Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely a...Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely applicable method for identifying groups through machine learning techniques in real space,taking into account the impact of redshift distortion.Our methodology involves two neural networks:one is a classification model for identifying central galaxy groups,and the other is a regression model for predicting the mass of these groups.Both models input observable galaxy traits,allowing future applicability to real survey data.Testing on simulated datasets indicates our method accurately identifies over 92%of groups with M_(vir)≥10^(11) h^(−1)M_(⊙),with 80%achieving a membership completeness of at least 80%.The predicted group masses vary by less than 0.3 dex across different mass scales,even in the absence of a priori data.Our network adapts seamlessly to expand to sparse samples with a flux limit of mr<14,to high redshift samples at z=1.08,and to galaxy samples from the TNG300 hydrodynamical simulation without further training.Furthermore,the framework can easily adjust to real surveys by training on redshift-distorted samples without needing parameter changes.Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.展开更多
In our previous work,we identified~100,000 metal-poor stars([Fe/H]<-1.0)from the LAMOST Survey.This work estimates their chemical abundances and explores the origin and evolution of the Galactic metal-poor disk.Our...In our previous work,we identified~100,000 metal-poor stars([Fe/H]<-1.0)from the LAMOST Survey.This work estimates their chemical abundances and explores the origin and evolution of the Galactic metal-poor disk.Our chemo-dynamical analysis reveals four main populations within the metal-poor disk:(1)a primordial disk older than 12 Gyr with[Fe/H]>-1.5;(2)debris stars from the progenitor galaxy of Gaia±Sausage±Enceladus(GSE),but now residing in the Galactic disk;(3)the metal-poor tail of the metal-rich,high-αdisk formed10±12 Gyr ago,with metallicity lower limit extending to-2.0;(4)the metal-poor tail of the metal-rich,low-αdisk younger than 8 Gyr,reaching a lower metallicity limit of-1.8.These results reveal the presence of a primordial disk and show that both high-αand low-αdisks reach lower metallicities than previously thought.Analysis of merger debris reveals that Wukong,with extremely low metallicity,likely originates from merger events distinct from GSE.Additionally,three new substructures are identified:ShangGu-1,characterized by unusual[Fe/H]-eccentricity correlations;ShangGu-2,possibly heated disk stars;and ShangGu-3,which can be divided into four subgroups based on differing orbital directions,with two aligning with the previously known Nyx and Nyx-2.展开更多
Based on the collected multiwavelength data, namely in the radio(NVSS, FIRST, RATAN-600), IR(WISE),optical(Pan-STARRS), UV(GALEX), and X-ray(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blaza...Based on the collected multiwavelength data, namely in the radio(NVSS, FIRST, RATAN-600), IR(WISE),optical(Pan-STARRS), UV(GALEX), and X-ray(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen's self-organizing maps(SOMs), we have constructed an independent classification of the blazars(five classes) and compared the classes with the known Roma-BZCAT classification(FSRQs, BL Lacs, galaxy-dominated BL Lacs, and blazars of an uncertain type) as well as with the high synchrotron peaked(HSP) blazars from the 3HSP catalog and blazars from the TeVCat catalog. The obtained groups demonstrate concordance with the BL Lac/FSRQ classification along with a continuous character of the change in the properties. The group of HSP blazars stands out against the overall distribution. We examine the characteristics of the five groups and demonstrate distinctions in their spectral energy distribution shapes. The effectiveness of the clustering technique for objective analysis of multiparametric arrays of experimental data is demonstrated.展开更多
The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies.Defining strategies to quickly and efficiently extract useful physical information from this data is mandato...The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies.Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates(SFRs) are the most fundamental. We develop a novel tool, Multi-Layer Perceptron for Predicting Galaxy Parameters(MLP-GaP), that uses a machine learning(ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multiband catalogs. We first adopt a mock data set generated by the Code Investigating GALaxy Emission(CIGALE) for training and testing data sets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training data set. The results of the test performed on the mock data set show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from spectral energy distribution fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.展开更多
