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Random Matrices. Hieu D. Nguyen Rowan University. Rowan Math Seminar 12-10-03. Historical Motivation. Statistics of Nuclear Energy Levels. - Excited states of an atomic nucleus. Level Spacings. – Successive energy levels. – Nearest-neighbor level spacings. Wigner’s Surmise.

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Random Matrices

Hieu D. Nguyen

Rowan University

Rowan Math Seminar

12-10-03


Historical Motivation

Statistics of Nuclear Energy Levels

- Excited states of an atomic nucleus


Level Spacings

– Successive energy levels

– Nearest-neighbor level spacings




Basic Concepts in Probability and Statistics

Statistics

– Data set of values

– Mean

– Variance

Probability

– Continuous random variable on [a,b]

– Probability density function (p.d.f.)

– Total probability equals 1


Examples of P.D.F.

– Probability of choosing x

between a and b

– Mean

– Variance


Wigner’s Surmise

Notation

– Successive energy levels

– Nearest-neighbor level spacings

– Mean spacing

– Relative spacings

Wigner’s P.D.F. for Relative Spacings


Are Nuclear Energy Levels Random?

Poisson Distribution (Random Levels)

Distribution of 1000 random numbers in [0,1]




Distribution of Zeros of Riemann Zeta Function

Fun Facts

1.

is irrational (Apery’s constant)

2.

can be analytically continued to all

3.

4.

(functional equation)

5. Zeros of

Trivial Zeros:

(critical line)

Non-Trivial Zeros (RH):


Distribution of Zeros and Their Spacings

First 105 Zeros

First 200 Zeros


Asymptotic Behavior of Spacings for Large Zeros

Question: Is there a Hermitian matrix H which has

the zeros of as its eigenvalues?


Model of The Nucleus

Quantum Mechanics

– Hamiltonian (Hermitian operator)

– Bound state (eigenfunction)

– Energy level (eigenvalue)

Statistical Approach

– Hermitian matrix

(Matrix eigenvalue problem)


Basics Concepts in Linear Algebra

Matrices

n x n square matrix

Special Matrices

Symmetric:

Hermitian:

Orthogonal:


Eigensystems

– Eigenvalue

– Eigenvector

Similarity Transformations (Conjugation)

Diagonalization


Gaussian Orthogonal Ensembles (GOE)

– random N x N real symmetric matrix

Distribution of eigenvalues of 200 real symmetric

matrices of size 5 x 5

Level spacing

Eigenvalues

Entries of each matrix is chosen randomly and

independently from a Gaussian distribution with


500 matrices of size 5 x 5

1000 matrices of size 5 x 5


10 x 10 matrices

20 x 20 matrices


Why Gaussian Distribution?

Uniform P.D.F.

Gaussian P.D.F.


Statistical Model for GOE

– random N x N real symmetric matrix

Assumptions

  • Probability of choosing H is invariant under

  • orthogonal transformations

  • 2. Entries of H are statistically independent

Joint Probability Density Function (j.p.d.f.) for H

– p.d.f. for choosing

– j.p.d.f. for choosing


Lemma (Weyl, 1946)

All invariant functions of an (N x N) matrix H

under nonsingular similarity transformations

can be expressed in terms of the traces of the first N

powers of H.

Corollary

Assumption 1 implies that P(H) can be

expressed in terms of tr(H), tr(H2), …, tr(HN).


Observation

(Sum of eigenvalues of H)



Now, P(H) being invariant under U means that its

derivative should vanish:


We now apply (*) to the equation immediately above

to ‘separate variables’, i.e. divide it into

groups of expressions which depend on mutually

exclusive sets of variables:

It follows that say

(constant)


It can be proven that Ck = 0. This allows us to

separate variables once again:

(constant)

Solving these differential equations yields our

desired result:

(Gaussian)


Theorem

Assumption 2 implies that P(H) can be expressed

in terms of tr(H) and tr(H2), i.e.


J.P.D.F. for the Eigenvalues of H

Change of variables for j.p.d.f.



Lemma

Corollary

Standard Form


Density of Eigenvalues

Level Density

We define the probability density of finding a level

(regardless of labeling) around x, the positions of

the remaining levels being unobserved, to be

Asymptotic Behavior for Large N (Wigner, 1950’s)

20 x 20 matrices


Two-Point Correlation

We define the probability density of finding a level

(regardless of labeling) around each of the points x1

and x2, the positions of the remaining levels being

unobserved, to be

We define the probability density for finding

two consecutive levels inside an interval

to be


Level Spacings

Limiting Behavior (Normalized)

We define the probability density that in an infinite

series of eigenvalues (with mean spacing unity)

an interval of length 2t contains exactly two levels

at positions around the points y1 and y2 to be

P.D.F. of Level Spacings

We define the probability density of finding a

level spacing s = 2t between two successive levels

y1 = -t and y2 = t to be


Multiple Integration of

Key Idea

as a determinant:

Write

(Oscillator wave functions)

(Hermite polynomials)


Harmonic Oscillator (Electron in a Box)

NOTE: Energy levels are quantized (discrete)


Formula for Level Spacings?

  • Eigenvalues of a matrix whose entries are

  • integrals of functions involving the oscillator

  • wave functions

The derivation of this formula very complicated!



Random Matrices and Solitons

Korteweg-de Vries (KdV) equation

Soliton Solutions


Cauchy Matrices

- Cauchy matrices are symmetric and positive definite

Eigenvalues of A:

Logarithms of Eigenvalues:


Level Spacings of Eigenvalues of Cauchy Matrices

Assumption

The values kn are chosen randomly and independently

on the interval [0,1] using a uniform distribution

1000 matrices of size 4 x 4

Log distribution

Distribution of spacings


Level Spacings

First-Order Log Spacings

1000 matrices of size 4 x 4

10,000 matrices of size 4 x 4

Second-Order Log Spacings


Open Problem

Mathematically describe the distributions of

these first- and higher-order log spacings


References

1. Random Matrices, M. L. Mehta, Academic Press, 1991.


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