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On the distribution of powered numbers

  • Jörg Brüdern EMAIL logo and Olivier Robert
Published/Copyright: May 22, 2024

Abstract

Asymptotic formulae are established for the number of natural numbers m with largest square-free divisor not exceeding m ϑ , for any fixed positive parameter ϑ . Related counting functions are also considered.

MSC 2010: 11N25; 11N37

1 Introduction

Motivated by questions about diophantine equations and the a b c -conjecture, Mazur [1] proposed to smooth out the set of positive l th powers in a multiplicative way, by what he named powered numbers. To introduce the latter, let k ( m ) denote the largest square-free divisor of the natural number m . Mazur’s powered numbers (relative to the real number l > 0 ) are the numbers m N with k ( m ) l m . Note here that l need not be integral in this definition, but for l N , the powered numbers (relative to l ) contain the l th powers. It is proposed in [1] to replace, within a given diophantine equation, an l th power by the corresponding powered numbers and to consider the resulting equation between powered numbers as the associated “rounded” diophantine equation.

In this note, we analyse the distribution of powered numbers. We find it more appropriate to work with the real number ϑ = 1 l . The condition k ( m ) l m is then expressed equivalently as k ( m ) m ϑ , and for any ϑ > 0 , we define the set

A ( ϑ ) = { m N : k ( m ) m ϑ } .

Thus, Mazur’s powered numbers (relative to l ) are exactly the elements of A ( 1 l ) . For approaches to rounded diophantine problems, it is relevant to determine the density of the set A ( ϑ ) . We therefore consider the number S ϑ ( x ) of elements in A ( ϑ ) that do not exceed x . It is not difficult to see that for any 0 < ϑ < 1 , the number S ϑ ( x ) obeys the inequalities

(1.1) x ϑ S ϑ ( x ) x ϑ + ε ,

whenever ε is a given positive real number and x is large in terms of ε . It now transpires that for ϑ = 1 l , the powered numbers are not much denser than the l th powers. A weaker version of (1.1) occurs in the study of Mazur [1] who refers to Granville, showing him an “easy” argument supposedly confirming the inequalities x ϑ ε S ϑ ( x ) x ϑ + ε . In Mazur’s article, there is no indication how this would go but the simplest argument we know allows one to take ε = 0 in the lower bound. To substantiate this claim, fix a number ϑ ( 0 , 1 ) . Define l N by 1 l < ϑ 1 ( l 1 ) , and t R by l t = 1 ϑ . Then, t ( 0 , 1 ] and

(1.2) 1 ϑ = t ( l 1 ) + ( 1 t ) l .

Let W ( x ) be the number of natural numbers w that have a representation

(1.3) w = 8 l n l 1 m l

with n , m square-free and constrained to the intervals

(1.4) 1 2 x 8 l ϑ t < n x 8 l ϑ t , 1 2 x 8 l ϑ ( 1 t ) < m x 8 l ϑ ( 1 t ) .

The numbers w counted by W ( x ) uniquely determine the square-free numbers n , m with (1.3) and (1.4). This follows from unique factorisation. Hence, W ( x ) equals the number of pairs n , m of square-free numbers satisfying (1.4). In particular, we see that W ( x ) x ϑ .

Next, we observe that (1.3) gives k ( w ) 2 n m , and therefore, k ( w ) 2 ( 8 l x ) ϑ . By (1.2) and (1.4), we also have w 2 ( 2 l 1 ) x . It follows that k ( w ) w ϑ . Hence, numbers counted by W ( x ) are also counted by S ϑ ( x ) . This yields S ϑ ( x ) W ( x ) , and the left inequality in (1.1) follows at once.

For the upper bound in (1.1), one may refer to authorities like Tenenbaum [2, Theorem II.1.15]. However, the shortest argument available to us uses Rankin’s trick within the following chain of obvious inequalities:

S ϑ ( x ) m x k ( m ) x ϑ 1 m = 1 x ϑ k ( m ) x m ε x ϑ + ε .

In this argument, one needs to realise that the series k ( m ) 1 m ε converges, but this is immediately seen after converting this series to an Euler product.

