Abstract
Gravitational wave(GW)astronomy is witnessing a transformative shift from terrestrial to space-based detection,with missions like Taiji at the forefront.While the transition brings unprecedented opportunities for exploring massive black hole binaries(MBHBs),it also imposes complex challenges in data analysis,particularly in parameter estimation amidst confusion noise.Addressing this gap,we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji en-vironment.Innovatively,our approach simplifies the data's complexity,employs a transformation mapping to overcome the year-period time-dependent response function,and unveils additional multimodality in the arrival time parameter.Our method estimates MBHBs several orders of magnitude faster than conventional techniques,maintaining high accuracy even in complex backgrounds.These findings significantly enhance the efficiency of GW data analysis,paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.
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