[Paper] Flexible Gravitational-Wave Parameter Estimation with Transformers

Source: arXiv - 2512.02968v1

Overview

The paper presents Dingo‑T1, a transformer‑based neural network that can perform gravitational‑wave (GW) parameter estimation across a wide variety of analysis configurations without needing to be retrained for each case. By making the model flexible enough to handle missing data, different detector setups, and custom frequency cuts, the authors show that deep learning can keep pace with the growing volume and complexity of GW observations.

Key Contributions

• Flexible Transformer Architecture – Introduces a novel design that accepts variable‑length inputs and can gracefully handle missing or masked data at inference time.
• Unified Model for Multiple Settings – A single trained Dingo‑T1 model successfully analyzes 48 events from LIGO‑Virgo‑KAGRA O3 under dozens of detector‑frequency configurations.
• Improved Sample Efficiency – Increases the median effective sample size on real events from 1.4 % (baseline) to 4.2 %, meaning fewer posterior samples are needed for the same statistical precision.
• Enables Systematic Studies – Demonstrates how the model can be used to explore the impact of detector choices and frequency cuts on inferred astrophysical parameters.
• Supports Consistency Tests – Applies the model to inspiral‑merger‑ringdown (IMR) consistency checks, a key test of General Relativity, without retraining.

Methodology

1. Transformer Backbone – The authors adapt the self‑attention mechanism (originally popularized in NLP) to GW time‑frequency data. Each detector’s spectrogram is tokenized, and positional encodings preserve the frequency ordering.
2. Mask‑aware Training – During training, random subsets of frequency bins or entire detector channels are masked out. The network learns to infer parameters even when parts of the input are missing, which translates to flexibility at inference.
3. Conditional Embedding of Analysis Settings – Configuration details (e.g., which detectors are active, low‑frequency cut‑offs) are encoded as auxiliary tokens and fed into the transformer, allowing the same weights to adapt to different setups.
4. Posterior Approximation – The model outputs a set of samples from the posterior distribution of source parameters (masses, spins, sky location, etc.) using a normalizing‑flow decoder that maps latent Gaussian noise to physically plausible parameter values.
5. Training Data – Simulated GW signals spanning the full LIGO‑Virgo‑KAGRA parameter space are used, with realistic noise added. The training set includes a wide range of detector configurations to teach the model the required flexibility.

Results & Findings

• Robustness Across Configurations – Dingo‑T1 reproduces the posterior estimates of the standard Bayesian pipelines for all 48 O3 events, even when the input data are deliberately altered (e.g., removing one detector or raising the low‑frequency cut).
• Sample‑Efficiency Gains – The median effective sample size (ESS) rises to 4.2 % of the total generated samples, a three‑fold improvement over the baseline model that lacked flexibility.
• Speed – Inference for a single event takes on the order of seconds on a modern GPU, compared to hours for traditional Markov‑Chain Monte Carlo (MCMC) methods.
• IMR Consistency Tests – Using Dingo‑T1, the authors perform inspiral‑merger‑ringdown consistency checks on several events and recover results consistent with General Relativity, showing that the flexible model can be plugged into higher‑level scientific analyses.

Practical Implications

• Rapid Turn‑around for Alerts – Observatories can obtain credible parameter posteriors within seconds of a detection, enabling faster electromagnetic follow‑ups and multi‑messenger campaigns.
• Cost‑Effective Scaling – A single model replaces the need to train and maintain dozens of specialized networks for different detector states, reducing engineering overhead for GW data centers.
• What‑If Analyses – Researchers can instantly explore “what‑if” scenarios (e.g., how a new detector would improve sky localization) without rerunning expensive Bayesian pipelines.
• Future‑Proofing – As next‑generation detectors (Einstein Telescope, Cosmic Explorer) come online with broader bandwidths and new noise characteristics, the mask‑aware transformer can be fine‑tuned rather than rebuilt from scratch.
• Integration into Pipelines – Dingo‑T1’s output is a set of posterior samples, directly compatible with existing astrophysical inference tools (e.g., Bilby, PyCBC Inference), making adoption straightforward.

Limitations & Future Work

• Training Cost – The initial training on a massive simulated dataset still requires substantial GPU resources and careful hyper‑parameter tuning.
• Domain Gap – While the model performs well on O3 real data, subtle mismatches between simulated noise and actual detector noise could affect performance on future runs.
• Extending to Higher‑Dimensional Physics – Incorporating additional physics (e.g., tidal effects for neutron stars, eccentricity) will increase the output dimensionality and may demand larger models or more sophisticated decoders.
• Explainability – As with most deep‑learning approaches, interpreting why the transformer makes specific posterior choices remains an open challenge.

Overall, Dingo‑T1 showcases how modern deep‑learning architectures can bring both flexibility and speed to gravitational‑wave inference, paving the way for real‑time, adaptable analysis pipelines in the era of high‑rate GW astronomy.

Authors

• Annalena Kofler
• Maximilian Dax
• Stephen R. Green
• Jonas Wildberger
• Nihar Gupte
• Jakob H. Macke
• Jonathan Gair
• Alessandra Buonanno
• Bernhard Schölkopf

Paper Information

• arXiv ID: 2512.02968v1
• Categories: gr-qc, astro-ph.IM, cs.LG
• Published: December 2, 2025
• PDF: Download PDF