标题： 从广义相对论偏离的函数推断 显示英文标题

标题： Functional inference on deviations from General Relativity

摘要： 广义相对论的扩展通常预测引力波形形态的修改，这些修改在功能上依赖于源参数，例如合并黑洞的质量和自旋。 然而，当前对强场引力的分析缺乏稳健的数据驱动方法来推断这种功能依赖性。 在本工作中，我们引入了GRANITA，这是一种非微扰、理论无关的框架，使用高斯过程回归来表征与参数相关的偏离广义相对论的现象。 利用该方法的灵活性，我们分析了模拟数据和LIGO-Virgo-KAGRA公开目录中的真实事件。 我们展示了该方法在参数空间中检测和量化波形偏差的能力。 此外，我们表明该方法可以识别可能来自环境效应或次级未建模物理的随机（非确定性）偏差。 随着引力波对强引力的测试精度不断提高，我们的框架提供了一种系统的方法来约束修改后的引力，并减轻天体物理或仪器系统误差的污染。 显示英文摘要

摘要： Extensions of general relativity often predict modifications to gravitational waveform morphology that depend functionally on source parameters, such as the masses and spins of coalescing black holes. However, current analyses of strong-field gravity lack robust, data-driven methods to infer such functional dependencies. In this work, we introduce GRANITA, a non-perturbative, theory-agnostic framework to characterize parameter-dependent deviations from general relativity using Gaussian process regression. Leveraging the flexibility of this method, we analyze both simulated data and real events from the LIGO-Virgo-KAGRA public catalog. We demonstrate the ability of our approach to detect and quantify waveform deviations across the parameter space. Furthermore, we show that the method can identify stochastic (non-deterministic) deviations, potentially arising from environmental effects or subdominant unmodeled physics. As gravitational-wave tests of strong gravity advance in precision, our framework provides a principled approach to constrain modified gravity and to mitigate contamination from astrophysical or instrumental systematics.