For some of the finer questions on rounded diophantine equations, such as the asymptotic distribution of their solutions, the rough bounds displayed in (1.1) do not suffice. Instead, one desires an asymptotic formula for S ϑ ( x ) , and our primary goal in this note is to supply such a formula. Let 1 y x . Following Robert and Tenenbaum [3], we write N ( x , y ) for the number of m N with m x and k ( m ) y . The numbers counted here are their nuclear numbers. It seems natural to expect that the trivial upper bound S ϑ ( x ) N ( x , x ϑ ) should not be very wasteful. However, it was conjectured by Erdős[1] (1962) and proved instantly by de Bruijn and van Lint [4] (1963) that

(1.5) m x m k ( m ) = o m x x k ( m ) , ( x ) .

For a quantitative version of this estimate, see [3, Théorème 4.4]. These results suggest that the order of magnitude of S ϑ ( x ) is somewhat smaller than that of N ( x , x ϑ ) , and this is indeed the case.

Before we make this precise, we recall an estimate for N ( x , y ) . We are primarily interested in the case y = x ϑ with ϑ > 0 , but we work in the wider range y > exp ( ( log x ) 2 3 ) . The multiplicative function

(1.6) ψ ( m ) = p m ( p + 1 ) ( m 1 )

and the function F : [ 0 , ) [ 0 , ) defined by

F ( v ) = 6 π 2 m 1 1 ψ ( m ) min 1 , e v m

are featured in the uniform asymptotic formula

(1.7) N ( x , y ) = ( 1 + o ( 1 ) ) y F ( log ( x y ) ) ( y > exp ( ( log x ) 2 3 ) , x )

that is contained in [3, Proposition 10.1]. As we shall see momentarily, for each pair ϑ , x with 0 < ϑ < 1 and x 2 , there is exactly one real number α = α ϑ ( x ) > 0 with

(1.8) p p α log p ( p α 1 ) ( 1 + ( p + 1 ) ( p α 1 ) ) = ( 1 ϑ ) log x .

We are now in a position to state our first result.

Theorem 1

Let 0 < ϑ < 1 fixed. Then, for x 9 , one has

(1.9) S ϑ ( x ) = x ϑ F ( ( 1 ϑ ) log x ) α ϑ ( x ) ϑ 1 + O log log x log x .

For x 27 , one also has

(1.10) S ϑ ( x ) = 2 x ϑ F ( ( 1 ϑ ) log x ) ϑ ( 1 ϑ ) ( log x ) log log x 1 + O log log log x log log x .

This result calls for several comments. First, we take y = x ϑ in (1.7) and substitute the resulting equation

(1.11) N ( x , x ϑ ) = ( 1 + o ( 1 ) ) x ϑ F ( ( 1 ϑ ) log x ) ( x )

within (1.10) to infer that

S ϑ ( x ) = ( 1 + o ( 1 ) ) N ( x , x ϑ ) 1 ϑ 2 1 ϑ 1 2 ( ( log x ) log log x ) 1 2 ( x ) .

This is an analogue of (1.5), in quantitative form that is of strength comparable to [3, Théorème 4.4].

Our second comment concerns the implicitly defined function α ϑ ( x ) . It originates in the Dirichlet series

(1.12) G ( s ) = 6 π 2 m 1 1 ψ ( m ) m s = 6 π 2 p 1 + 1 ( p + 1 ) ( p s 1 )

that converges absolutely in Re s > 0 and therefore has no zeros in this half plane. For real numbers σ > 0 , we have G ( σ ) > 0 . We may then define

g ( σ ) = log G ( σ ) .

Note that g extends to a holomorphic function on the right half plane, and one computes the logarithmic derivative of G ( s ) from the Euler product representation (1.12) to

g ( s ) = p p s log p ( p s 1 ) ( 1 + ( p + 1 ) ( p s 1 ) ) .

Note that the sum on the right-hand side here coincides with the sum in (1.8). On differentiating again, it transpires that the real function g : ( 0 , ) R is increasing. Considering σ 0 and σ , one finds that its range is the open interval ( , 0 ) . We conclude that for a given v > 0 , there is a unique positive number σ v with

g ( σ v ) + v = 0 .

