Probability density function for Riemann-zeta zeros

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Curious about the expected probability distribution for the spacing between Riemann zeta zeros, of the form $s_n=\sigma+it_n$, where $\sigma=0.5$ and $t_n$ is the imaginary part of the $n$-th zero.

The mean spacing between zeros decreases slowly as the height $t_n$ goes up, somewhat confounding the issue, but taking a narrow slice of 100,000 zeros starting at the billionth zero ($t_{1000000000}$) should make that variation negligible. Here's a histogram of the spacings in that region: enter image description here

The longer upper tail precludes a normal distribution. The red curve is a best fit gamma distribution, which doesn't quite do it either. The mean spacing at the beginning of the range covered is $0.351087$ compared to $0.351073$ at the end, so that variation is small.

What is the closest distribution to model the spacing?

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Just looking at other distributions, the three-parameter Burr type XII distribution fits pretty well. Here's the result as above around $t_{1e9}$:

enter image description here

The Burr type XII pdf is:

$f(x|\alpha,c,k)={{kc\over \alpha}({x\over \alpha})^{c-1}\over{(1+({x\over a})^c)^{k+1}}}$

For the above fit,

$\alpha = 0.837947, c=2.76292$ and $k=8.68889 $

However, the best fit parameters change at other heights, e.g. at $t_{10e9}$: enter image description here

$\alpha = 0.776292, c=2.73405$ and $k=9.38153 $

Note the mean has shifted down slightly. The mean is given by:

$\mu = \alpha k\Gamma(k-1/c)\Gamma(1+1/c)/\Gamma(k+1)$

The Burr type XII distribution is used most often to study mortality, survival, failure rates and the like. I guess the interval between a zeta zero and the next could be thought of as its lifespan.

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I've done five million spacings starting from billionth (1e9) zero, and find that from the distribution I had in use Nakagami had highest log-likelihood.Five million consecutive spacings at 1e9 with Nakagami distribution fit

However, when studying spacings $\Gamma(i+n)-\Gamma(i)$ with n=2,3,4...etc. Johnson distribution is superior. This is because skewness and kurtosis of the distributions change and Johnson (SB or SU) is very flexible distribution.

First, second third and fourth spacings with Johnson SB distribtuion fits