A Deep Learning Framework for Amplitude Generation of Generic EMRIs

TL;DR

This paper presents a deep learning framework that rapidly generates accurate Teukolsky amplitudes for generic EMRIs, significantly improving computational efficiency for gravitational wave data analysis.

Contribution

It introduces a convolutional encoder-decoder model with transfer learning for fast, end-to-end fitting of EMRI waveform amplitudes, enabling real-time waveform generation.

Findings

Full harmonic amplitudes generated within milliseconds

Median mode-distribution error around 10^{-3}

Framework viable for efficient EMRI waveform modeling

Abstract

One of the main targets for space-borne gravitational wave detectors is the detection of Extreme Mass Ratio Inspirals (EMRIs). The data analysis of EMRIs requires waveform models that are both accurate and fast. The major challenge for the fast generation of such waveforms is the generation of the Teukolsky amplitudes for generic (eccentric and inclined) Kerr orbits. The requirement for the modeling of $\sim10^5$ harmonic modes across a four-dimensional parameter space makes traditional approaches, including direct computation or dense interpolation, computationally prohibitive. To overcome this issue, we introduce a convolutional encoder-decoder architecture for a fast and end-to-end global fitting of the Teukolsky amplitudes. We also adopt a transfer learning strategy to reduce the size of the training dataset, and the model is trained gradually from the simplest Schwarzschild circular orbits to generic Kerr orbits step by step. Within this framework, we obtain a surrogate model based on a semi-analytical Post-Newtonian dataset, and the full harmonic amplitudes can be generated within milliseconds, while the median mode-distribution error for generic orbits is approximately $\sim10^{-3}$. This result indicates that the framework is viable for constructing efficient waveform models for EMRIs.