AI 中文总结
本研究采用跨维度贝叶斯推断框架tBilby，用正弦高斯子波和线性调频子波重建核心坍缩超新星引力波信号，最高获85%重叠度，可推断原中子星大小，为引力波天文学提供有力工具。
AI 中文摘要
核心坍缩超新星（CCSNe）是当前及下一代引力波观测台极具潜力的未来引力波源。重建CCSNe的引力波信号颇具挑战，因为这类信号包含随机元素、具有多个复杂特征且覆盖宽频率范围。信号的随机性尤其推动了对与形态无关的重建技术的需求，一旦观测到该技术，将使我们能够推断新生原中子星的属性、旋转特性以及未知的CCSNe爆发机制。本研究使用跨维度贝叶斯推断框架tBilby对CCSNe的引力波信号展开重建研究。我们在合成的Advanced LIGO探测器噪声中，针对一系列信噪比的模拟信号验证了该方法，采用正弦高斯子波和线性调频子波两种类型的子波重建信号，计算得出注入信号与重建信号的重叠度最高达85%。我们发现，即便重建重叠值较低，仍能捕获主导模式的时频结构，足以推断演化中原中子星的大小。这些能力确立了tBilby作为引力波天文学中爆发源研究的强大工具。
英文摘要
Core-collapse supernovae (CCSNe) are promising future sources of gravitational waves for current and next-generation observatories. Reconstructing CCSN gravitational-wave signals is challenging as they contain stochastic elements, have multiple complex features, and cover a wide frequency band. The stochasticity of the signal in particular motivates the need for morphology-independent reconstruction techniques which, once observed, will enable us to infer properties of the newly born proto-neutron star, the rotation, and the unknown CCSN explosion mechanism. In this work, we investigate the reconstruction of gravitational-wave signals from CCSNe using the transdimensional Bayesian inference framework tBilby. We demonstrate the method using simulated signals in synthetic Advanced LIGO detector noise at a range of signal-to-noise ratios. We reconstruct the signals using two types of wavelets: sine Gaussians and chirplets. We calculate overlaps between injected and reconstructed signals of up to 85%. We find that even when reconstruction overlap values are low, enough of the time-frequency structure of the dominant mode is captured to still make statements about the size of the evolving proto-neutron star. These capabilities establish tBilby as a powerful tool for gravitational-wave astronomy with burst sources.
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