Statistical Physics: Quantum ideal gas

In summary, the partition function for a quantum ideal gas can only be calculated using the grand canonical ensemble due to the indistinguishability of particles. Using the microcanonical ensemble is possible if the stationary states have a small range of energies, but this is not commonly used.
  • #1
aburriu

Homework Statement


I'm reading the book about Statistical Physics from W. Nolting, specifically the chapter about quantum gas.
In the case of a classical ideal gas, we can get the state functions with the partition functions of the three ensembles (microcanonical, canonical and grand canonical). However, in the case of a quantum ideal gas, we can only apply the grand canonical ensemble. Why?

Homework Equations


The Hamilton operator for the whole system is additive:
[ tex ] H = \sum_{i=1}^N H^{(i)} [ /tex ]
where (i) denotes the particle number.
Each particle can be described by the Schrödinger equation:
[ tex ] H^{(i)} |\varphi_k^{(i)}> = \varepsilon_{k} |\varphi_k^{(i)}> [ /tex ]
where the subscript k characterizes the set of quantum numbers (n, l, ml, ms )

The Attempt at a Solution


I'm guessing it has to do with the indistinguishability of the particles. I've read something about the Fock states, but I didn't grasp the concept.
 
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  • #2
The partition function is built with the assumption that particles are distinguishable: you must be able to identify a single particle within a large collection of particles and keep track of its state. This does not agree with quantum mechanics at all.

Compare this to the grand canonical partition function: we are instead observing a small collection of particles within a much larger collection of particles, and both collections are able to exchange particles. E.g., if there are four particles currently in a given state, we can't be sure if all four were originally part of our small collection, or if some came from the much larger collection. Even though this is a very classical way to approach a collection of particles, it has the same consequences as if we had just made all of our particles indistinguishable. So this generalizes nicely in quantum mechanics.

Unfortunately, I'm not familiar enough with the microcanonical ensemble to give you a straight answer on this one, but the Wikipedia article seems to suggest that you can treat quantum systems with the microcanonical ensemble, provided the stationary states of the system have a very small range of energies.
 

1. What is statistical physics?

Statistical physics is a branch of physics that uses statistical methods to study the behavior of large systems of particles, such as atoms and molecules. It aims to understand the macroscopic properties of these systems by analyzing the microscopic behavior of their individual components.

2. What is a quantum ideal gas?

A quantum ideal gas is a theoretical model that describes a gas composed of quantum particles, such as atoms or molecules, at low temperatures and low densities. It assumes that the particles do not interact with each other and obeys quantum mechanical principles, such as the uncertainty principle.

3. How is statistical physics applied to quantum ideal gases?

In statistical physics, quantum ideal gases are described using the Bose-Einstein or Fermi-Dirac statistics, which take into account the quantum nature of the particles. These statistics are used to calculate the thermodynamic properties of the gas, such as its pressure, temperature, and entropy.

4. What is the difference between a classical and quantum ideal gas?

A classical ideal gas follows the classical laws of physics, where particles are treated as point masses that do not interact with each other. A quantum ideal gas, on the other hand, takes into account the wave-like nature of particles and their interactions through quantum mechanical principles.

5. What are some real-life applications of statistical physics and quantum ideal gases?

Statistical physics and quantum ideal gases have many applications in various fields, such as astrophysics, material science, and engineering. Some examples include understanding the behavior of gases in stars, predicting the properties of new materials, and developing new technologies, such as quantum computing.

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