High-precision regression of physical parameters from black hole images generated by General Relativistic Ray Tracing(GRRT)is essential for investigating spacetime curvature and advancing black hole astrophysics.Howev...High-precision regression of physical parameters from black hole images generated by General Relativistic Ray Tracing(GRRT)is essential for investigating spacetime curvature and advancing black hole astrophysics.However,owing to limitations in observational resolution,high observational costs,and imbalanced distributions of positive and negative samples,black hole images often suffer from data scarcity,sparse parameter spaces,and complex structural characteristics.These factors pose significant challenges to conventional regression methods based on simplified physical models.To overcome these challenges,this study introduces the Multiscale Adaptive Network(MANet),a novel regression framework grounded in deep learning.MANet integrates an Adaptive Channel Attention(ACA)module to selectively enhance features in physically informative regions.Meanwhile,a Multiscale Enhancement Feature Pyramid(MEFP)is employed to capture fine-grained spatial structures,such as photon rings and accretion disks,while alleviating information loss due to downsampling.Experimental evaluations on GRRT-simulated datasets demonstrate that MANet substantially improves parameter estimation accuracy and generalization capability in high-dimensional parameter spaces,outperforming existing baseline approaches.This framework presents a promising avenue for high-precision parameter regression in Event Horizon Telescope(EHT)data analysis and broader astrophysical imaging applications characterized by sparse and noisy data.展开更多
A nuisance parameter is introduced to the semimartingale regression model proposed by Aalen(1980), and we construct two estimators for this nuisance parameter based on the results ofparametric estimation which were gi...A nuisance parameter is introduced to the semimartingale regression model proposed by Aalen(1980), and we construct two estimators for this nuisance parameter based on the results ofparametric estimation which were given by Mckeague (1986) using the method of sieves. Theconsistency of the estimators is also provided.展开更多
基金the Natural Science Foundation of Hunan Province under grant No.2023JJ30384the National Natural Science Foundation of China under Grants No.12374408the University Students'Innovation and Entrepreneurship Training Program Project。
文摘High-precision regression of physical parameters from black hole images generated by General Relativistic Ray Tracing(GRRT)is essential for investigating spacetime curvature and advancing black hole astrophysics.However,owing to limitations in observational resolution,high observational costs,and imbalanced distributions of positive and negative samples,black hole images often suffer from data scarcity,sparse parameter spaces,and complex structural characteristics.These factors pose significant challenges to conventional regression methods based on simplified physical models.To overcome these challenges,this study introduces the Multiscale Adaptive Network(MANet),a novel regression framework grounded in deep learning.MANet integrates an Adaptive Channel Attention(ACA)module to selectively enhance features in physically informative regions.Meanwhile,a Multiscale Enhancement Feature Pyramid(MEFP)is employed to capture fine-grained spatial structures,such as photon rings and accretion disks,while alleviating information loss due to downsampling.Experimental evaluations on GRRT-simulated datasets demonstrate that MANet substantially improves parameter estimation accuracy and generalization capability in high-dimensional parameter spaces,outperforming existing baseline approaches.This framework presents a promising avenue for high-precision parameter regression in Event Horizon Telescope(EHT)data analysis and broader astrophysical imaging applications characterized by sparse and noisy data.
文摘A nuisance parameter is introduced to the semimartingale regression model proposed by Aalen(1980), and we construct two estimators for this nuisance parameter based on the results ofparametric estimation which were given by Mckeague (1986) using the method of sieves. Theconsistency of the estimators is also provided.
