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Let be the divisor function. A classical application of the Dirichlet hyperbola method gives the asymptotic
where denotes the estimate as . Much better error estimates are possible here, but we will not focus on the lower order terms in this discussion. For somewhat idiosyncratic reasons I will interpret this estimate (and the other analytic number theory estimates discussed here) through the probabilistic lens. Namely, if is a random number selected uniformly between and , then the above estimate can be written as
that is to say the random variable has mean approximately . (But, somewhat paradoxically, this is not the median or mode behaviour of this random variable, which instead concentrates near , basically thanks to the Hardy-Ramanujan theorem.)
Now we turn to the pair correlations for a fixed positive integer . There is a classical computation of Ingham that shows that
The error term in (2) has been refined by many subsequent authors, as has the uniformity of the estimates in the aspect, as these topics are related to other questions in analytic number theory, such as fourth moment estimates for the Riemann zeta function; but we will not consider these more subtle features of the estimate here. However, we will look at the next term in the asymptotic expansion for (2) below the fold.
Using our probabilistic lens, the estimate (2) can be written as
From (1) (and the asymptotic negligibility of the shift by ) we see that the random variables and both have a mean of , so the additional factor of represents some arithmetic coupling between the two random variables.
Ingham’s formula can be established in a number of ways. Firstly, one can expand out and use the hyperbola method (splitting into the cases and and removing the overlap). If one does so, one soon arrives at the task of having to estimate sums of the form
for various . For much less than this can be achieved using a further application of the hyperbola method, but for comparable to things get a bit more complicated, necessitating the use of non-trivial estimates on Kloosterman sums in order to obtain satisfactory control on error terms. A more modern approach proceeds using automorphic form methods, as discussed in this previous post. A third approach, which unfortunately is only heuristic at the current level of technology, is to apply the Hardy-Littlewood circle method (discussed in this previous post) to express (2) in terms of exponential sums for various frequencies . The contribution of “major arc” can be computed after a moderately lengthy calculation which yields the right-hand side of (2) (as well as the correct lower order terms that are currently being suppressed), but there does not appear to be an easy way to show directly that the “minor arc” contributions are of lower order, although the methods discussed previously do indirectly show that this is ultimately the case.
Each of the methods outlined above requires a fair amount of calculation, and it is not obvious while performing them that the factor will emerge at the end. One can at least explain the as a normalisation constant needed to balance the factor (at a heuristic level, at least). To see this through our probabilistic lens, introduce an independent copy of , then
using symmetry to order (discarding the diagonal case ) and making the change of variables , we see that (4) is heuristically consistent with (3) as long as the asymptotic mean of in is equal to . (This argument is not rigorous because there was an implicit interchange of limits present, but still gives a good heuristic “sanity check” of Ingham’s formula.) Indeed, if denotes the asymptotic mean in , then we have (heuristically at least)
and we obtain the desired consistency after multiplying by .
This still however does not explain the presence of the factor. Intuitively it is reasonable that if has many prime factors, and has a lot of factors, then will have slightly more factors than average, because any common factor to and will automatically be acquired by . But how to quantify this effect?
One heuristic way to proceed is through analysis of local factors. Observe from the fundamental theorem of arithmetic that we can factor
where the product is over all primes , and is the local version of at (which in this case, is just one plus the –valuation of : ). Note that all but finitely many of the terms in this product will equal , so the infinite product is well-defined. In a similar fashion, we can factor
(or in terms of valuations, ). Heuristically, the Chinese remainder theorem suggests that the various factors behave like independent random variables, and so the correlation between and should approximately decouple into the product of correlations between the local factors and . And indeed we do have the following local version of Ingham’s asymptotics:
From the Euler formula
we see that
and so one can “explain” the arithmetic factor in Ingham’s asymptotic as the product of the arithmetic factors in the (much easier) local Ingham asymptotics. Unfortunately we have the usual “local-global” problem in that we do not know how to rigorously derive the global asymptotic from the local ones; this problem is essentially the same issue as the problem of controlling the minor arc contributions in the circle method, but phrased in “physical space” language rather than “frequency space”.