From [3, Lemme 6.6], we infer that for v 3 , one has

(1.13) σ v = 2 v log v 1 + O log log v log v .

Here, we choose v = ( 1 ϑ ) log x and then have α ϑ ( x ) = σ v . In particular, we see that (1.9) and (1.13) imply (1.10). Thus, it only remains to prove (1.9).

Our last comment concerns the actual size of S ϑ ( x ) . This requires some more information on the function F . From [3, (2.12)], we have the asymptotic relation

(1.14) log F ( v ) = ( 1 + o ( 1 ) ) 8 v log v ( v ) .

By inserting (1.14) into (1.10), we deduce that there exists some positive number β ( x ; ϑ ) > 0 with

(1.15) S ϑ ( x ) = x ϑ + β ( x ; ϑ ) ( 0 < ϑ < 1 , x x 0 ( ϑ ) )

and the property that for any fixed ϑ ( 0 , 1 ) one has

β ( x ; ϑ ) = ( 1 + o ( 1 ) ) 8 ( 1 ϑ ) ( log x ) ( log log x ) ( x ) .

Note that (1.15) yields another proof of (1.1).

We now turn to local estimates for S ϑ ( x ) . In our case, this amounts to comparing the respective behaviour of S ϑ ( z x ) and S ϑ ( x ) uniformly for large x , when z is in some sense sufficiently close to 1. Such estimates are often obtained with the saddle-point method, and we follow this route here, too. In a suitable range for z , the fraction S ϑ ( z x ) S ϑ ( x ) may be approximated by a simple function of z .

Theorem 2

Let 0 < ϑ < 1 . Then, for x large, we have

S ϑ ( z x ) = z ϑ S ϑ ( x ) 1 + O log log x log x

uniformly for z > 0 with log z log log x .

Finally, we consider the counting function for a variation of the powered numbers. For given ϑ ( 0 , 1 ) and Θ R , we consider

S ϑ , Θ ( x ) = # { n x : k ( n ) n ϑ ( log n ) Θ } .

Note that S ϑ ( x ) = S ϑ , 0 ( x ) . The set of integers such that k ( n ) n ϑ ( log n ) Θ plays a prominent role in our companion article [5], and therefore, we provide an estimate for S ϑ , Θ ( x ) . It turns out that the conditions k ( n ) n ϑ and k ( n ) n ϑ ( log n ) Θ are relatively close and that the ratio S ϑ , Θ ( x ) S ϑ ( x ) is roughly of size ( log x ) Θ .

Theorem 3

Let 0 < ϑ < 1 and Θ R be fixed. Then, for x large, one has

S ϑ , Θ ( x ) = ( log x ) Θ S ϑ ( x ) 1 + O log log x log x .

2 Proof of Theorem 1

In this section, we derive Theorem 1. Before we embark on the main argument, we fix some notations and recall a pivotal result concerned with the distribution of square-free numbers. This involves the function ψ ( m ) as defined in (1.6), the Möbius function μ ( m ) , and for a parameter 0 γ 1 2 at our disposal, the product

r γ ( m ) = p m ( 1 + 4 γ p 1 2 ) .

One then has the estimate (see [3, (10.1)])

(2.1) l z μ 2 ( l k ) = 6 k z π 2 ψ ( k ) + O ( r γ ( k ) z 1 γ )

that holds uniformly relative to the square-free number k and the real parameters z , γ in the ranges z 1 , 0 γ 1 2 .

The first steps of our argument follow the pattern laid out in [3, Sect. 10]. Unique factorisation shows that for all natural numbers n , there exists exactly one pair of coprime natural numbers l , m with μ ( l ) 2 = 1 and n = l m k ( m ) . Note that the two conditions ( l , m ) = 1 and μ ( l ) 2 = 1 are equivalent to the single condition μ ( l k ( m ) ) 2 = 1 . Furthermore, one has k ( n ) = l k ( m ) . With ϑ ( 0 , 1 ) now fixed, it follows that S ϑ ( x ) equals the number of ( l , m ) N 2 satisfying the conditions

μ ( l k ( m ) ) 2 = 1 , l m k ( m ) x , l k ( m ) ( l m k ( m ) ) ϑ .

