Thermal Physics - energy, microstates, and probabilities

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SUMMARY

The discussion focuses on the mathematical methods used in thermal physics to derive the number of microstates, specifically through the equation for Ω(E). The equation presented, which involves combinatorial factors and approximations, demonstrates how to calculate the number of microstates for a system with energy E. The key insight is that when the number of states s is much smaller than r and (N-r), the last factor in the equation approaches 1, simplifying the calculation. This understanding is crucial for solving problems related to energy distributions in thermal systems.

PREREQUISITES
  • Understanding of thermal physics concepts, particularly microstates and macrostates.
  • Familiarity with combinatorial mathematics and its application in statistical mechanics.
  • Knowledge of the fundamental principles of probability as they relate to energy distributions.
  • Proficiency in mathematical methods used in physics, such as limits and approximations.
NEXT STEPS
  • Study the derivation of the Boltzmann distribution in statistical mechanics.
  • Learn about the concept of entropy and its relation to microstates.
  • Explore combinatorial methods in physics, focusing on applications in thermal systems.
  • Investigate the implications of the law of large numbers in statistical physics.
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Students and professionals in physics, particularly those specializing in thermal physics, statistical mechanics, and anyone interested in the mathematical foundations of energy distributions in physical systems.

Ascendant78
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Homework Statement


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Homework Equations


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The Attempt at a Solution


The first part I'm not worried about, but the second part is worked out in the "relevant equations" section. Honestly, it looks like more magic than a Harry Potter movie going on there to me. I'm at a loss as to what mathematical method/s are being utilized to get to that answer?
 
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I'm not sure that I understand your concern. From the equations, you can prove exactly that:

\Omega(E=(r-s) \Delta) = \Omega(E=r \Delta)[\dfrac{r^s}{(N-r)^s}] [\dfrac{ (1-\frac{1}{r}) (1-\frac{2}{r}) ... (1 - \frac{s-1}{r})}{ (1 + \frac{1}{N-r}) (1 + \frac{2}{N-r}) ... (1 + \frac{s}{N-r})}]

Then the only issue is proving that if s \ll r and s \ll (N-r), then the last factor is approximately 1.
 

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