Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors?

TL;DR

This paper introduces a novel deep learning approach combining Transformers and Normalizing Flows to efficiently estimate parameters of overlapping gravitational wave signals, promising rapid and accurate inference for next-generation detectors.

Contribution

It presents a new model integrating Transformers and Normalizing Flows for unbiased, fast parameter estimation of overlapping signals in gravitational wave data, a significant advancement over existing methods.

Findings

Maintains accuracy regardless of data correlation levels.

Estimates chirp mass and coalescence times within 10-20% of true values.

Demonstrates robustness on simulated signals.

Abstract

Overlapping signals represent one of the major data analysis challenges in next-generation gravitational wave detectors. We leverage Transformers and Normalizing Flows, state-of-the-art machine learning algorithms, to address the parameter estimation of overlapping binary black hole mergers in the Einstein Telescope (ET). Our proposed model combines a Transformer-based "Knowledge Extractor Neural Network" (KENN) with a Normalizing Flow (HYPERION) to perform rapid and unbiased inference over multiple overlapping black hole binary events. The choice of architecture leverages the strength of Transformers in capturing complex and long-range temporal structures in the strain time series data, while Normalizing Flows provide a powerful framework to sample posterior distributions. We demonstrate the effectiveness and robustness of our model over simulated gravitational wave signals, showing that it maintains the same level of accuracy regardless of the correlation level in the data. Moreover our model provides estimates of chirp mass and coalescence times within <10-20% from the true simulated value. The results obtained are promising and show how this approach might represent a first step toward a deep-learning based inference pipeline for ET and other future gravitational wave detectors.