Gaussian probability distribution of formation PBH

In summary, the parameter σ is the RMS density contrast and can be obtained from the power spectrum of density fluctuations in the early Universe. Its value depends on the cosmological model and the scale of density fluctuations being considered.
  • #1
koulbichok
3
0
Hello. If we consider PBH formation from collapse of large density perturbation in the early Universe, a mass PBH depends on density contrast as
242251

And δ must be larger then
242252
. Also we have β — an abundance of black holes, it's the ratio of the PBH energy density to the total energy density, this is defined as
242253

Where P(δ) is Gaussian, which describes a perturbation profile and hence the probability of black hole formation with current δ.
242254

So, I have a problem with σ. I know this is average value of density contrast and might depends on particle Horizon mass, but I don't know how to find σ.
 
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  • #2


Hello, thank you for bringing up this interesting topic. The parameter σ is the root mean square (RMS) density contrast and can be calculated using the power spectrum of density fluctuations in the early Universe. This power spectrum can be obtained from cosmological models and observations of the cosmic microwave background radiation. The exact value of σ depends on the specific cosmological model and the scale at which you are considering the density fluctuations. For PBH formation, it is important to consider the density fluctuations on small scales, typically corresponding to the horizon mass. I recommend consulting with a cosmologist or referring to literature on cosmological models to obtain a more precise value for σ in your specific scenario. Additionally, it is important to note that the value of σ may also be affected by other factors such as the equation of state of the early Universe and the presence of other forms of matter or energy. I hope this helps to clarify your understanding of σ and its role in PBH formation.
 

FAQ: Gaussian probability distribution of formation PBH

What is the Gaussian probability distribution of formation PBH?

The Gaussian probability distribution, also known as the normal distribution, is a mathematical function that describes the probability of a continuous variable taking on a certain value. In the context of PBH (primordial black hole) formation, it is used to model the likelihood of PBHs forming in the early universe.

How is the Gaussian probability distribution related to PBH formation?

The Gaussian probability distribution is used to model the fluctuations in the density of matter in the early universe, which is a key factor in PBH formation. According to the theory, if the density fluctuations exceed a certain threshold, PBHs can form from the collapse of matter in these regions.

What factors affect the shape of the Gaussian probability distribution for PBH formation?

The shape of the Gaussian probability distribution for PBH formation is affected by various factors, including the initial conditions of the universe, the equation of state of the early universe, and the properties of the dark matter component. Changes in these factors can lead to deviations from the standard Gaussian distribution.

Can the Gaussian probability distribution be used to predict the number of PBHs in the universe?

Yes, the Gaussian probability distribution can be used to predict the number of PBHs in the universe. By analyzing the shape and parameters of the distribution, scientists can estimate the number of PBHs that could have formed in the early universe. However, this prediction is subject to uncertainties and may vary depending on the assumptions made in the model.

How does the Gaussian probability distribution of PBH formation compare to other models?

The Gaussian probability distribution is just one of several models used to describe PBH formation. Other models, such as the lognormal distribution and the power-law distribution, have also been proposed. Each model has its own strengths and limitations, and further research is needed to determine which one best fits the observational data.

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