Machine learning for number theory: unsupervised learning with L-functions : WestminsterResearch

Publication dates
Title	Machine learning for number theory: unsupervised learning with L-functions
Authors	Oliver, T.
Type	Conference paper
Abstract	There is a strong tradition of computation in number theory, with notable data-driven insights including the prime number theorem and the conjecture of Birch and Swinnerton-Dyer. A huge arithmetic online database known as the LMFDB went live in the mid-2010s, to which we began applying machine learning methodologies in 2020. This led to a data scientific perspective on old problems, and the discovery of surprising new structures in arithmetic statistics known as "murmurations". In this extended abstract, we will apply unsupervised learning techniques to a small dataset taken from the LMFDB, chosen so as to demonstrate one approach to generalising the original experiments.
Keywords	PCA
	Clustering
	L-functions
Year	2024
Conference	8th International Congress on Mathematical Software (ICMS2024)
Publisher	Springer
Published online	17 Jul 2024
Journal	Lecture Notes in Computer Science
Journal citation	1479, pp. 196-203
ISSN	0302-9743
	1611-3349
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-031-64529-7_21
Web address (URL)	https://link.springer.com/chapter/10.1007/978-3-031-64529-7_21

Related outputs

Murmurations of elliptic curves
Oliver, T., He, Y.-H., Lee, K.-H. and Pozdnyakov, A. 2024. Murmurations of elliptic curves. Experimental Mathematics. Advanced online publication. https://doi.org/10.1080/10586458.2024.2382361

PCA, arithmetic, and murmurations
Oliver, T. 2024. PCA, arithmetic, and murmurations. Murmurations in Arithmetic. ICERM (Brown University) 06 - 08 Jul 2023 World Scientific Publishing. https://doi.org/10.1142/S2810939224400021

Preprint: Murmurations of Dirichlet Characters
Lee, K.-H., Oliver, T. and Pozdnyakov, A. 2023. Preprint: Murmurations of Dirichlet Characters. arXiv. https://doi.org/10.48550/arXiv.2307.00256

Machine Learning Class Numbers of Real Quadratic Fields
Oliver, T., Amir, M., He, Y.-H., Lee, K.-H. and Sultanow, E. 2023. Machine Learning Class Numbers of Real Quadratic Fields. International Journal of Data Science in the Mathematical Sciences. 1 (2), pp. 107-134. https://doi.org/10.1142/s2810939223500016

Counting points on elliptic curves
Oliver, T. and Wuthrich, C. 2023. Counting points on elliptic curves. LMS newsletter. 509, pp. 31-35. https://doi.org/10.1112/NLMS

Supervised learning of arithmetic invariants
Oliver, T. 2023. Supervised learning of arithmetic invariants. in: Yang-Hui He (ed.) Machine Learning in Pure Mathematics and Theoretical Physics World Scientific Publishing. pp. 331-363

Machine learning invariants of arithmetic curves
He, Y.-H., Lee, K.-H. and Oliver, T. 2023. Machine learning invariants of arithmetic curves. Journal of Symbolic Computation. 115, pp. 478-491. https://doi.org/10.1016/j.jsc.2022.08.017

Ratios of Artin L-functions
Hochfilzer, L. and Oliver, T. 2022. Ratios of Artin L-functions. Journal of Number Theory. 236, pp. 1-40. https://doi.org/10.1016/j.jnt.2021.07.007

Preprint: Murmurations of Elliptic Curves
He, Y.-H., Lee, K.-H., Oliver, T. and Pozdnyakov, A. 2022. Preprint: Murmurations of Elliptic Curves. arXiv. https://doi.org/10.48550/arxiv.2204.10140

Preprint: Machine Learning Class Numbers of Real Quadratic Fields
Amir, M., He, Y.-H., Lee, K.-H., Oliver, T. and Sultanow, E. 2022. Preprint: Machine Learning Class Numbers of Real Quadratic Fields. arXiv. https://doi.org/10.48550/arxiv.2209.09283

Preprint: Convergance of Kac-Moody Eisenstein Series over a Function Field
Lee, K.-H., Liu, D. and Oliver, T. 2022. Preprint: Convergance of Kac-Moody Eisenstein Series over a Function Field. arXiv. https://doi.org/10.48550/arxiv.2203.08628

Machine-learning the Sato-Tate conjecture
He, Y.-H., Lee, K.-H. and Oliver, T. 2022. Machine-learning the Sato-Tate conjecture. Journal of Symbolic Computation. 111, pp. 61-72. https://doi.org/10.1016/j.jsc.2021.11.002

Ratios of Artin L-functions
Hochfilzer, L. and Oliver, T. 2022. Ratios of Artin L-functions. Journal of Number Theory. 236, pp. 1-40. https://doi.org/10.1016/j.jnt.2021.07.007