These last three conditions we recast more compactly as

(2.2) μ ( l k ( m ) ) 2 = 1 , l k ( m ) min ( x m , m ϑ ( 1 ϑ ) ) .

From now on, the number κ = ϑ ( 1 ϑ ) features prominently, and we also put y = x ϑ . Note that

(2.3) min ( x m , m κ ) = m κ if and only if m x y .

Hence, we consider the ranges m x y and x y < m x separately. By (2.2) and (2.3), this leads to the decomposition

(2.4) S ϑ ( x ) = S ϑ ( 1 ) ( x ) + S ϑ ( 2 ) ( x ) ,

in which

S ϑ ( 1 ) ( x ) = m x y k ( m ) m κ l m κ k ( m ) μ ( l k ( m ) ) 2 , S ϑ ( 2 ) ( x ) = x y < m x m k ( m ) x l x m k ( m ) μ ( l k ( m ) ) 2 .

We apply (2.1) with k = k ( m ) to both inner sums and obtain

(2.5) S ϑ ( 1 ) ( x ) = 6 π 2 m x y k ( m ) m κ m κ ψ ( m ) + O ( R 1 ) ,

(2.6) S ϑ ( 2 ) ( x ) = 6 π 2 x y < m x m k ( m ) x x m ψ ( m ) + O ( R 2 ) ,

where

R 1 = m x y k ( m ) m κ r γ ( m ) m κ k ( m ) 1 γ , R 2 = x y < m x m k ( m ) x r γ ( m ) x m k ( m ) 1 γ .

It turns out that R 1 and R 2 are small. In order to couch their estimation, as well as the analysis of other error terms that arise later, under the umbrella of a single treatment, we choose parameters γ and σ with 0 < γ < σ 1 2 and introduce the series

(2.7) E = m = 1 r γ ( m ) y k ( m ) 1 γ x y m σ .

If one turns the sum over m into an Euler product, the factor at the prime p becomes 1 + O σ ( p γ 1 σ ) , so the conditions σ > γ > 0 ensure convergence. It is routine to show that

R 1 + R 2 E .

In fact, by Rankin’s trick,

R 1 m x y k ( m ) m κ r γ ( m ) m κ k ( m ) 1 γ x y m σ .

For m x y , one has m κ y , and it follows that R 1 E .

Likewise, one confirms R 2 E by observing that 1 γ > 1 2 σ so that

(2.8) x m k ( m ) 1 γ = y k ( m ) 1 γ x y m 1 γ y k ( m ) 1 γ x y m σ .

The appearance of k ( m ) in the summation conditions on the right-hand sides of (2.5) and (2.6) is a nuisance, and we proceed by removing these. If the condition k ( m ) m κ is removed from the sum in (2.5), one imports an error no larger than

m x y k ( m ) > m κ m κ ψ ( m ) m x y k ( m ) > m κ m κ k ( m ) m x y m κ k ( m ) 1 γ x y m σ E .

Here, the penultimate inequality is obtained by the argument that completed the estimation of R 1 . Similarly, if the condition m k ( m ) x is removed from the summation condition in (2.6), then the resulting error does not exceed

x y < m x m k ( m ) > x x m ψ ( m ) x y < m x m k ( m ) > x x m k ( m ) m > x y m k ( m ) > x x m k ( m ) = R ,

say. By Rankin’s trick and (2.8),

R m > x y x m k ( m ) 1 γ m > x y y k ( m ) 1 γ x y m σ E .

Collecting together, we deduce from (2.5) and (2.6) the asymptotic relations

S ϑ ( 1 ) ( x ) = 6 π 2 m x y m κ ψ ( m ) + O ( E ) , S ϑ ( 2 ) ( x ) = 6 π 2 x y < m x x m ψ ( m ) + O ( E ) ,

and by (2.4), we infer that

S ϑ ( x ) = 6 π 2 m x m κ ψ ( m ) min ( 1 , x m κ 1 ) + O ( E ) .

