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Quantum Distribution Functions for Bosons, Fermions, & otherwise Objects

Quantum Distribution Functions for Bosons, Fermions, & otherwise Objects. Formalism: Appendix C Ch 11.1-11.4 Applications: Ch 11.5-11.11. http://www.slimy.com/~steuard/teaching/tutorials/Lagrange.html http://www.cs.berkeley.edu/~klein/papers/lagrange-multipliers.pdf.

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Quantum Distribution Functions for Bosons, Fermions, & otherwise Objects

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  1. Quantum Distribution FunctionsforBosons, Fermions, & otherwise Objects Formalism: Appendix C Ch 11.1-11.4 Applications: Ch 11.5-11.11 http://www.slimy.com/~steuard/teaching/tutorials/Lagrange.html http://www.cs.berkeley.edu/~klein/papers/lagrange-multipliers.pdf

  2. FORMALISM OUTLINE • Quick Basic Probability Ideas • Boltzmann Distribution Fn: App C • Comparison of Ytot* Ytot for Bosons & Fermions: 11-2 • Detailed Balance: 11-3 • w/o special requirements • bosons • fermions • Distribution Fns: 11-4 • various interpretations of the ea factor

  3. How to do the Lagrange Multiplier technique • function f(x1, x2, …) • Constraint g(x1, x2, …)=c • Form new function F = f + l (g-c) • Maximize it wrt x1, x2, … • Choose something for l based upon other information Maximize a function subject to certain constraints on the dependent variables

  4. Example Maximize f = xy subject to constraint x2+y2=1 F = xy +l (x2+y2-1) dF/dx = y + 2lx = 0  -2l = y/x y/x = x/y x2 = y2 dF/dy = x + 2ly = 0  -2l = x/y x2+y2=1  x2+x2=1  2x2 = 1  x=0.707

  5. Example Minimize f = xy subject to constraint x2+y2=1 F = xy +l (x2+y2-1) (y+2lx) + (x+2ly) = 0 dF/dx = y + 2lx (1+2l)y + (1+2l)x = 0 dF/dy = x + 2ly y = -x x2+y2=1  x2+x2=1  2x2 = 1  x=±0.707

  6. BACKGROUND PROBABILITY IDEAS

  7. Given N=5 objects and p=1 bin; How many ways can one put n=2 objects in the bin ? (in a definite order)

  8. Given N=5 objects and p=1 bin; How many ways can one put n=2 objects in the bin ? (without regard to order) Note that after filling this box, there are (N-n) objects unused.

  9. Given N total objects and p total bins; How many ways can one put n1 objects in bin #1 n2 objects in bin #2 n3 objects in bin #3 * * * (without regard to order) * * *

  10. * * * n1 n2 n3

  11. Probability of finding a particular arrangement:

  12. Probability Summary N total objects p total states np * * * n5 n4 n3 n2 n1

  13. BOLTZMANN DISTRIBUTION Probability of finding a particular energy e subject to the constraint that there are N total particles and Etot energy

  14. p * * * 2 1

  15. p * * * 2 1

  16. Sterling’s Formula The second term is largest by at least a parsec

  17. note: reset 1 + a a

  18. To discover the expression for the normalization constant OK, so what is b = ?

  19. Temperature is defined in terms of the average kinetic energy

  20. Boltzmann Distribution Probability of finding a particular energy e subject to the constraints that there are N total particles and fixed Etot ea = 1/kT

  21. How to normalize theBoltzmann Distribution

  22. Many, many possible states, closely spaced in energy

  23. Finite number of states,but no restriction on filling

  24. Finite number of states,but with restriction on filling

  25. Many, many closely-spaced states,but with restriction on filling

  26. 11-2 COMPARING PROBABILITIES Y*Yfor Indistinguishable Boson/Fermion Particles to thosewithout worrying about B/F requirements OR what do the B/F requirements do to probabilities?

  27. Distinguishable Particle Probabilities One particle in a state b Ytot = Yb(1) Prob = Ytot* Ytot = Yb(1) *Yb(1) = 1 Two particles in a state b Ytot = Yb(1) Yb(2) Prob = Yb(1)*Yb(1) Yb(2)*Yb(2) = 1 Three particles in a state b Ytot = Yb(1) Yb(2) Yb(3) Prob = Yb(1)*Yb(1) Yb(2)*Yb(2) Yb(3)*Yb(3) = 1 So what ? Nothing special happens here…..

  28. Indistinguishable Boson Probabilities One particle in a state b Ytot = Yb(1) Prob = Ytot* Ytot = Yb(1) *Yb(1) = 1 Two particles in a state b Ytot = [Yb(1) Yb(2) + Yb(2) Yb(1)] = 2 Yb(1) Yb(2) Prob = | 2 Yb(1) Yb(2) |2 = 2 = 2! Three particles in a state b Ytot = [ Yb(1) Yb(2) Yb(3) + … ] Prob = | 6 Yb(1) Yb(2) Yb(3) |2 = 6 = 3! If there are already n bosons in a state, the probability of one more joining them is enhanced by (1+n) than what the prob would be w/o indistinguishability requirements

  29. Indistinguishable Fermion Probabilities One particle in a state b Ytot = Yb(1) Prob = Ytot* Ytot = Yb(1) *Yb(1) = 1 Two particles in a state b Ytot = [Yb(1) Yb(2) - Yb(2) Yb(1)] = 0 Prob = 0 If there are already n fermions in a state, the probability of one more joining them is enhanced by (1-n) than what the prob would be w/o indistinguishability requirements

  30. DETAILEDBALANCEER 11-4

  31. Principle of Detailed Balance For two states of a system with fixed total energy, n2 e2 n1 e1 Where the particles can jump between states by some unknown mechanism, Rate of upward going transitions = Rate of downward going transitions n1 Rate 12 = n2 Rate 21 transitions/sec per particle

  32. Detailed Balance distinguishable particles(but with no other special requirements) Since by the Boltzmann distribution n ~ e-e/kT Gives us the ratio of the two transition rates

  33. Detailed Balance indistinguishable bosons

  34. L & R sides are unrelated except for Temp

  35. !

  36. Bose distribution function Probable # bosons of an energy e in a system of fixed total energy at a temperature T

  37. Detailed Balance indistinguishable fermions ( short derivation )

  38. Fermi distribution function Probable # fermions of an energy e in a system of fixed total energy at a temperature T

  39. SUMMARYofDistribution Functionsandwhat is this a thing?

  40. Collected Distribution Functions

  41. Normalization Interpretation * * This interpretation may not be so useful for Bose & Fermi distributions * For Ntot free particles strictly confined to a 3-D region of space of volume V.

  42. Chemical Potential Interpretation m = - a kT A uniform description for all three distributions. Used for Bose distribution.

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