We analyze the galaxy pairs in a set of volume limited samples from the Sloan Digital Sky Survey to study the effects of minor interactions on the star formation rate(SFR)and color of galaxies.We carefully design cont...We analyze the galaxy pairs in a set of volume limited samples from the Sloan Digital Sky Survey to study the effects of minor interactions on the star formation rate(SFR)and color of galaxies.We carefully design control samples of isolated galaxies by matching the stellar mass and redshift of the minor pairs.The SFR distributions and color distributions in the minor pairs differ from their controls at>99%significance level.We also simultaneously match the control galaxies in stellar mass,redshift and local density to assess the role of the environment.The null hypothesis can be rejected at>99%confidence level even after matching the environment.Our analysis shows a quenching in the minor pairs where the degree of quenching decreases with the increasing pair separation and plateaus beyond 50 kpc.We also prepare a sample of minor pairs with Hαline information.We calculate the SFR of these galaxies using the Hαline and repeat our analysis.We observe a quenching in the Hαsample too.We find that the majority of the minor pairs are quiescent systems that could be quenched due to minor interactions.Combining data from the Galaxy Zoo and Galaxy Zoo 2,we find that only∼1%galaxies have a dominant bulge,4%–7%galaxies host a bar and 5%–10%of galaxies show active galactic nucleus(AGN)activity in minor pairs.This indicates that the presence of bulge,bar or AGN activity plays an insignificant role in quenching the galaxies in minor pairs.The more massive companion satisfies the criteria for mass quenching in most of the minor pairs.We propose that the stripping and starvation likely caused the quenching in the less massive companion at a later stage of evolution.展开更多
From the Owens Valley Radio Observatory 40 m radio telescope,we have collected the light curves of the 15 GHz radio band for FSRQ J0153-1153,spanning from 2009 February to 2018 February.The Lomb-Scargle Periodogram me...From the Owens Valley Radio Observatory 40 m radio telescope,we have collected the light curves of the 15 GHz radio band for FSRQ J0153-1153,spanning from 2009 February to 2018 February.The Lomb-Scargle Periodogram method and the Weighted Wavelet Z-transform method are employed to search for the quasi-periodic oscillation(QPO)signal of these data,and the simulation method for the light curve is utilized to estimate the significance level of this QPO signal;thus through these techniques,the QPO signal of 3.7±0.5 yr with a significance level of 3.68σis revealed for the first time.It is most likely an explanation for the QPO signal that a binary black hole system gives rise to a Newtonian-driven the precession of jet.Based on this assumption,we find that the mass of the secondary black hole in this system may be larger than the mass of the primary black hole;and we estimate the intrinsic QPO of jet precession and the QPO of companion star orbit.展开更多
Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments ...Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions.We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments.The star formation rate(SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets.The analysis is repeated for three volume limited samples covering different magnitude ranges.The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of~50 kpc.We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc.Some earlier studies report that the galaxy pairs align with the filament axis.Such alignment inside filaments indicates anisotropic accretion that may cause these differences.We do not observe these trends in the brighter galaxy samples.The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments.The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.展开更多
Long-term spectroscopic monitoring campaigns on active galactic nuclei(AGNs)provide a wealth of information about its interior structure and kinematics.However,a number of the observations suffer from the contaminatio...Long-term spectroscopic monitoring campaigns on active galactic nuclei(AGNs)provide a wealth of information about its interior structure and kinematics.However,a number of the observations suffer from the contamination of second-order spectra(SOS)which will introduce some undesirable uncertainties at the red side of the spectra.In this paper,we test the effect of SOS and propose a method to correct it in the time domain spectroscopic data using the simultaneously observed comparison stars.Based on the reverberation mapping(RM)data of NGC 5548 in2019,one of the most intensively monitored AGNs by the Lijiang 2.4 m telescope,we find that the scientific object,comparison star,and spectrophotometric standard star can jointly introduce up to~30%SOS for Grism 14.This irregular but smooth SOS significantly affects the flux density and profile of the emission line,while having little effect on the light curve.After applying our method to each spectrum,we find that the SOS can be corrected effectively.The deviation between corrected and intrinsic spectra is~2%,and the impact of SOS on time lag is very minor.This method makes it possible to obtain the HαRM measurements from archival data provided that the spectral shape of the AGN under investigation does not have a large change.展开更多
The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that mac...The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that machine learning can recover the relation between the uncertainties of different parameters,especially,as predicted by the error propagation formula.Gravitational lensing can be used to probe both astrophysics and cosmology.As a practical application,we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters(effective lens mass ML and Einstein radiusθ_(E))in accordance with the theoretical formula for the singular isothermal ellipse(SIE)lens model.The relation of errors of lens mass and Einstein radius,(e.g.,the ratio of standard deviations F=σ_(ML)/σ_(θ_(E)))predicted by the deep convolution neural network are consistent with the error propagation formula of the SIE lens model.As a proof-of-principle test,a toy model of linear relation with Gaussian noise is presented.We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise.Error propagation plays a crucial role in identifying the physical relation among parameters,rather than a coincidence relation,therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters.展开更多