Machine-learning number fields
He, Y.-H., Lee, K.-H. and Oliver, T. 2022. Machine-learning number fields. Mathematics, Computation and Geometry of Data. 2 (1), pp. 49-66. https://doi.org/10.4310/MCGD.2022.v2.n1.a2

Character expansion of Kac–Moody correction factors
Lee, K.-H., Liu, D and Oliver, T. 2021. Character expansion of Kac–Moody correction factors. Pacific Journal of Mathematics. 313 (1), pp. 159-183. https://doi.org/10.2140/pjm.2021.313.159

Preprint: Twisting moduli for gl(2)
Bedert, B., Cooper, G., Oliver, T. and Zhang, P. 2020. Preprint: Twisting moduli for gl(2). arXiv. https://doi.org/10.48550/arxiv.2003.02557

Preprint: Machine-Learning the Sato-Tate Conjecture
He, Y.-H., Lee, K.-H. and Oliver, T. 2020. Preprint: Machine-Learning the Sato-Tate Conjecture. arXiv. https://doi.org/10.48550/arxiv.2010.01213

Preprint: Machine-learning number fields
He, Y.-H., Lee, K.-H. and Oliver, T. 2020. Preprint: Machine-learning number fields. arXiv. https://doi.org/10.48550/arxiv.2011.08958

Preprint: Machine-learning arithmetic curves
He, Y.-H., Lee, K.-H. and Oliver, T. 2020. Preprint: Machine-learning arithmetic curves. arXiv. https://doi.org/10.48550/arxiv.2012.04084

Twisting moduli for GL(2)
Bedert, B., Cooper, G., Oliver, T. and Zhang, P. 2020. Twisting moduli for GL(2). Journal of Number Theory. 217, pp. 142-162. https://doi.org/10.1016/j.jnt.2020.04.008

Weil's converse theorem for Maass forms and cancellation of zeros
Oliver, T. and Neururer, M. 2020. Weil's converse theorem for Maass forms and cancellation of zeros. Acta Arithmetica. 196, pp. 387-422. https://doi.org/10.4064/aa190811-3-2

Preprint: Ratios of Artin L-functions
Hochfilzer, L. and Oliver, T. 2019. Preprint: Ratios of Artin L-functions. arXiv. https://doi.org/10.48550/arxiv.1910.02821

Preprint: Weil's Converse Theorem for Maass Forms and Cancellation of Zeros
Neururer, M. and Oliver, T. 2018. Preprint: Weil's Converse Theorem for Maass Forms and Cancellation of Zeros. arXiv. https://doi.org/10.48550/arxiv.1809.06586

A conjectural extension of Hecke’s converse theorem
Bettin, S., Bober, J., Booker, A., Conrey, B., Lee, M., Molteni, G., Oliver, T., Platt, D. and Steiner, R. 2018. A conjectural extension of Hecke’s converse theorem. The Ramanujan Journal. 47, pp. 659-684. https://doi.org/10.1007/s11139-017-9953-y

A conjectural extension of hecke's converse theorem
Bettin, S., Bober, J.W., Booker, A.R., Conrey, B., Lee, M., Molteni, G., Oliver, T., Platt, D.J. and Steiner, R.S. 2017. A conjectural extension of hecke's converse theorem. arXiv. https://doi.org/10.48550/arxiv.1704.02570

Notes on low degree L-data
Oliver, T. 2017. Notes on low degree L-data. Analytic Number Theory and Related Areas. Research Institute for Mathematical Sciences, Kyoto University 04 - 06 Nov 2015

Automorphicity and mean-periodicity
Oliver, T. 2017. Automorphicity and mean-periodicity. Journal of the Mathematical Society of Japan. 69 (1), pp. 25-51. https://doi.org/10.2969/jmsj/06910025

Zeta integrals on arithmetic surfaces
Oliver, T. 2016. Zeta integrals on arithmetic surfaces. St Petersburg Math. J.. 27, pp. 1003-1028. https://doi.org/10.1090/spmj/1432

Higher Dimensional Adeles, Mean-Periodicity and Zeta Functions of Arithmetic Surfaces
Oliver, T. 2014. Higher Dimensional Adeles, Mean-Periodicity and Zeta Functions of Arithmetic Surfaces. PhD thesis University of Nottingham School of Mathematical Sciences

Permalink - https://westminsterresearch.westminster.ac.uk/item/w9q30/machine-learning-for-number-theory-unsupervised-learning-with-l-functions

File access - Request a copy

Machine learning for number theory: unsupervised learning with L-functions

Related outputs

Restricted files

Share this

Usage statistics

Export as