Opportunities for the Mathematical Sciences

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Table of Contents
Summary Article
Individual Contributions
  Statistics as the information science
  Statistical issues for databases, the internet, and experimental data
  Mathematics in image processing, computer graphics, and computer vision
  Future challenges in analysis
  Getting inspiration from electrical engineering and computer graphics to develop interesting new mathematics
  Research opportunities in nonlinear partial differential equations
  Risk assessment for the solutions of partial differential equations
  Discrete mathematics for information technology
  Random matrix theory, quantum physics, and analytic number theory
  Mathematics in materials science
  Mathematical biology: analysis at multiple scales
  Number Theory and its Connections to Geometry and Analysis
  Revealing hidden values: inverse problems in science and industry
  Complex stochastic models for perception and inference
  Model theory and tame mathematics
  Beyond flatland: the future of space and time
  Mathematics in molecular biology and medicine
  The year 2000 in geometry and topology
  Computations and numerical simulations
  Numbers, insights and pictures: using mathematics and computing to understand mathematical models
List of Contributors with Affiliations

Random Matrix Theory, Quantum Physics, and Analytic Number Theory

A. Granville

Some of the most extraordinary cross-fertilization of ideas in recent mathematics comes in understanding the details of the distributions of primes, class numbers, ranks of elliptic curves, Frobenius eigenvalues in finite fields, etc., all important quantities that can be described in terms of the zeros of certain "zeta functions." Following up on earlier work of Montgomery and Dyson, Peter Sarnak of Princeton University has been pushing the idea that these spectra can be understood in terms of the distribution of eigenvalues from classes of random matrices drawn from certain classical groups, and with various collaborators, providing compelling evidence that this is so. Much of this is inspired by the work of Wigner, Mehta, and Dyson who used these same matrix models (which date back to Hermann Weyl) when modeling resonance lines of heavy nuclei (in quantum physics).

This new approach was inspired by Montgomery's work in the seventies, which determined the (Fourier transform of the) pair correlation function for pairs of zeros of the Riemann zeta-function in a limited range, and conjectured the pair correlation function in all ranges. At the time Dyson noted the analogies between Montgomery's results and conjectures, and a large body of work in quantum physics, but it was only in the late nineties that Rudnick and Sarnak were able to prove the generalization of Montgomery's results for n-level correlations. This led researchers to compute such correlation functions for many of the zeta functions of interest to number theory, and to determine the statistics of interest to physicists, and they quickly revealed much previously hidden structure. Although the computations are very suggestive, it is hard to concretely prove much. The one exception is the work of Katz and Sarnak on varieties over finite fields: starting from Deligne's great work on "equidistribution" of Frobenius eigenvalues for varieties over finite fields, they have proved that for many "families" the eigenvalues for the varieties are distributed just as the eigenvalues for certain classical groups. This is highly applicable work, which should reach into, for example, coding theory, and truly changes the limits of our understanding.

On the conjectural side, many young researchers have been using these ideas to delve into otherwise impenetrable questions, or to indicate how one might approach difficult problems. One exciting example is the very recent work of Conrey, Keating, Rubinstein and Snaith, who have shown that we would expect around x3/4 (log x)11/8 of the quadratic twists of a given elliptic curve to have rank bigger than one, a level of precision until recently unimaginable.


Last Modified:
Mar 24, 2017

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