The Chandra Galactic Center Survey detected -800 X-ray point-like sources in the 2°× 0.8° sky region around the Galactic Center. We study the spatial and luminosity distributions of these sources accord...The Chandra Galactic Center Survey detected -800 X-ray point-like sources in the 2°× 0.8° sky region around the Galactic Center. We study the spatial and luminosity distributions of these sources according to their spectral properties. Fourteen bright sources detected are used to fit jointly an absorbed power-law model, from which the power-law photon index is determined to be -2.5. Assuming that all other sources have the same power-law form, the relation between hardness ratio and HI column density NH is used to estimate the NH values for all sources. Monte Carlo simulations show that these sources are more likely concentrated in the Galactic center region, rather than distributed throughout the Galactic disk. We also find that the luminosities of the sources are positively correlated with their HI column densities, i.e., a more luminous source has a higher HI column density. From this relation, we suggest that the X-ray luminosity comes from the interaction between an isolated old neutron star and interstellar medium （mainly dense molecular clouds）. Using the standard Bondi accretion theory and the statistical information of molecular clouds in the Galactic center, we confirm this positive correlation and calculate the luminosity range in this scenario, which is consistent with the observation （10^32 - 10^35 erg s^-1）.展开更多
基金supported by the National Key R&D Program of China(2022YFA1602901)the National Natural Science Foundation of China(NSFC,grant Nos.11988101,11873051,12125302,and 11903043)+2 种基金CAS Project for Young Scientists in Basic Research(grant No.YSBR-062)the China Manned Space Program(grant Nos.CMS-CSST-2025-A03 and CMSCSST-2025-A10)the K.C.Wong Education Foundation.
文摘Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely applicable method for identifying groups through machine learning techniques in real space,taking into account the impact of redshift distortion.Our methodology involves two neural networks:one is a classification model for identifying central galaxy groups,and the other is a regression model for predicting the mass of these groups.Both models input observable galaxy traits,allowing future applicability to real survey data.Testing on simulated datasets indicates our method accurately identifies over 92%of groups with M_(vir)≥10^(11) h^(−1)M_(⊙),with 80%achieving a membership completeness of at least 80%.The predicted group masses vary by less than 0.3 dex across different mass scales,even in the absence of a priori data.Our network adapts seamlessly to expand to sparse samples with a flux limit of mr<14,to high redshift samples at z=1.08,and to galaxy samples from the TNG300 hydrodynamical simulation without further training.Furthermore,the framework can easily adjust to real surveys by training on redshift-distorted samples without needing parameter changes.Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.
基金supported by the National Natural Science Foundation of China(NSFC,grant Nos.11988101 and12222305)National Key R&D Program of China No.2024YFA1611900Guoshoujing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,LAMOST)is a National Major Scientific Project built by the Chinese Academy of Sciences。
文摘In our previous work,we identified~100,000 metal-poor stars([Fe/H]<-1.0)from the LAMOST Survey.This work estimates their chemical abundances and explores the origin and evolution of the Galactic metal-poor disk.Our chemo-dynamical analysis reveals four main populations within the metal-poor disk:(1)a primordial disk older than 12 Gyr with[Fe/H]>-1.5;(2)debris stars from the progenitor galaxy of Gaia±Sausage±Enceladus(GSE),but now residing in the Galactic disk;(3)the metal-poor tail of the metal-rich,high-αdisk formed10±12 Gyr ago,with metallicity lower limit extending to-2.0;(4)the metal-poor tail of the metal-rich,low-αdisk younger than 8 Gyr,reaching a lower metallicity limit of-1.8.These results reveal the presence of a primordial disk and show that both high-αand low-αdisks reach lower metallicities than previously thought.Analysis of merger debris reveals that Wukong,with extremely low metallicity,likely originates from merger events distinct from GSE.Additionally,three new substructures are identified:ShangGu-1,characterized by unusual[Fe/H]-eccentricity correlations;ShangGu-2,possibly heated disk stars;and ShangGu-3,which can be divided into four subgroups based on differing orbital directions,with two aligning with the previously known Nyx and Nyx-2.
文摘Based on the collected multiwavelength data, namely in the radio(NVSS, FIRST, RATAN-600), IR(WISE),optical(Pan-STARRS), UV(GALEX), and X-ray(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen's self-organizing maps(SOMs), we have constructed an independent classification of the blazars(five classes) and compared the classes with the known Roma-BZCAT classification(FSRQs, BL Lacs, galaxy-dominated BL Lacs, and blazars of an uncertain type) as well as with the high synchrotron peaked(HSP) blazars from the 3HSP catalog and blazars from the TeVCat catalog. The obtained groups demonstrate concordance with the BL Lac/FSRQ classification along with a continuous character of the change in the properties. The group of HSP blazars stands out against the overall distribution. We examine the characteristics of the five groups and demonstrate distinctions in their spectral energy distribution shapes. The effectiveness of the clustering technique for objective analysis of multiparametric arrays of experimental data is demonstrated.
