Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums

TL;DR

This paper advances elliptic curve rank classification by combining multi-range Mestre--Nagao sums with machine learning to optimize sum weightings, improving predictive accuracy.

Contribution

It introduces a multi-value sum approach and a neural network-based weighting scheme to enhance rank heuristics for elliptic curves.

Findings

Multi-value sums improve classification accuracy.

Neural network weighting slightly outperforms traditional heuristics.

Adaptive sums show promise for better rank estimation.

Abstract

Determining the rank of an elliptic curve E/Q remains a central challenge in number theory. Heuristics such as Mestre--Nagao sums are widely used to estimate ranks, but there is considerable room for improving their predictive power. This paper introduces two novel methods for enhancing rank classification using Mestre--Nagao sums. First, we propose a ``multi-value'' approach that simultaneously uses two distinct sums, S_0 and S_5, evaluated over multiple ranges. This multi-sum perspective significantly improves classification accuracy over traditional single-sum heuristics. Second, we employ machine learning -- specifically deep neural networks -- to learn optimal, potentially conductor-dependent weightings for Mestre--Nagao sums directly from data. Our results indicate that adaptively weighted sums offer a slight edge in rank classification over traditional methods.