Gaussian vs. non-Gaussian fluctuations

In summary, the conversation discusses the concept of gaussian and non-gaussian fluctuations in the context of inflation. It is mentioned that gaussian fluctuations refer to a random distribution of photons in the CMB, while non-gaussian fluctuations suggest a preferred distribution and can be caused by topological defects in the universe. The relationship between the nature of fluctuations and the mechanism of inflation is also discussed, with examples such as multiple fields, non-slow roll evolution, and non-trivial vacuum/initial state being mentioned as potential causes of non-gaussianity. It is also noted that non-gaussianity can arise in the distribution of the amplitude of fluctuations without violating isotropy.
  • #1
Einj
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Hello everyone,
I know this is a very basic question but I was wondering, in the context of inflation, what does it mean to have gaussian or non-gaussian fluctuations.
First of all, are we talking about the fluctuations of the inflation?
Second of all, how is the nature of the fluctuations related to the mechanism of inflation, say e.g. the inflaton potential?

Thanks!
 
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  • #2
Gaussian, wrt to the CMB, refers to a distribution of CMB photons that is consistent with a gaussian probability curve [i.e., random]. A non-guassian distribution suggests a preferred distribution of CMB photons [i.e., corresponding to topological defects in the universe]. Most cosmologists are suspicious of non gaussianity because it violates the cosmological principle.
 
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  • #3
Thanks for the reply! How can non-gaussianity be generated at the level of inflation?
 
  • #5
Einj said:
Thanks for the reply! How can non-gaussianity be generated at the level of inflation?
All sorts of ways: multiple fields, non-slow roll evolution, strong higher-order couplings, non-trivial vacuum/initial state, non-canonical kinetic terms (e.g. DBI inflation), etc...
 
  • #6
Chronos said:
Gaussian, wrt to the CMB, refers to a distribution of CMB photons that is consistent with a gaussian probability curve [i.e., random]. A non-guassian distribution suggests a preferred distribution of CMB photons [i.e., corresponding to topological defects in the universe]. Most cosmologists are suspicious of non gaussianity because it violates the cosmological principle.
Nongaussianity can also arise in the distribution of the amplitude of fluctuations (the [itex]a_{\ell m}[/itex]) in the temperature power spectrum. I believe this can happen without violating isotropy.
 

Related to Gaussian vs. non-Gaussian fluctuations

1. What is the difference between Gaussian and non-Gaussian fluctuations?

Gaussian fluctuations refer to the random variation of a physical quantity that follows a normal distribution, also known as a bell curve. This means that most data points are clustered around the average value, with fewer data points occurring at the extreme ends. Non-Gaussian fluctuations, on the other hand, do not follow a bell curve and can have a more complicated distribution.

2. Why do we care about Gaussian vs. non-Gaussian fluctuations?

Understanding the type of fluctuations present in a system is important for accurately modeling and predicting its behavior. Many physical phenomena, such as noise in electronic circuits or fluctuations in stock prices, are described by Gaussian or non-Gaussian distributions. Knowing which type of distribution to use can improve the accuracy of our predictions and help us make better decisions.

3. Can non-Gaussian fluctuations occur in a system with only a few variables?

Yes, non-Gaussian fluctuations can occur in systems with only a few variables. This is because the distribution of fluctuations is determined not only by the number of variables, but also by the interactions between them. Nonlinear interactions between variables can result in non-Gaussian fluctuations, even in systems with a small number of variables.

4. Are Gaussian fluctuations always desirable in a system?

Not necessarily. While Gaussian fluctuations are often easier to model and analyze, non-Gaussian fluctuations can provide valuable insights into the underlying dynamics of a system. For example, non-Gaussian fluctuations can indicate the presence of complex interactions or critical phenomena in a system.

5. How can we determine if a system exhibits Gaussian or non-Gaussian fluctuations?

There are various statistical tests that can be used to determine the type of fluctuations present in a system. One common method is to calculate the kurtosis, which measures the "peakedness" of a distribution. A high kurtosis value indicates non-Gaussian fluctuations, while a low kurtosis value suggests Gaussian fluctuations. Additionally, visual inspection of data can also provide insights into the type of fluctuations present.

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