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Let $\mu(n)$ be the Möbius function and $S(x)$ be the number of positive integers $n \le x$ such that

$$ \sum_{r = 1}^{n-1} \mu(r)\gcd(n,r) = 0 $$

My experimental data for $n \le 6 \times 10^5 $seems to suggest that the number of solutions $\le x$ is growing at about

$$ S(x) \sim (\zeta(2)-1)\sqrt{x} $$ Is there any explanation for this? The square may come from the growth rate of the Mertens function $M(n) = \sum_{r \le n}\mu(r)$ while the appearance $\zeta(2)$ may be due to the fact that it appears in many sums involving the Pillai function $P(n) = \sum_{r \le n} \gcd(n,r)$.

I also observed that $200$ out of the $230$ zeroes for $n \le 1.3 \times 10^5$, were primes which indicate that the zeroes might be dominated by primes. For a prime $p$, $\sum_{r = 1}^{p-1} \mu(r)\gcd(p,r) = M(p-1)$ so I guess it is more likely that a sequence of $\pm 1$ adds to to $0$ than a sequence of integers of higher absolute value.

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Update: Increased graph for the number of zeroes from $1.5 \times 10^5$ to $6 \times 10^5$

I have posted this in MO since it is unanswered in MSE.

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    $\begingroup$ I deleted my answer. I stated this inequality $$ \sum_{r = 1}^n \mu(r) \leq \sum_{r = 1}^n \mu(r)\gcd(n,r) \leq n \sum_{r = 1}^n \mu(r). $$ It were true then the fact that Mertens function has infinitely many integral zeroes would prove your claim. But the inequality is false. I was careless. $\endgroup$ – Parthiv Basu Jul 18 at 15:20
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    $\begingroup$ I doubt we can answer it, your sequence $a_n = \sum_{r = 1}^{n-1} \mu(r)\gcd(n,r)$ isn't directly related to $\zeta(s)$ and up to approximations we can only estimate the changes of signs, not the number of times it is $0$ $\endgroup$ – reuns Jul 18 at 20:14
  • $\begingroup$ I guess I should post it in MO. $\endgroup$ – Nilotpal Kanti Sinha Jul 19 at 4:49
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Here's an extended comment with some observations.

Quick summary is, I don't think the hypothesis is correct as $S(x)/\sqrt{x}$ starts to drop off as $x$ increases beyond $1,000,000$.

One way of thinking of this is in terms of $\mu(r)$ being "randomly" $\pm1$ for square-free $r$, so the cumulative sum of $\mu(r)$ would be like a random walk ... except the $\mu(r)$ will tend to cancel more frequently as, eg, $\mu(r)$ and $\mu(2r)$ will cancel for odd $r$. This would explain that $S(x)$ increases at a rate proportional to $\sqrt{x}$: the variance of the sum $\sum_{r=1}^n \mu(r)$ would have variance proportional to $n$ and thus a likelihood proportional to $1/\sqrt{n}$ of being zero. However, using this in a naive manner does not seem to work as I'll explain later, however it may still give some indications as to what is going on.

As is noted in the question, a large portion of the zeroes are for $n$ prime. This seems natural as all the terms in the sum then has $\gcd(r,n)=1$ and thus adds less variance. If $n$ has large proper divisors $d|n$, values of $r$ which are multiples of $d$ add more variance as they are multiplied by $d$.

However, if we look at the zeroes which are not prime, many of them are multiples of $3$: often $n=3p$ for some prime $p$. This is also natural, as the terms for multiples of $p$, $r=p$ and $r=2p$, cancel out.

There are more zeroes than just for $n=p$ and $n=3p$, but they tend to have in common that the partial sums for each divisor (ie for each different value of $\gcd(r,n)$) tends to be zero or close to zero.

It is not clear to me how the relative density of the different kinds of zeroes---primes, 3 times primes, and other composites---changes as $n$ increases. It seems to be fairly constant, perhaps with the "other composites" class falling slightly, although I find the numbers hard to judge. If that is the case, the number of zeroes for $n$ prime should be an indication of how fast $S(x)$ increases. However, if the likelihood of a prime $n$ being a zero is proportional to $1/\sqrt{n}$ and the density of primes is $~1/\ln n$, this should make $S(x)$ increase proportional to $\sqrt{x}/\ln x$.

I've run computations up to $n=5,000,000$ using the same formula as provided by Greg Martin at MathOverflow which allows partial sums per divisor to be cached for efficient computation: went reasonably fast even in Sage/Python.

What seems clear is that the ratio $S(x)/\sqrt{x}$ starts to drop as $x$ increases past $1,000,000$: for $x>2,500,000$, the ratio seems to stay below $0.5$, and I suspect it will keep dropping.

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  • $\begingroup$ Thanks for the in-depth heuristics and I agree with your analysis. I have stopped computing after $n = 600,000$ so did not observed this fall after $n > 1,000,000$. In my Sagemath implementation it took be a few days to compute for $n = 600,000$ so your code is definitely faster. Would it be possible for you to share your code and the data so that I can run some computations for higher $n$. $\endgroup$ – Nilotpal Kanti Sinha Jul 31 at 4:47
  • $\begingroup$ Using your heuristics, I fitted a curve of the form $\frac{a\sqrt{x}}{\log x}$ the data for $n \le 600000$ and the best fit within only this much data is with $a = 7.7569$ and the fit is only very slightly worse than the curve mentioned in the question. So I guess for larger values of $n$ an estimate of this form with a sharper value of $a$ would me more accurate. $\endgroup$ – Nilotpal Kanti Sinha Jul 31 at 5:48
  • $\begingroup$ @NilotpalKantiSinha: Yes, the simple heuristic doesn't work very well. I suspect one reason is that $\mu$ isn't "random" enough, but instead cancels itself out more systematically. Realise I didn't really write much about that...will revisit my posting when I get a second look at the results. $\endgroup$ – Einar Rødland Jul 31 at 8:17

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