Remark 2 The relation between the local means and the global mean can also be seen heuristically through the application
Let us now prove this proposition. One could brute-force the computations by observing that for any fixed , the valuation is equal to with probability , and with a little more effort one can also compute the joint distribution of and , at which point the proposition reduces to the calculation of various variants of the geometric series. I however find it cleaner to proceed in a more recursive fashion (similar to how one can prove the geometric series formula by induction); this will also make visible the vague intuition mentioned previously about how common factors of and force to have a factor also.
It is first convenient to get rid of error terms by observing that in the limit , the random variable converges vaguely to a uniform random variable on the profinite integers , or more precisely that the pair converges vaguely to . Because of this (and because of the easily verified uniform integrability properties of and their powers), it suffices to establish the exact formulae
We begin with (5). Observe that is coprime to with probability , in which case is equal to . Conditioning to the complementary probability event that is divisible by , we can factor where is also uniformly distributed over the profinite integers, in which event we have . We arrive at the identity
As and have the same distribution, the quantities and are equal, and (5) follows by a brief amount of high-school algebra.
We use a similar method to treat (6). First treat the case when is coprime to . Then we see that with probability , and are simultaneously coprime to , in which case . Furthermore, with probability , is divisible by and is not; in which case we can write as before, with and . Finally, in the remaining event with probability , is divisible by and is not; we can then write , so that and . Putting all this together, we obtain
Now suppose that is divisible by , thus for some integer . Then with probability , and are simultaneously coprime to , in which case . In the remaining event, we can write , and then and . Putting all this together we have
which by (5) (and replacing by ) leads to the recursive relation
and (6) then follows by induction on the number of powers of .
for certain complicated but explicit coefficients . For instance, is given by the formula
where is the Euler-Mascheroni constant,
The formula for is similar but even more complicated. The error term was improved by Heath-Brown to ; it is conjectured (for instance by Conrey and Gonek) that one in fact has square root cancellation here, but this is well out of reach of current methods.
These lower order terms are traditionally computed either from a Dirichlet series approach (using Perron’s formula) or a circle method approach. It turns out that a refinement of the above heuristics can also predict these lower order terms, thus keeping the calculation purely in physical space as opposed to the “multiplicative frequency space” of the Dirichlet series approach, or the “additive frequency space” of the circle method, although the computations are arguably as messy as the latter computations for the purposes of working out the lower order terms. We illustrate this just for the term below the fold.
In analytic number theory, an arithmetic function is simply a function from the natural numbers to the real or complex numbers. (One occasionally also considers arithmetic functions taking values in more general rings than or , as in this previous blog post, but we will restrict attention here to the classical situation of real ofr complex arithmetic functions.) Experience has shown that a particularly tractable and relevant class of arithmetic functions for analytic number theory are the multiplicative functions, which are arithmetic functions with the additional property that
whenever are coprime. (One also considers arithmetic functions, such as the logarithm function or the von Mangoldt function, that are not genuinely multiplicative, but interact closely with multiplicative functions, and can be viewed as “derived” versions of multiplicative functions; see this previous post.) A typical example of a multiplicative function is the divisor function
that counts the number of divisors of a natural number . (The divisor function is also denoted in the literature.) The study of asymptotic behaviour of multiplicative functions (and their relatives) is known as multiplicative number theory, and is a basic cornerstone of modern analytic number theory.
There are various approaches to multiplicative number theory, each of which focuses on different asymptotic statistics of arithmetic functions . In elementary multiplicative number theory, which is the focus of this set of notes, particular emphasis is given on the following two statistics of a given arithmetic function :
- The summatory functions
of an arithmetic function , as well as the associated natural density
(if it exists).
- The logarithmic sums
of an arithmetic function , as well as the associated logarithmic density
(if it exists).
A classical case of interest is when is an indicator function of some set of natural numbers, in which case we also refer to the natural or logarithmic density of as the natural or logarithmic density of respectively. However, in analytic number theory it is usually more convenient to replace such indicator functions with other related functions that have better multiplicative properties. For instance, the indicator function of the primes is often replaced with the von Mangoldt function .
Typically, the logarithmic sums are relatively easy to control, but the summatory functions require more effort in order to obtain satisfactory estimates; see Exercise 7 below.
