Take the 2-minute tour ×
MathOverflow is a question and answer site for professional mathematicians. It's 100% free, no registration required.

I've always been fascinated by the fact that the classical Gauss sum has absolute value $\sqrt p$, which is exactly what we would expect if we were to interpret the Gauss sum as a random walk. In particular, I have long wondered whether this apparent link between number theory and probability theory is just a curiosity, or whether it is a symptom of a deeper connection. For example, there are many heuristic arguments in number theory that treat primes as being "random." Is there any example of such a heuristic argument that can be made rigorous by using the above observation about Gauss sums?

share|improve this question
2  
Speaking solely for myself, what I would expect if I were to interpret the (quadratic) Gauss sum as a random walk is that most of the time its absolute value would be pretty close to $\sqrt p$; I would not expect it to be, as it is, exactly $\sqrt p$ all the time. Higher order Gauss sums are maybe closer to what I'd expect of a random walk (I'm not sure if that makes them better or worse candidates for the sort of application you have in mind). –  Gerry Myerson Jan 22 '11 at 3:23
4  
I wouldn't expect exactly $\sqrt{p}$, and the expectation of the absolute value of the random walk isn't $\sqrt{p}$. The expectation of the square of a random walk (with $p$ unit steps) is $p$, but the square of the expectation is not the expectation of the square. –  Kevin O'Bryant Jan 22 '11 at 3:28

3 Answers 3

up vote 8 down vote accepted

The thing is that it is well known that for the quadratic Gauss sums, expressed as an exponential sum rather than with Legendre symbols, the path is very much not a random walk when you plot it in the complex plane. There is plenty of structure visible as approximate Cornu spirals.

Such sums are not the only Gauss sums, as I know to my cost; and the quadratic case is atypical (perhaps). But from a high-flown point of view, a Gauss sum is a special function in the theory of finite fields (like a Gamma function). There seems to be more mileage in asking about what is special about it.

share|improve this answer

Caveat lector: as an answer to Timothy's question, this is tangential at best.

Regarding Gerry and Kevin's comments, people might be interested in Section 2 of these notes of mine from an undergraduate number theory course, in which the philosophy of "almost square root error" is batted around for a while, especially with regard to the Riemann hypothesis. This is maybe my favorite set of lecture notes from this course, perhaps because I got a chance to (talk and) write excitedly about things I hardly understand: I certainly do not claim a professional level of insight here. (Indeed some regulars on this site could do far better, and I would be interested to hear their critiques.)

The upshot though is that of agreement with Gerry and Kevin: having size $\sqrt{p}$ on the nose is just a little bit better than random, but the little bit makes a big difference: to me this suggests very precise structure rather than randomness.

Please see here for another take on the subject of almost square root error in the context of the Sato-Tate Conjecture. This latter article was written at almost the same time as mine, and the author happens to be my former thesis advisor. I am reasonably sure that both of these coincidences are indeed coincidental.

share|improve this answer
    
At the start of Section 1.3 of the notes which you link to, you write that Riemann used the notation $\sigma + it$ for complex variables in his paper on the zeta-function. He never used $\sigma$. In fact the only complex numbers whose real and imaginary parts he wrote out were on the line with real part 1/2. –  KConrad Jul 24 '11 at 15:35

Actually I would think there are connections. Even if the the coincidence $\sqrt{p}$ seems ordinary, there are low-correlation sequences which owe their low correlation to Gauss sum estimates and looking from a probabilistic view point sequences, I would think there would be interpretations from a view point of uncorrelated random variables. Your starting point could be low-correlation sequences used in communications systems and beyond that I would think Nicholas Katz and Sarnak's dive into random matrices would help. http://books.google.com/books?id=wXyOPbzvowsC&printsec=frontcover&dq=inauthor:%22Nicholas+M.+Katz%22&hl=en&ei=j9IrTu24CpKnsAKG5c3DCw&sa=X&oi=book_result&ct=result&resnum=5&ved=0CD8Q6AEwBA#v=onepage&q&f=false

share|improve this answer

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.