Note that the sum on the right is a partial sum of a convergent series. If one completes the sum, then it is immediate that the error thus imported is bounded by R and hence by E . We have now reached the provisional expansion

(2.9) S ϑ ( x ) = 6 π 2 m = 1 m κ ψ ( m ) min ( 1 , x m κ 1 ) + O ( E ) .

It remains to estimate E . In its definition (2.7), we encounter a sum over a multiplicative function, and so

E = y 1 γ ( x y ) σ p 1 + p γ r γ ( p ) p ( p σ 1 ) y 1 γ ( x y ) σ p 1 + p γ p ( p σ 1 ) p 1 + 4 γ p γ 1 2 p ( p σ 1 ) .

Now, since p γ 1 2 1 , γ < σ , p σ 1 σ log p , and since the sum p 1 p log p converges

p 1 + 4 γ p γ 1 2 p ( p σ 1 ) p 1 + 4 p log p 1 .

As in the discussion following (1.13), we write v = ( 1 ϑ ) log x = log x y , and then choose σ = σ v and γ = σ v 1 log y . Then γ < σ , and for large x , we have σ < 1 2 . By (1.12), one has

p 1 + p γ p ( p σ 1 ) = π 2 6 G ( σ ) p 1 + ( p γ 1 ) ( p + 1 ) + 1 p + p ( p + 1 ) ( p σ 1 ) .

Now, on recalling (1.13),

p e 1 σ 1 + ( p γ 1 ) ( p + 1 ) + 1 p + p ( p + 1 ) ( p σ 1 ) p e 1 σ 1 + 1 p 1 σ ( v log v ) 1 2 ,

while one also has

p > e 1 σ 1 + ( p γ 1 ) ( p + 1 ) + 1 p + p ( p + 1 ) ( p σ 1 ) p > e 1 σ 1 + 1 p 1 + σ γ ζ 1 + 1 log y log y .

On collecting together, this shows that

E y 1 σ v e v σ v G ( σ v ) ( v log v ) 1 2 log y .

From [3, (2.11)], we deduce that

F ( v ) log v v 1 4 e v σ v G ( σ v ) ( v 2 ) ,

and hence,

E y 1 σ v F ( v ) v 3 4 ( log v ) 1 4 log y .

With the choice of y and v , one has σ v = α ϑ ( x ) . Moreover, log y and v have the order of magnitude log x so that the last inequality now reads

(2.10) E x ϑ F ( ( 1 ϑ ) log x ) x ϑ α ϑ ( x ) ( log x ) 7 4 ( log log x ) 1 4 .

Our final task is to compare our estimate for E with the size of the sum on the right of (2.9).

Recall that in view of (1.1) and (1.11), S ϑ ( x ) and N ( x , x ϑ ) are of comparable size. In order to mimick the estimate (1.11), we introduce the function

H ϑ ( x ) = 6 π 2 x ϑ m = 1 m κ ψ ( m ) min ( 1 , x m κ 1 )

so that (2.9) now reads

S ϑ ( x ) = x ϑ H ϑ ( x ) + O ( E ) .

Our aim is to give an estimate of H ϑ ( x ) by using the saddle-point method, and to describe more precisely the behaviour of the function H ϑ ( x ) F ( ( 1 ϑ ) log x ) as x .

Lemma 1

Fix ϑ with 0 < ϑ < 1 . Then, for x 27 , one has

H ϑ ( x ) = F ( ( 1 ϑ ) log x ) α ϑ ( x ) ϑ 1 + O log log x log x .

Proof

The argument is modelled on [3, Section 8]. Recall the identity

1 2 π i σ + i R y s s ( 1 s ) d s = min ( 1 , y ) ( 0 < σ < 1 , y > 0 ) .

We then have the integral representation

H ϑ ( x ) = 1 x ϑ 6 π 2 m 1 m κ ψ ( m ) 1 2 π i σ + i R 1 s ( 1 s ) x m 1 ( 1 ϑ ) s d s = 1 2 π i σ + i R G s ϑ 1 ϑ x s ϑ d s s ( 1 s ) = 1 2 π R G σ + i t ϑ 1 ϑ x σ + i t ϑ d t ( σ + i t ) ( 1 σ i t ) .