基金support of the National Nature Science Foundation of China (Nos.12303017,and12203096)supported by Anhui Provincial Natural Science Foundation project No.2308085QA33supported by the science research grants from the China Manned Space Project。
文摘The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies.Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates(SFRs) are the most fundamental. We develop a novel tool, Multi-Layer Perceptron for Predicting Galaxy Parameters(MLP-GaP), that uses a machine learning(ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multiband catalogs. We first adopt a mock data set generated by the Code Investigating GALaxy Emission(CIGALE) for training and testing data sets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training data set. The results of the test performed on the mock data set show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from spectral energy distribution fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.
基金financial support from the SERB,DST,Government of India through the project CRG/2019/001110IUCAA,Pune for providing support through an associateship program+8 种基金IISER Tirupati for support through a postdoctoral fellowshipFunding for the SDSS and SDSS-II has been provided by the Alfred P.Sloan Foundationthe Participating Institutionsthe National Science Foundationthe U.S.Department of Energythe National Aeronautics and Space Administrationthe Japanese Monbukagakushothe Max Planck Societythe Higher Education Funding Council for England.
文摘We analyze the galaxy pairs in a set of volume limited samples from the Sloan Digital Sky Survey to study the effects of minor interactions on the star formation rate(SFR)and color of galaxies.We carefully design control samples of isolated galaxies by matching the stellar mass and redshift of the minor pairs.The SFR distributions and color distributions in the minor pairs differ from their controls at>99%significance level.We also simultaneously match the control galaxies in stellar mass,redshift and local density to assess the role of the environment.The null hypothesis can be rejected at>99%confidence level even after matching the environment.Our analysis shows a quenching in the minor pairs where the degree of quenching decreases with the increasing pair separation and plateaus beyond 50 kpc.We also prepare a sample of minor pairs with Hαline information.We calculate the SFR of these galaxies using the Hαline and repeat our analysis.We observe a quenching in the Hαsample too.We find that the majority of the minor pairs are quiescent systems that could be quenched due to minor interactions.Combining data from the Galaxy Zoo and Galaxy Zoo 2,we find that only∼1%galaxies have a dominant bulge,4%–7%galaxies host a bar and 5%–10%of galaxies show active galactic nucleus(AGN)activity in minor pairs.This indicates that the presence of bulge,bar or AGN activity plays an insignificant role in quenching the galaxies in minor pairs.The more massive companion satisfies the criteria for mass quenching in most of the minor pairs.We propose that the stripping and starvation likely caused the quenching in the less massive companion at a later stage of evolution.
基金supported by private funding from the California Institute of Technology and the Max Planck Institute for Radio Astronomyby NASA grants NNX08AW31G,NNX11A043G,and NNX14AQ89G and NSF grants AST0808050 and AST-1109911+4 种基金partially supported by the regional first-class discipline of Guizhou province(QJKYF[2018]216)major research projects for innovation groups in Guizhou province(Grant No.KY[2018]028)Electronic Manufacturing Industry-University-Research Base of Ordinary Colleges and Universities in Guizhou Province(Qianjiaohe KY Zi[2014]No.230-3,Youth Foundation of the Education Department of Guizhou Province(No.KY[2017]248)Guizhou Science and Technology Department(QKHJC[2019]1323)Talent base for R&D of new optoelectronic materials and electronic devices。
文摘From the Owens Valley Radio Observatory 40 m radio telescope,we have collected the light curves of the 15 GHz radio band for FSRQ J0153-1153,spanning from 2009 February to 2018 February.The Lomb-Scargle Periodogram method and the Weighted Wavelet Z-transform method are employed to search for the quasi-periodic oscillation(QPO)signal of these data,and the simulation method for the light curve is utilized to estimate the significance level of this QPO signal;thus through these techniques,the QPO signal of 3.7±0.5 yr with a significance level of 3.68σis revealed for the first time.It is most likely an explanation for the QPO signal that a binary black hole system gives rise to a Newtonian-driven the precession of jet.Based on this assumption,we find that the mass of the secondary black hole in this system may be larger than the mass of the primary black hole;and we estimate the intrinsic QPO of jet precession and the QPO of companion star orbit.
