arXiv subjects

Stella Menziltsidou

Publications and source records attributed to Stella Menziltsidou.

2 recordsLinked to original sources

arXiv2026

A Diagnostic Inverse-PINN Study of Identifiability in Reduced Black-Hole Spin Inference

Black-hole spin is a fundamental parameter in relativistic astrophysics, influencing accretion efficiency, jet launching, and perturbative modes. This work investigates a hybrid physicsinformed inverse framework for reduced black-hole spin inference. Spin recovery is formulated as an inverse problem constrained by a reduced scalar angular Teukolsky-like equation. A Physics-Informed Neural Network approximates the angular mode function, while the spin parameter is treated as a trainable physical quantity linked to the residual of the governing differential operator. The framework is evaluated under controlled synthetic and noisecontaminated angular-mode configurations across multiple reference spin values. The main result is diagnostic rather than confirmatory. The PINN reproduces angular-mode profiles qualitatively; however, the inferred spin values cluster near the upper part of the allowed interval rather than accurately recovering the full range of reference spins. This reveals a weak-identifiability regime in which accurate angular-profile reconstruction and low residual loss do not necessarily imply accurate spin recovery. The results highlight the diagnostic value of physics-informed constraints for reduced black-hole spin-inference experiments, while also showing the need for further identifiability analysis, loss reweighting, joint eigenvalue inference, full Kerr perturbation modeling, realistic uncertainty models, and validation against numerical solvers.

astro-ph.HE↗

arXiv2025

Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning

The measurement of black hole spin is considered one of the key problems in relativistic astrophysics. Existing methods, such as continuum fitting, X-ray reflection spectroscopy and quasi-periodic oscillation analysis, have systematic limitations in accuracy, interpretability and scalability. In this work, a hybrid approach is proposed in which theoretical models based on the Teukolsky formalism are integrated with Physics-Informed Neural Networks (PINNs). A PINN model is developed to solve the linearized spin problem in the scalar case, with physical constraints directly embedded into the training process. Annotated data are not required; instead, the model is trained using the differential operator and boundary conditions as supervision. It is demonstrated that the PINN converges reliably, with residual loss values below 1e-7 and a root mean squared error (RMSE) of the order of 1e-6 (final approx 5.4 x 1e-8). Benchmarking results indicate that the proposed method outperforms both classical and data-driven machine learning approaches in terms of AUC and sensitivity, while also exhibiting superior interpretability, generalizability and adherence to physical principles, with moderate computational cost. Potential extensions include integration with general relativistic magnetohydrodynamics (GRMHD) solvers and application to real observational data. These findings support the viability of physics-based machine learning as a robust framework for accurate and interpretable black hole spin estimation.

astro-ph.HE↗