Normalization of spacings

In summary, the article discusses Montgomery's pair correlation conjecture, which relates to the normalized spacing between non-trivial zeros of the Riemann zeta function on the critical strip. This spacing is represented by the normalized interval length, which is calculated by multiplying the non-normalized interval length by (ln(z/(2π)))/(2π)) and then normalizing it by multiplying it by 2π/ln(T). The purpose of this normalization is to make the interval lengths fall between 0 and 1. The difference between the two factors used for normalization is not clear, and it is not explained why one is almost the reciprocal of the other. The origin of this expression is not fully understood both intuitively and mathematically
  • #1
nomadreid
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In the Wiki article on Montgomery's pair correlation conjecture https://en.wikipedia.org/wiki/Montgomery's_pair_correlation_conjecture, it is stated that the normalized spacing between one non-trivial zero γn =½+iT of the Riemann zeta function and the next γn+1 on the critical strip Re(z)= ½ is
the non-normalized interval length L= (γn+1 - γn) times (ln(z/(2π)))/(2π)) .(*)

Also in the intro it says that it is normalized by
multiplying L times 2π /ln(T). (**)
My questions are very elementary so that I can get started on understanding this; it is not a question about the conjecture or the RH per se.
[1] I am not sure what the normalization is supposed to do: make every interval
0<normalized length <1 ?
[2] I seem to have missed something in the difference between (*) and (**):
they are multiplying by two different factors, one of which is almost the reciprocal of the other. So, why the reciprocal if they want to do the same thing, and one that is answered, why almost the reciprocal (difference of a factor of 2π in the argument of the log)?
[3] Where does this expression come from: both intuitively and mathematically? It looks vaguely like an inverse of the normal function, but not quite.
[4] In the intro the author characterizes this informally as an "average" spacing. Average of what? All spacings between zeros? I am not sure how to match up this "average" with the given formulas.

Thanks.
 
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  • #2
Let me make the question simpler: when one refers to normalizing a distribution, one usually means to subtract the expected value of the population from each sample and then divide the result by the population standard deviation. OK, but how does one do this in the case of the imaginary components of the zeros on the critical strip of the Riemann zeta function if one does not know all the zeros? Does one just base it on the millions of non-trivial zeros one already knows? Thanks.
 

1. What is normalization of spacings?

Normalization of spacings is a statistical method used to adjust the spacing or distribution of data points in a dataset. It is used to make the data more comparable or easier to analyze by removing any inherent biases or variations in the spacing of the data points.

2. Why is normalization of spacings important?

Normalization of spacings is important because it allows for more accurate comparisons and analyses of data. It helps to remove any systematic errors or biases that may be present in the data, making it easier to identify patterns and trends.

3. What are the different methods of normalizing spacings?

There are several methods for normalizing spacings, including linear transformation, logarithmic transformation, and z-score transformation. Linear transformation involves rescaling the data to a new range, while logarithmic transformation involves taking the logarithm of the data. Z-score transformation involves standardizing the data by subtracting the mean and dividing by the standard deviation.

4. When should normalization of spacings be used?

Normalization of spacings should be used when analyzing data that has varying scales or units of measurement. It is also useful when comparing data from different sources or when trying to identify patterns or trends in a dataset.

5. Are there any limitations to normalization of spacings?

Yes, there are limitations to normalization of spacings. It may not be appropriate for data that is already normally distributed or for datasets with extreme outliers. It may also introduce new biases or distortions in the data if not applied correctly.

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