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Jingqi Lai

Publications and source records attributed to Jingqi Lai.

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Nonlinear tails of massive scalar fields around a black hole

Nonlinear effects play a fundamental role in the late-time ringdown of black holes, with direct implications for gravitational-wave observations. For massive fields, these dynamics become richer, yet their nonlinear signatures remain poorly understood. Here, we systematically study nonlinear tails of massive scalar perturbations, from a toy model with ingoing and outgoing sources to a self-interacting scalar model, revealing nonlinear tails and contrasting the results with their linear counterparts. We find that the nonlinear tails of massive scalar fields, opposite to massless ones, decay as the same rate as linear tails in the intermediate time, independent of source parameters or initial conditions. Nevertheless, quadratic quasinormal modes could serve as a probe to the nonlinear effects of massive fields.

gr-qc

The effects of data gaps on ringdown signals with space-based joint observation

In space-based gravitational wave observatories such as Taiji, LISA, and TianQin, data gaps are inevitable due to mission design, implementation, and the long duration of observations. These data gaps degrade data quality and cause spectral leakage during Fourier transformations. Since ringdown signals are a key scientific objective for these observatories, it is crucial to assess the impact of data gaps on ringdown signal observations. This study employs LISA's science requirement of maintaining a duty cycle of at least 75% to evaluate the worst-case impact of data gaps, and uses massive black hole binary catalogs to assess the average effects. Our findings indicate that, on average, data gaps increase parameter estimation errors by approximately 2.1 times for the (2,2) mode and by about 1.6 times for the (3,3) mode. Joint observation is commonly employed to alleviate the impact of data gaps. Similarly, we have evaluated the effects of joint observation with two configurations, Taiji-LISA and Taiji-TianQin, which demonstrate notable mitigation of the effects of data gaps. This work provides a quantitative assessment of data gaps on ringdown signals and highlights the significance of joint observation.

gr-qc