.Additionally,Fv&Sv LMXBs tend to be harder than Fv&Ss LMXBs below 7 keV,while the opposite is true for HMXBs.Our results suggest that LMXBs may dominate unclassified sources,and NS binaries are likely to be the primary type of X-ray binaries with ambiguous compact stars.By comparing the HR of transient sources in their outburst and low-flux states,it is found that the averaged HR of four sources in the two states are roughly comparable within uncertainties.We also investigate the spatial properties of the three groups and find that Fv&Sv sources are mainly located in the longitude of-20°<l<9°,Fv&Ss sources cross the Galactic Plane,and Fs&Ss sources are predominantly concentrated in 19°<l<42°.In addition,analyzing the HR spatial distributions shows the absorption of soft X-rays(primarily below 2 keV)in the Galactic Plane.展开更多
X-ray observations play a crucial role in time-domain astronomy.The Einstein Probe(EP),a recently launched X-ray astronomical satellite,emerges as a forefront player in the field of time-domain astronomy and high-ener...X-ray observations play a crucial role in time-domain astronomy.The Einstein Probe(EP),a recently launched X-ray astronomical satellite,emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics.With a focus on systematic surveys in the soft X-ray band,EP aims to discover high-energy transients and monitor variable sources in the universe.To achieve these objectives,a quick and reliable classification of observed sources is essential.In this study,we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy(LEIA)and EP-WXT simulations.The proposed Random Forest classifier,built on selected features derived from light curves,energy spectra,and location information,achieves an accuracy of approximately 95%on EP simulation data and 98%on LEIA observational data.The classifier is integrated into the LEIA data processing pipeline,serving as a tool for manual validation and rapid classification during observations.This paper presents an efficient method for the classification of X-ray sources based on single observations,along with implications of most effective features for the task.This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.展开更多
基金supported by the National Natural Science Foundation of China(NSFC,grant Nos.12173103 and U1738205)supported by the China Scholarship Council under No.202104910250based on observations with Insight-HXMT,a project funded by the China National Space Administration(CNSA)and the Chinese Academy of Sciences(CAS)。
文摘We present the timing analysis of the nonlinear variability in two black hole low mass X-ray binaries MAXI J1820+070 and MAXI J1535-571 by using the bicoherence,a measure of phase coupling at different Fourier frequencies.We found different patterns,e.g.,“cross”and“hypotenuse,”for LFQPOs in different outburst states.When they can be clearly distinguished,bicoherence patterns are similar over a wide energy range of 1–100 keV.It is intriguing that in some type-C QPOs we found the patterns that are normally observed in type-B QPOs.On the contrary,the“hypotenuse”pattern,a characteristic of type-C QPOs,was detected in a type-B QPO.This suggests that different types of QPOs may originate from similar underlying mechanisms.In addition,we speculate that the nonlinear variability may be a promising approach to disentangle distinct QPO models which assume different interactions between the broadband noise and QPO components.
基金the support from the National Natural Science Foundation of China under grant Nos.12333007,U1838202,U1838201,U1838107,U1838113,U1838113 and U2038102the Youth Innovation Promotion Association of the CAS(grant id 2018014)+1 种基金the National Key R&D Program of China(grant No.2021YFA0718500)partially supported by the International Partnership Program of the Chinese Academy of Sciences(grant No.113111KYSB20190020)。
文摘We conduct a statistical analysis of the hardness ratio(HR)for bright sources in the 4 yr Galactic Plane Scanning Survey catalog of Insight-HXMT.Depending on the stable(variable)flux F_(s)(F_(v))or spectrum S_(s)(S_(v))of each source,the bright sources are classified into three groups:F_(v)&S_(v),F_(v)&S_(s),and F_(s)&_(s).Our study of the HR characteristics in different types of sources reveals that accretion-powered neutron star(NS)low-mass X-ray binaries(LMXBs)exhibit softer energy spectra than NS high-mass X-ray binaries(HMXBs),but harder energy spectra than black hole binaries in most cases.This difference is probably due to their different magnetic field strengths.Additionally,Fv&Sv LMXBs tend to be harder than Fv&Ss LMXBs below 7 keV,while the opposite is true for HMXBs.Our results suggest that LMXBs may dominate unclassified sources,and NS binaries are likely to be the primary type of X-ray binaries with ambiguous compact stars.By comparing the HR of transient sources in their outburst and low-flux states,it is found that the averaged HR of four sources in the two states are roughly comparable within uncertainties.We also investigate the spatial properties of the three groups and find that Fv&Sv sources are mainly located in the longitude of-20°<l<9°,Fv&Ss sources cross the Galactic Plane,and Fs&Ss sources are predominantly concentrated in 19°<l<42°.In addition,analyzing the HR spatial distributions shows the absorption of soft X-rays(primarily below 2 keV)in the Galactic Plane.
基金supported by the National Key Research and Development Program of China(2022YFF0711500)National Natural Science Foundation of China(NSFC,grant Nos.12373110,12273077,12103070,and 12333004)+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant Nos.XDA15310300,XDB0550200,XDB0550100,and XDB0550000)supported by China National Astronomical Data Center(NADC)Chinese Virtual Observatory(China-VO)supported by Astronomical Big Data Joint Research Center,cofounded by National Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud。
文摘X-ray observations play a crucial role in time-domain astronomy.The Einstein Probe(EP),a recently launched X-ray astronomical satellite,emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics.With a focus on systematic surveys in the soft X-ray band,EP aims to discover high-energy transients and monitor variable sources in the universe.To achieve these objectives,a quick and reliable classification of observed sources is essential.In this study,we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy(LEIA)and EP-WXT simulations.The proposed Random Forest classifier,built on selected features derived from light curves,energy spectra,and location information,achieves an accuracy of approximately 95%on EP simulation data and 98%on LEIA observational data.The classifier is integrated into the LEIA data processing pipeline,serving as a tool for manual validation and rapid classification during observations.This paper presents an efficient method for the classification of X-ray sources based on single observations,along with implications of most effective features for the task.This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.