基金financial support from the SERB,DST,Government of India through the project CRG/2019/001110IUCAA,Pune for providing support through an associateship program+1 种基金IISER Tirupati for support through a postdoctoral fellowshipFunding for the SDSS and SDSS-Ⅱhas been provided by the Alfred P.Sloan Foundation,the U.S.Department of Energy,the National Aeronautics and Space Administration,the Japanese Monbukagakusho,the Max Planck Society,and the Higher Education Funding Council for England。
文摘Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions.We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments.The star formation rate(SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets.The analysis is repeated for three volume limited samples covering different magnitude ranges.The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of~50 kpc.We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc.Some earlier studies report that the galaxy pairs align with the filament axis.Such alignment inside filaments indicates anisotropic accretion that may cause these differences.We do not observe these trends in the brighter galaxy samples.The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments.The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.
基金funded by the National Key R&D Program of China with No.2021YFA1600404the National Natural Science Foundation of China(NSFC+6 种基金grant Nos.11991051,12303022,12373018,12203096,12103041,12073068)Yunnan Fundamental Research Projects(grant Nos.202301AT070339,202301AT070358)Yunnan Postdoctoral Foundation Funding Project,the Yunnan Province Foundation(202001AT070069)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2022058)the Topnotch Young Talents Program of Yunnan Province,Special Research Assistant Funding Project of Chinese Academy of Sciencesthe science research grants from the China Manned Space Project with No.CMS-CSST-2021-A06Funding for the telescope has been provided by the Chinese Academy of Sciences and the People’s Government of Yunnan Province。
文摘Long-term spectroscopic monitoring campaigns on active galactic nuclei(AGNs)provide a wealth of information about its interior structure and kinematics.However,a number of the observations suffer from the contamination of second-order spectra(SOS)which will introduce some undesirable uncertainties at the red side of the spectra.In this paper,we test the effect of SOS and propose a method to correct it in the time domain spectroscopic data using the simultaneously observed comparison stars.Based on the reverberation mapping(RM)data of NGC 5548 in2019,one of the most intensively monitored AGNs by the Lijiang 2.4 m telescope,we find that the scientific object,comparison star,and spectrophotometric standard star can jointly introduce up to~30%SOS for Grism 14.This irregular but smooth SOS significantly affects the flux density and profile of the emission line,while having little effect on the light curve.After applying our method to each spectrum,we find that the SOS can be corrected effectively.The deviation between corrected and intrinsic spectra is~2%,and the impact of SOS on time lag is very minor.This method makes it possible to obtain the HαRM measurements from archival data provided that the spectral shape of the AGN under investigation does not have a large change.
基金supported by the National Natural Science Foundation of China(grant No.11922303)the Natural Science Foundation of Chongqing(grant No.CSTB2023NSCQ-MSX0103)+1 种基金the Key Research Program of Xingtai 2020ZC005the Fundamental Research Funds for the Central Universities(grant No.2042022kf1182)。
文摘The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that machine learning can recover the relation between the uncertainties of different parameters,especially,as predicted by the error propagation formula.Gravitational lensing can be used to probe both astrophysics and cosmology.As a practical application,we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters(effective lens mass ML and Einstein radiusθ_(E))in accordance with the theoretical formula for the singular isothermal ellipse(SIE)lens model.The relation of errors of lens mass and Einstein radius,(e.g.,the ratio of standard deviations F=σ_(ML)/σ_(θ_(E)))predicted by the deep convolution neural network are consistent with the error propagation formula of the SIE lens model.As a proof-of-principle test,a toy model of linear relation with Gaussian noise is presented.We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise.Error propagation plays a crucial role in identifying the physical relation among parameters,rather than a coincidence relation,therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters.
基金Supported by the National Natural Science Foundation of China.
文摘The Chandra Galactic Center Survey detected -800 X-ray point-like sources in the 2°× 0.8° sky region around the Galactic Center. We study the spatial and luminosity distributions of these sources according to their spectral properties. Fourteen bright sources detected are used to fit jointly an absorbed power-law model, from which the power-law photon index is determined to be -2.5. Assuming that all other sources have the same power-law form, the relation between hardness ratio and HI column density NH is used to estimate the NH values for all sources. Monte Carlo simulations show that these sources are more likely concentrated in the Galactic center region, rather than distributed throughout the Galactic disk. We also find that the luminosities of the sources are positively correlated with their HI column densities, i.e., a more luminous source has a higher HI column density. From this relation, we suggest that the X-ray luminosity comes from the interaction between an isolated old neutron star and interstellar medium （mainly dense molecular clouds）. Using the standard Bondi accretion theory and the statistical information of molecular clouds in the Galactic center, we confirm this positive correlation and calculate the luminosity range in this scenario, which is consistent with the observation （10^32 - 10^35 erg s^-1）.
