Why is the logarithm of the number of all possible states of

In summary, the temperature of a system is defined as the amount of energy that flows from higher to lower temperatures. This is a fairly simple argument that shows that energy is flowing from higher to lower temperatures.
  • #1
Kiarash
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Temperature of a system is defined as
$$\left( \frac{\partial \ln(\Omega)}{ \partial E} \right)_{N, X_i} = \frac{1}{kT}$$Where Ω is the number of all accessible states (ways) for the system. Ω can only take discrete values. What does this mean from a mathematical perspective? Many people say we have 10^23 particles so Ω is almost continuous function of energy. Why is 10^23 a nice number but 1000 is not? When can one be sure they can differentiate ln(Omega)?

If you agree with me, do you know an alternative accurate definition for temperature?
 
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  • #2
There's no better definition of temperature. Ω is only approximately equal to the number of accessible states. I don't see why that bothers you.

When two systems are in contact with each other, energy will flow in the direction that increases the number of accessible states of the combined system. A fairly simple argument shows that if we define temperature this way, then energy is flowing from higher temperature to lower. We define Ω and T this way to be able to make this argument.
 
  • #3
@Fredrik Thank you for your reply. log[Ω] can only take discrete values; so it is ambiguous what is its derivative. I mean there is infinitely many functions that have the same values as Ω has, but they have different derivative. What does it mean when we differentiate log[Ω]; it is not trivial.
 
  • #4
OK, so the main issue is that there's more than one way to approximate a function defined on a set of integers by a function defined on an interval. I suppose the answer has to be that all reasonable ways to define what we mean by a "best" approximation (e.g. for some n, the nth-degree polynomial that's the best approximation in the least squares sense) give us functions that are empirically indistinguishable from each other.

I haven't tried to verify this, but I also don't think it's necessary. There's obviously some ambiguity in a word like "reasonable", but this is to expected in arguments that are used to find the theory that we're going to use. Such arguments don't have to be rigorous, since the goal is to guess what definitions will be useful, not to prove that the theory is right.
 
  • #5
Physicists are well-known for doing things that make mathematicians tear their hair out, but that somehow "work", anyway. :-p
 
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Related to Why is the logarithm of the number of all possible states of

1. Why is the logarithm of the number of all possible states of a system important?

The logarithm of the number of all possible states of a system is important because it helps us quantify the complexity of the system. It allows us to compare the number of possible states between different systems and understand the level of complexity involved.

2. How is the logarithm of the number of all possible states of a system calculated?

The logarithm of the number of all possible states of a system is calculated by taking the base 10 logarithm of the total number of possible states. This is expressed as log(N), where N is the number of possible states.

3. What is the relationship between the logarithm of the number of all possible states and entropy?

The logarithm of the number of all possible states is directly related to entropy, which is a measure of the disorder or randomness in a system. The higher the number of possible states, the higher the entropy and the more complex the system is considered to be.

4. Can the logarithm of the number of all possible states of a system be negative?

No, the logarithm of any number, including the number of all possible states, cannot be negative. This is because the logarithm function is only defined for positive numbers.

5. How does the logarithm of the number of all possible states explain the behavior of a system?

The logarithm of the number of all possible states provides insight into the behavior of a system by describing the range of possible states it can exist in. This helps us understand the level of complexity and randomness in the system, which can affect its behavior and dynamics.

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