Flexible Gravitational-Wave Parameter Estimation with Transformers

2025-12-02

AI Summary

Gravitational-wave parameter estimation faces challenges in flexibility and efficiency due to dynamic changes in detector configurations, frequency-band coverage, and noise characteristics. We propose Dingo-T1—the first Transformer-based adaptive Bayesian inference framework—capable of seamless, inference-time adaptation to heterogeneous detector networks, variable frequency bands, and local data truncation, without retraining. By integrating deep learning with Bayesian inference and incorporating a robust training strategy to handle missing or incomplete data, Dingo-T1 improves median sample efficiency from 1.4% to 4.2% across 48 real gravitational-wave events. To our knowledge, this is the first method enabling systematic, single-model-driven parameter estimation across diverse detector configurations and simultaneous consistency tests of general relativity. The framework significantly enhances both the generalizability and physical interpretability of gravitational-wave data analysis.

Technology Category

• Machine Learning: Bayesian Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Flexible parameter estimation for gravitational-wave data analysis
• Adapting to diverse detector and frequency configurations
• Handling missing or incomplete data in gravitational-wave observations

Innovation

• Transformer-based architecture for flexible gravitational-wave analysis
• Training strategy enabling adaptation to diverse analysis settings
• Single model handling multiple events and configurations efficiently