Murmurations, Mestre--Nagao sums, and Convolutional Neural Networks for elliptic curves

📅 2026-03-18
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This study investigates the prediction of analytic ranks of elliptic curves over the rational numbers, exploring their deep connection with Frobenius traces. We propose a one-dimensional convolutional neural network model that leverages sequences of Frobenius traces to predict analytic ranks and employs saliency analysis to interpret the model’s decisions. Our research uncovers novel patterns linking murmurations phenomena with Mestre–Nagao sums, revealing how these relationships vary with conductor and predicted rank. Across multiple conductor ranges, the model achieves high predictive accuracy while offering mathematically interpretable insights, thereby opening new avenues at the intersection of arithmetic geometry and machine learning.

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📝 Abstract
We apply one-dimensional convolutional neural networks to the Frobenius traces of elliptic curves over $\mathbb{Q}$ and evaluate and interpret their predictive capacity. In keeping with similar experiments by Kazalicki--Vlah, Bujanović--Kazalicki--Novak, and Pozdnyakov, we observe high accuracy predictions for the analytic rank across a range of conductors. We interpret the prediction using saliency curves and explore the interesting interplay between murmurations and Mestre--Nagao sums, the details of which vary with the conductor and the (predicted) rank.
Problem

Research questions and friction points this paper is trying to address.

elliptic curves
analytic rank
Frobenius traces
murmurations
Mestre--Nagao sums
Innovation

Methods, ideas, or system contributions that make the work stand out.

convolutional neural networks
elliptic curves
Frobenius traces
murmurations
Mestre–Nagao sums
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