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

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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

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