Machine learning the vanishing order of rational L-functions
Joanna Bieri, Giorgi Butbaia, Edgar Costa, Alyson Deines, Kyu-Hwan Lee, David Lowry-Duda, Thomas Oliver, Yidi Qi, Tamara Veenstra
TL;DR
The paper tackles predicting the vanishing order $r=\operatorname{ord}_{s=(w+1)/2} L(s)$ of rational $L$-functions from Dirichlet coefficients $\{a_p\}$ using machine learning. It builds the RAT/PRAT datasets, defines normalized feature vectors $\overline{a}_p$ and $v(L)\in\mathbb{R}^{168}$, and applies PCA, LDA, and CNNs to learn $r$, achieving high accuracy and demonstrating transfer learning across subdatasets like ECNF and G2Q. The results show strong separability of vanishing orders and reveal that murmuration-like coefficient patterns encode arithmetic information related to the central value, with connections to Mestre–Nagao sums. These findings offer data-driven insights into arithmetic invariants and suggest avenues for further mathematical and ML investigations across broader conductor ranges.
Abstract
In this paper, we study the vanishing order of rational $L$-functions from a data scientific perspective. Each $L$-function is represented in our data by finitely many Dirichlet coefficients, the normalisation of which depends on the context. We observe murmuration-like patterns in averages across our dataset, find that PCA clusters rational $L$-functions by their vanishing order, and record that LDA and neural networks may accurately predict this quantity.
