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Statistical Mechanics - Physics at Oregon State University

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22 CHAPTER 1. FOUNDATION OF STATISTICAL MECHANICS.<br />

Problem 6.<br />

The <strong>at</strong>omic spin on an <strong>at</strong>om can take 2S+1 different values, si = −S, −S +<br />

1, · · · , +S − 1, +S. The total magnetic moment is given by M = N<br />

si and<br />

the total number of <strong>at</strong>oms is N. Calcul<strong>at</strong>e the multiplicity function g(N, M),<br />

counting all st<strong>at</strong>es of the system for these values N and M. Define x = M<br />

N and<br />

calcul<strong>at</strong>e g(N, x) when N becomes very large.<br />

Problem 7.<br />

Twelve physics gradu<strong>at</strong>e students go to the bookstore to buy textbooks.<br />

Eight students buy a copy of Jackson, six students buy a copy of Liboff, and<br />

two students buy no books <strong>at</strong> all. Wh<strong>at</strong> is the probability th<strong>at</strong> a student who<br />

bought a copy of Jackson also bought a copy of Liboff? Wh<strong>at</strong> is the probability<br />

th<strong>at</strong> a student who bought a copy of Liboff also bought a copy of Jackson?<br />

Problem 8.<br />

For an ideal gas we have U = 3<br />

2 NkBT , where N is the number of particles.<br />

Use the rel<strong>at</strong>ion between the entropy S(U, N) and the multiplicity function<br />

g(U, N) to determine how g(U, N) depends on U.<br />

Problem 9.<br />

The energy eigenst<strong>at</strong>es of a harmonic oscill<strong>at</strong>or are ɛn = ω(n + 1<br />

2 ) for<br />

n = 0, 1, 2, · · · Consider a system of N such oscill<strong>at</strong>ors. The total energy of this<br />

system in the st<strong>at</strong>e {n1, n2, · · · , nN } is<br />

where we have defined<br />

U =<br />

N<br />

i=1<br />

ɛni = (M + 1<br />

2 N)ω<br />

M =<br />

N<br />

i=1<br />

Calcul<strong>at</strong>e the multiplicity function g(M, N). Hint: rel<strong>at</strong>e g(M, N) to g(M, N+<br />

1) and use the identity<br />

ni<br />

m<br />

<br />

n + k n + 1 + m<br />

=<br />

n n + 1<br />

k=0<br />

Show th<strong>at</strong> for large integers g(M, N) is a narrow Gaussian distribution in<br />

x = M<br />

N .<br />

i=1

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