If an arithmetic function is multiplicative (or closely related to a multiplicative function), then there is an important further statistic on an arithmetic function beyond the summatory function and the logarithmic sum, namely the Dirichlet series
for various real or complex numbers . Under the hypothesis (3), this series is absolutely convergent for real numbers , or more generally for complex numbers with . As we will see below the fold, when is multiplicative then the Dirichlet series enjoys an important Euler product factorisation which has many consequences for analytic number theory.
In the elementary approach to multiplicative number theory presented in this set of notes, we consider Dirichlet series only for real numbers (and focusing particularly on the asymptotic behaviour as ); in later notes we will focus instead on the important complex-analytic approach to multiplicative number theory, in which the Dirichlet series (4) play a central role, and are defined not only for complex numbers with large real part, but are often extended analytically or meromorphically to the rest of the complex plane as well.
Remark 1 The elementary and complex-analytic approaches to multiplicative number theory are the two classical approaches to the subject. One could also consider a more “Fourier-analytic” approach, in which one studies convolution-type statistics such as
as for various cutoff functions , such as smooth, compactly supported functions. See for instance this previous blog post for an instance of such an approach. Another related approach is the “pretentious” approach to multiplicative number theory currently being developed by Granville-Soundararajan and their collaborators. We will occasionally make reference to these more modern approaches in these notes, but will primarily focus on the classical approaches.
To reverse the process and derive control on summatory functions or logarithmic sums starting from control of Dirichlet series is trickier, and usually requires one to allow to be complex-valued rather than real-valued if one wants to obtain really accurate estimates; we will return to this point in subsequent notes. However, there is a cheap way to get upper bounds on such sums, known as Rankin’s trick, which we will discuss later in these notes.
The basic strategy of elementary multiplicative theory is to first gather useful estimates on the statistics of “smooth” or “non-oscillatory” functions, such as the constant function , the harmonic function , or the logarithm function ; one also considers the statistics of periodic functions such as Dirichlet characters. These functions can be understood without any multiplicative number theory, using basic tools from real analysis such as the (quantitative version of the) integral test or summation by parts. Once one understands the statistics of these basic functions, one can then move on to statistics of more arithmetically interesting functions, such as the divisor function (2) or the von Mangoldt function that we will discuss below. A key tool to relate these functions to each other is that of Dirichlet convolution, which is an operation that interacts well with summatory functions, logarithmic sums, and particularly well with Dirichlet series.
One of the basic problems in analytic number theory is to obtain bounds and asymptotics for sums of the form
in the limit , where ranges over natural numbers less than , and is some arithmetic function of number-theoretic interest. (It is also often convenient to replace this sharply truncated sum with a smoother sum such as , but we will not discuss this technicality here.) For instance, the prime number theorem is equivalent to the assertion
It is thus of interest to develop techniques to estimate such sums . Of course, the difficulty of this task depends on how “nice” the function is. The functions that come up in number theory lie on a broad spectrum of “niceness”, with some particularly nice functions being quite easy to sum, and some being insanely difficult.
At the easiest end of the spectrum are those functions that exhibit some sort of regularity or “smoothness”. Examples of smoothness include “Archimedean” smoothness, in which is the restriction of some smooth function from the reals to the natural numbers, and the derivatives of are well controlled. A typical example is
One can already get quite good bounds on this quantity by comparison with the integral , namely
with sharper bounds available by using tools such as the Euler-Maclaurin formula (see this blog post). Exponentiating such asymptotics, incidentally, leads to one of the standard proofs of Stirling’s formula (as discussed in this blog post).
One can also consider “non-Archimedean” notions of smoothness, such as periodicity relative to a small period . Indeed, if is periodic with period (and is thus essentially a function on the cyclic group ), then one has the easy bound
This is a good estimate when is much smaller than , but as approaches in magnitude, the error term begins to overwhelm the main term , and one needs much more delicate information on the fractional part of in order to obtain good estimates at this point.
One can also consider functions which combine “Archimedean” and “non-Archimedean” smoothness into an “adelic” smoothness. We will not define this term precisely here (though the concept of a Schwartz-Bruhat function is one way to capture this sort of concept), but a typical example might be
where is periodic with some small period . By using techniques such as summation by parts, one can estimate such sums using the techniques used to estimate sums of periodic functions or functions with (Archimedean) smoothness.
