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[2602.16656] Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks
Summary
This article explores the nonlinear quenching effects on polar field buildup in the Sun using Physics-Informed Neural Networks (PINN), highlighting the interplay of tilt and latitude quenching in solar cycle predictions.
Why It Matters
Understanding the mechanisms behind solar magnetic field dynamics is crucial for predicting solar cycles, which can impact space weather and technology on Earth. This research leverages advanced machine learning techniques to improve predictive models, offering significant implications for astrophysics and solar research.
Key Takeaways
- Physics-Informed Neural Networks provide a robust framework for modeling solar magnetic field dynamics.
- Tilt quenching (TQ) and latitude quenching (LQ) significantly influence polar field buildup and solar cycle amplitude.
- The study reveals a smooth inverse-square relationship between TQ and LQ contributions, enhancing predictive accuracy.
- The findings suggest that nonlinear interactions can explain the observed modulation of solar cycles.
- PINN outperforms traditional models in accuracy and consistency, showcasing its potential for future solar research.
Astrophysics > Solar and Stellar Astrophysics arXiv:2602.16656 (astro-ph) [Submitted on 18 Feb 2026] Title:Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks Authors:Jithu J. Athalathil, Mohammed H. Talafha, Bhargav Vaidya View a PDF of the paper titled Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks, by Jithu J. Athalathil and 1 other authors View PDF Abstract:The solar dynamo relies on the regeneration of the poloidal magnetic field through processes strongly modulated by nonlinear feedbacks such as tilt quenching (TQ) and latitude quenching (LQ). These mechanisms play a decisive role in regulating the buildup of the Sun's polar field and, in turn, the amplitude of future solar cycles. In this work, we employ Physics-Informed Neural Networks (PINN) to solve the surface flux transport (SFT) equation, embedding physical constraints directly into the neural network framework. By systematically varying transport parameters, we isolate the relative contributions of TQ and LQ to polar dipole buildup. We use the residual dipole moment as a diagnostic for cycle-to-cycle amplification and show that TQ suppression strengthens with increasing diffusivity, while LQ dominates in advection-dominated regimes. The ratio $\Delta D_{\mathrm{LQ}}/\Delta D_{\mathrm{TQ}}$ exhibits a smooth inverse-square dependence on the dynamo effectivity range, refining previo...