Tests of Isospectrality of Quasinormal Mode with Gravitational Wave Observations

This dataset contains the code and posterior samples associated with the paper "Tests of Isospectrality of Quasinormal Mode with Gravitational Wave Observations" by Rui Niu, Zhao Li, Tao Zhu, and Wen Zhao. The paper presents the first theory-agnostic test of black-hole isospectrality, the equality of the frequencies and damping times of parity-even and parity-odd quasinormal modes, using gravitational-wave observations from the LIGO-Virgo-KAGRA Collaboration. Isospectrality is generically broken by corrections beyond general relativity, in which case the dominant (2,2,0) quasinormal mode splits into two closely spaced components. The splitting is parameterized by the fractional frequency and damping-time deviations ($\delta f_2$, $\delta\tau_2$). Two scenarios are considered: a general scenario in which the two parity sectors shift independently (all deviation parameters ($\delta f_1$, $\delta\tau_1$, $\delta f_2$, $\delta\tau_2$) are free varied), and a symmetric scenario in which the common shift is fixed to the GR value ($\delta f_1=\delta\tau_1=0$). A hierarchical Bayesian analysis of ten selected events yields 90% credible upper limits on $\delta f_2<0.259$ (general) and $\delta f_2<0.084$ (symmetric). The damping-time splitting remains unconstrained at current detector sensitivity. The results are consistent with black-hole isospectrality and represent the first observational constraints on this fundamental prediction of GR. Contents:- config: The pyRing configuration files for the ten selected events in both scenarios, together with the outputs of each run.- hier_comb: Results of the hierarchical Bayesian inference, and the notebooks used to perform the hierarchical parameter estimation.- plot: The notebooks used to produce the figures and tables of the paper.- seclect_events: Various scripts related to event selection, such as downloading data, posterior extraction, ringdown SNR computation, and configuration generation.- pyRing: The customized version of the ringdown data analysis software pyRing used in this work.