Teukolsky by Design: A Hybrid Spectral-PINN solver for Kerr Quasinormal Modes
Alexandre M. Pombo, Lorenzo Pizzuti
TL;DR
SpectralPINN presents a hybrid spectral-PINN solver for Kerr QNMs that solves the Teukolsky equation in both separated and joint 2D formulations by replacing standard activations with Chebyshev polynomials. It achieves high-precision QNM frequencies comparable to Leaver's method in the separable case (hard normalization at ~0.001% cumulative error) and robust results in the non-separable 2D regime (~0.1%–0.01% depending on normalization). The method supports soft and hard normalization, uses a complex-valued, block-coordinate training regime with a specialized optimizer (HAdamD), and demonstrates a proof-of-concept non-separable deformation that maps to ET-era ringdown constraints, illustrating practical applicability to beyond-Kerr spectroscopy. Overall, SpectralPINN offers a flexible, high-accuracy pathway for QNM spectra in complex settings where separability, asymptotics, or field content complicate traditional solvers, enabling precise tests of GR and alternative compact-object models with next-generation gravitational-wave detectors.
Abstract
We introduce SpectralPINN, a hybrid pseudo-spectral/physics-informed neural network (PINN) solver for Kerr quasinormal modes that targets the Teukolsky equation in both the separated (radial/angular) and joint two-dimensional formulations. The solver replaces standard neural activation functions with Chebyshev polynomials of the first kind and supports both soft -- via loss penalties -- and hard -- enforced by analytic masks -- implementations of Leaver's normalization. Benchmarking against Leaver's continued-fraction method shows cumulative (real+imaginary part) relative frequency errors of $\sim 0.001\%$ for the separated formulation with hard normalization, $\sim 0.1\%$ for both the soft separated and soft joint formulations, and $\sim 0.01\%$ for the hard joint case. Exploiting our ability to solve the joint equation, we add a small quadrupolar perturbation to the Teukolsky operator, effectively rendering the problem non-separable. The resulting perturbed quasinormal modes are compared against the expected precision of the Einstein Telescope, allowing us to constrain the magnitude of the perturbation. These proof-of-concept results demonstrate that hybrid spectral-PINN solvers can provide a flexible pathway to quasinormal spectra in settings where separability, asymptotics, or field content become more intricate and high accuracy is required.