After a linear change of variable in t , we arrive at

H ϑ ( x ) = 1 2 π R G σ ϑ 1 ϑ + i t x σ + i ( 1 ϑ ) t ϑ ( 1 ϑ ) d t ( σ + i ( 1 ϑ ) t ) ( 1 σ i ( 1 ϑ ) t ) .

Recall again that v = ( 1 ϑ ) log x and that α ϑ ( x ) = σ v . We take σ = ϑ + ( 1 ϑ ) σ v . For large x , one then has 0 < σ < 1 , and the previous formula for H ϑ ( x ) becomes

H ϑ ( x ) = 1 2 π R G ( σ v + i t ) e ( σ v + i t ) v ( ϑ + ( 1 ϑ ) ( σ v + i t ) ) ( 1 σ v i t ) d t .

After truncation, we have

H ϑ ( x ) = 1 2 π v 2 v 2 G ( σ v + i t ) e ( σ v + i t ) v ( ϑ + ( 1 ϑ ) ( σ v + i t ) ) ( 1 σ v i t ) d t + O G ( σ v ) e v σ v v 2 .

Moreover, following the proof of [3, Théorème 8.6], we set

η v = ( log v ) g ( σ v ) ( log v ) 3 4 v 3 4 ,

and recall [3, Lemme 8.5], asserting that for some c > 0 , we have

G ( σ v + i t ) G ( σ v ) e c ( log v ) 2 ( η v t exp ( ( log v ) 38 37 ) ) .

It now follows that

H ϑ ( x ) = 1 2 π η v η v G ( σ v + i t ) e ( σ v + i t ) v ( ϑ + ( 1 ϑ ) ( σ v + i t ) ) ( 1 σ v i t ) d t + O G ( σ v ) e v σ v v 2 .

Setting

D m = v ( m + 1 ) 2 ( log v ) ( m 1 ) 2 ( m 0 ) ,

we have

H ϑ ( x ) = G ( σ v ) e v σ v 2 π η v η v ϒ ( t ) e g ( σ v ) t 2 2 d t + O G ( σ v ) e v σ v v 2 ,

where

ϒ ( t ) = e i g ( σ v ) t 3 6 + O ( t 4 D 4 ) ( ϑ + ( 1 ϑ ) ( σ v + i t ) ) ( 1 σ v i t ) .

By Taylor expansion, we infer

ϒ ( t ) = Z ( t ) ( ϑ + ( 1 ϑ ) σ v ) ( 1 σ v )

where

Z ( t ) = 1 + i t 1 1 σ v 1 ϑ ϑ + ( 1 ϑ ) σ v i g ( σ v ) t 3 6 + O ( t 2 + D 4 t 4 + D 3 2 t 6 ) .

Now, still following the pattern of the proof of [3, Théorème 8.6], one is led to

H ϑ ( x ) = x ( 1 ϑ ) α ϑ ( x ) G ( α ϑ ( x ) ) ϑ 2 π g ( α ϑ ( x ) ) 1 + O log log x log x .

We omit the details. From [3, Théorème 8.6] we import the relation

F ( v ) = e v σ v G ( σ v ) σ v 2 π g ( σ v ) 1 + O log v v ( v 2 ) ,

and the lemma follows.□

We may now complete the proof of Theorem 1. Using the lemma and (1.13), we obtain

H ϑ ( x ) F ( ( 1 ϑ ) log x ) ( ( log x ) log log x ) 1 2 ,

so that the estimate (2.10) implies

E x ϑ H ϑ ( x ) x ϑ α ϑ ( x ) ( log x ) 9 4 ( log log x ) 3 4 .

We then have

S ϑ ( x ) = x ϑ H ϑ ( x ) ( 1 + O ( x ϑ α ϑ ( x ) ( log x ) 9 4 ( log log x ) 3 4 ) ) .

