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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.

Teukolsky by Design: A Hybrid Spectral-PINN solver for Kerr Quasinormal Modes

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 for the separated formulation with hard normalization, for both the soft separated and soft joint formulations, and 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.

Paper Structure

This paper contains 15 sections, 47 equations, 5 figures, 4 tables.

Figures (5)

  • Figure 1: Schematic representation of the SpectralPINN architecture. Each neuron corresponds to a Chebyshev basis function, and each "layer" is associated with one of the coordinate dependence. The scheme is loosely inspired by the PINN designs in luna2023solving.
  • Figure 2: Cumulative ($\mathbb{R}+\mathbb{I}$) relative error of the perturbation frequency $\omega$ with respect to Leaver’s continued-fraction values, as a function of $a/M$ with $N_x = N_y = 100$. Results are shown for separated-ODE PINNs with soft (black circles) and hard (magenta inverted triangles) normalization; separated-ODE SpectralPINN runs with soft (red squares), dynamical (green crosses) and hard (orange diamonds) boundary conditions for $N=L=25$; and for the full 2D PDE SpectralPINN with soft (blue triangles) and hard (grey stars) normalization for $N=L=25$.
  • Figure 3: Modulus of the 2D solution $|p(x,y)|$ (left) and training loss as a function of epoch for soft and hard NC (right).
  • Figure 4: Dependence of the fundamental $s=-2,\,\ell=2,\, m=0$ QNM frequency on the quadrupolar deformation parameter $\omega(\varepsilon)$ for $a/M=\{0.3,\, 0.9\}$. The dashed lines correspond to the theoretical expected value: $\omega = 0.7540-i\, 0.1767$ for $a/M=0.3$ (red) and $\omega = 0.8240-i\, 0.1570$ for $a/M = 0.9$ (blue).
  • Figure 5: Minimal per-mode ringdown SNR $\rho_{\min}(\epsilon)$ required to distinguish a deviation $\epsilon$ from Kerr at the $D=4$ level, using the ET-D noise curve. We show the single-mode thresholds for the $(2,0)$ and $(2,2)$ modes and the combined constraint when both modes are used jointly, for spins $a/M=0.3$ (left) and $a/M=0.9$ (right).