Another class of functions that is reasonably well controlled are the multiplicative functions, in which whenever are coprime. Here, one can use the powerful techniques of multiplicative number theory, for instance by working with the Dirichlet series
which are clearly related to the partial sums (essentially via the Mellin transform, a cousin of the Fourier and Laplace transforms); for this post we ignore the (important) issue of how to make sense of this series when it is not absolutely convergent (but see this previous blog post for more discussion). A primary reason that this technique is effective is that the Dirichlet series of a multiplicative function factorises as an Euler product
One also obtains similar types of representations for functions that are not quite multiplicative, but are closely related to multiplicative functions, such as the von Mangoldt function (whose Dirichlet series is not given by an Euler product, but instead by the logarithmic derivative of an Euler product).
Moving another notch along the spectrum between well-controlled and ill-controlled functions, one can consider functions that are divisor sums such as
for some other arithmetic function , and some level . This is a linear combination of periodic functions and is thus technically periodic in (with period equal to the least common multiple of all the numbers from to ), but in practice this periodic is far too large to be useful (except for extremely small levels , e.g. ). Nevertheless, we can still control the sum simply by rearranging the summation:
and thus by (1) one can bound this by the sum of a main term and an error term . As long as the level is significantly less than , one may expect the main term to dominate, and one can often estimate this term by a variety of techniques (for instance, if is multiplicative, then multiplicative number theory techniques are quite effective, as mentioned previously). Similarly for other slight variants of divisor sums, such as expressions of the form
or expressions of the form
where each is periodic with period .
One of the simplest examples of this comes when estimating the divisor function
which counts the number of divisors up to . This is a multiplicative function, and is therefore most efficiently estimated using the techniques of multiplicative number theory; but for reasons that will become clearer later, let us “forget” the multiplicative structure and estimate the above sum by more elementary methods. By applying the preceding method, we see that
Here, we are (barely) able to keep the error term smaller than the main term; this is right at the edge of the divisor sum method, because the level in this case is equal to . Unfortunately, at this high choice of level, it is not always possible to always keep the error term under control like this. For instance, if one wishes to use the standard divisor sum representation
where is the Möbius function, to compute , then one ends up looking at
From Dirichlet series methods, it is not difficult to establish the identities
in the sense of conditionally convergent series. Assuming one can justify this (which, ultimately, requires one to exclude zeroes of the Riemann zeta function on the line , as discussed in this previous post), one is eventually left with the estimate , which is useless as a lower bound (and recovers only the classical Chebyshev estimate as the upper bound). The inefficiency here when compared to the situation with the divisor function can be attributed to the signed nature of the Möbius function , which causes some cancellation in the divisor sum expansion that needs to be compensated for with improved estimates.
However, there are a number of tricks available to reduce the level of divisor sums. The simplest comes from exploiting the change of variables , which can in principle reduce the level by a square root. For instance, when computing the divisor function , one can observe using this change of variables that every divisor of above is paired with one below , and so we have
except when is a perfect square, in which case one must subtract one from the right-hand side. Using this reduced-level divisor sum representation, one can obtain an improvement to (2), namely
This type of argument is also known as the Dirichlet hyperbola method. A variant of this argument can also deduce the prime number theorem from (3), (4) (and with some additional effort, one can even drop the use of (4)); this is discussed at this previous blog post.
Using this square root trick, one can now also control divisor sums such as
(Note that has no multiplicativity properties in , and so multiplicative number theory techniques cannot be directly applied here.) The level of the divisor sum here is initially of order , which is too large to be useful; but using the square root trick, we can expand this expression as
which one can rewrite as
The constraint is periodic in with period , so we can write this as
where is the number of solutions in to the equation , and so
The function is multiplicative, and can be easily computed at primes and prime powers using tools such as quadratic reciprocity and Hensel’s lemma. For instance, by Fermat’s two-square theorem, is equal to for and for . From this and standard multiplicative number theory methods (e.g. by obtaining asymptotics on the Dirichlet series ), one eventually obtains the asymptotic
Similar arguments give asymptotics for on other quadratic polynomials; see for instance this paper of Hooley and these papers by McKee. Note that the irreducibility of the polynomial will be important. If one considers instead a sum involving a reducible polynomial, such as , then the analogous quantity becomes significantly larger, leading to a larger growth rate (of order rather than ) for the sum.
