Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge. Deep learning provides a powerful alternative to traditional inference, but existing neural models typically lack the flexibility to handle variations in data analysis settings. Such variations accommodate imperfect observations or are required for specialized tests, and could include changes in detector configurations, overall frequency ranges, or localized cuts. We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time. Applied to parameter estimation, we demonstrate that a single flexible model -- called Dingo-T1 -- can (i) analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run under a wide range of analysis configurations, (ii) enable systematic studies of how detector and frequency configurations impact inferred posteriors, and (iii) perform inspiral-merger-ringdown consistency tests probing general relativity. Dingo-T1 also improves median sample efficiency on real events from a baseline of 1.4% to 4.2%. Our approach thus demonstrates flexible and scalable inference with a principled framework for handling missing or incomplete data -- key capabilities for current and next-generation observatories.

翻译：引力波数据分析依赖于从含噪探测器信号中准确高效地提取物理信息的方法，然而观测频率的不断提升与观测复杂性的日益增加构成了持续增长的挑战。深度学习为传统推断方法提供了强有力的替代方案，但现有神经网络模型通常缺乏灵活性，难以适应数据分析场景的变化。此类变化可容纳不完美的观测数据，或是专门化测试所必需，可能包括探测器配置、整体频率范围或局部数据截断的调整。我们提出了一种基于Transformer的灵活架构，并配合训练策略，使其能够在推断阶段适应多样化的分析场景。应用于参数估计时，我们证明单一灵活模型——命名为Dingo-T1——能够：（i）在多种分析配置下处理第三次LIGO-Virgo-KAGRA观测运行中的48个引力波事件；（ii）系统研究探测器及频率配置如何影响推断后验分布；（iii）执行检验广义相对论的旋进-合并-铃荡一致性测试。Dingo-T1还将实际事件的样本效率中值从基线1.4%提升至4.2%。因此，我们的方法通过处理缺失或不完整数据的原理性框架，展示了灵活且可扩展的推断能力——这对当前及下一代观测站而言是关键能力。