Learning Euler factors of elliptic curves
Abstract
We apply transformer models and feedforward neural networks to predict Frobenius traces $a_p$ from elliptic curves given other traces $a_q$. We train further models to predict $a_p \bmod 2$ from $a_q \bmod 2$, and cross-analysis such as $a_p \bmod 2$ from $a_q$. Our experiments reveal that these models achieve high accuracy, even in the absence of explicit number-theoretic tools like functional equations of $L$-functions. We also present partial interpretability findings.
Fields of Study & Subjects
Advanced Numerical Analysis Techniques
Publication details
- Journal / source
- Advances in Theoretical and Mathematical Physics
- Volume
- 29
- Issue
- 8
- Pages
- 2327–2351
- Publication date
- 1 January 2026
- Language
- EN