However, the square root trick is insufficient by itself to deal with higher order sums involving the divisor function, such as
the level here is initially of order , and the square root trick only lowers this to about , creating an error term that overwhelms the main term. And indeed, the asymptotic for such this sum has not yet been rigorously established (although if one heuristically drops error terms, one can arrive at a reasonable conjecture for this asymptotic), although some results are known if one averages over additional parameters (see e.g. this paper of Greaves, or this paper of Matthiesen).
Nevertheless, there is an ingenious argument of Erdös that allows one to obtain good upper and lower bounds for these sorts of sums, in particular establishing the asymptotic
for any fixed (not necessarily irreducible) and any fixed , due to van der Corput; this bound is in fact used to dispose of some error terms in the proof of (6). These should be compared with what one can obtain from the divisor bound and the trivial bound , giving the bounds
for any fixed .
for any , and the preceding methods then easily allow one to obtain the lower bound by taking small enough (more precisely, if has degree , one should take equal to or less). The upper bounds in (6) and (7) are more difficult. Ideally, if we could obtain upper bounds of the form
for any fixed , then the preceding methods would easily establish both results. Unfortunately, this bound can fail, as illustrated by the following example. Suppose that is the product of distinct primes , each of which is close to . Then has divisors, with of them close to for each . One can think of (the logarithms of) these divisors as being distributed according to what is essentially a Bernoulli distribution, thus a randomly selected divisor of has magnitude about , where is a random variable which has the same distribution as the number of heads in independently tossed fair coins. By the law of large numbers, should concentrate near when is large, which implies that the majority of the divisors of will be close to . Sending , one can show that the bound (8) fails whenever .
This however can be fixed in a number of ways. First of all, even when , one can show weaker substitutes for (8). For instance, for any fixed and one can show a bound of the form
For Erdös’s upper bound (6), though, one cannot afford to lose these additional factors of , and one must argue more carefully. Here, the key observation is that the counterexample discussed earlier – when the natural number is the product of a large number of fairly small primes – is quite atypical; most numbers have at least one large prime factor. For instance, the number of natural numbers less than that contain a prime factor between and is equal to
which, thanks to Mertens’ theorem
for some absolute constant , is comparable to . In a similar spirit, one can show by similarly elementary means that the number of natural numbers less than that are -smooth, in the sense that all prime factors are at most , is only about or so. Because of this, one can hope that the bound (8), while not true in full generality, will still be true for most natural numbers , with some slightly weaker substitute available (such as (7)) for the exceptional numbers . This turns out to be the case by an elementary but careful argument.
The Erdös argument is quite robust; for instance, the more general inequality
for fixed irreducible and , which improves van der Corput’s inequality (8) was shown by Delmer using the same methods. (A slight error in the original paper of Erdös was also corrected in this latter paper.) In a forthcoming revision to my paper on the Erdös-Straus conjecture, Christian Elsholtz and I have also applied this method to obtain bounds such as
which turn out to be enough to obtain the right asymptotics for the number of solutions to the equation .
Below the fold I will provide some more details of the arguments of Landreau and of Erdös.
Given a positive integer , let denote the number of divisors of n (including 1 and n), thus for instance d(6)=4, and more generally, if n has a prime factorisation
then (by the fundamental theorem of arithmetic)
Clearly, . The divisor bound asserts that, as gets large, one can improve this trivial bound to
for any , where depends only on ; equivalently, in asymptotic notation one has . In fact one has a more precise bound
The divisor bound is useful in many applications in number theory, harmonic analysis, and even PDE (on periodic domains); it asserts that for any large number n, only a “logarithmically small” set of numbers less than n will actually divide n exactly, even in the worst-case scenario when n is smooth. (The average value of d(n) is much smaller, being about on the average, as can be seen easily from the double counting identity
or from the heuristic that a randomly chosen number m less than n has a probability about 1/m of dividing n, and . However, (4) is the correct “worst case” bound, as I discuss below.)
The divisor bound is elementary to prove (and not particularly difficult), and I was asked about it recently, so I thought I would provide the proof here, as it serves as a case study in how to establish worst-case estimates in elementary multiplicative number theory.
[Update, Sep 24: some applications added.]