We may now replace H ϑ ( x ) by the estimate from Lemma 1. Since

x ϑ α ϑ ( x ) ( log x ) 9 4 ( log log x ) 3 4 log log x log x ,

this yields (1.9). As remarked earlier, (1.10) follows from (1.13) and (1.9). The proof of Theorem 1 is complete.

3 Proof of Theorem 2

Subject to the hypotheses of Theorem 2, when x is large, one has log z x log x . Hence, Theorem 1 implies that

(3.1) S ϑ ( z x ) = ( z x ) ϑ F ( ( 1 ϑ ) log z x ) α ϑ ( z x ) ϑ 1 + O log log x log x

holds uniformly for log z log log x . We recall [3, Proposition 8.7]. This asserts that uniformly for t v 3 4 ( log v ) 1 4 , one has

F ( v + t ) = e t σ v F ( v ) 1 + O log v + t 2 v v log v .

Using this estimate with v = ( 1 ϑ ) log x and t = ( 1 ϑ ) log z , one finds

F ( ( 1 ϑ ) log z x ) = F ( ( 1 ϑ ) log x ) z ( 1 ϑ ) α ϑ ( x ) 1 + O log log x log x .

Moreover, in the ranges for x and z considered here, one has

z ( 1 ϑ ) α ϑ ( x ) = 1 + O ( α ϑ ( x ) log z ) ,

which yields an admissible error term.

Finally, [3, (8.9)] implies that uniformly for t v 2 one has

σ v + t = σ v ( 1 + O ( t v ) ) .

Hence

α ϑ ( z x ) = α ϑ ( x ) 1 + O log z log x ,

which again leads to an admissible error term. Inserting these estimates in (3.1) completes the proof of Theorem 2.

It may be worth pointing out that the aforementioned argument actually proves a little more. A close inspection of the proof of Theorem 2 shows that the estimate

S ϑ ( z x ) = z ϑ + ( 1 ϑ ) α 0 ( x ) S ϑ ( x ) 1 + O log log x log x + ( log z ) 2 ( log x ) 3 2 ( log log x ) 1 2

holds uniformly in the range z > 0 , x > 27 , log z ( log x ) 3 4 ( log log x ) 1 4 .

4 Proof of Theorem 3

Before proving Theorem 3, we briefly sketch the main steps. We first choose a suitable real number U = U ( x ) such that log U = ( log x ) ( 1 + o ( 1 ) ) and count the integers n not exceeding x such that k ( n ) n ϑ ( log U ) Θ . The first step is to show that the number of these integers is essentially S ϑ ( x ) multiplied by ( log x ) Θ . The second step is to prove that the number of these integers is close to S ϑ , Θ ( x ) .

In light of this description, for any x 1 and any z > 0 , we set

B ( x , z ) = # { n x : k ( n ) z n ϑ } .

Theorem 4

Let 0 < ϑ < 1 be fixed. Then, for x large, one has

B ( x , z ) = z S ϑ ( x ) 1 + O log log x log x

uniformly for z > 0 with log z log log x .

Proof

We follow very closely the proof of Theorem 1, which corresponds to the case z = 1 . We redefine the meaning of y , now set to y = x ϑ z , and keep the notation κ and E . Note that in the sequel of this proof, the error term E is to be interpreted with the current specific choices for x and y .

For any n 1 , recall that there is a unique pair l , m with n = l m k ( m ) and μ 2 ( l k ( m ) ) = 1 . The conditions n x and k ( n ) z ϑ become

l m k ( n ) x and l k ( m ) m κ z 1 + κ .

By keeping the same dichotomy m x y and m > x y , it is straightforward that

B ( x , z ) = 6 π 2 m = 1 1 ψ ( m ) min ( m κ z 1 + κ , x / m ) + O ( E ) .

Moreover, (2.9) with the parameter x z κ 1 reads

S ϑ ( x z κ 1 ) = 6 π 2 m = 1 1 ψ ( m ) min ( m κ , x z κ 1 / m ) + O ( z ( 1 + κ ) ( 1 γ ) E ) .

Hence, one has

B ( x , z ) = z 1 + κ S ϑ ( x z κ 1 ) + O ( z ( 1 + κ ) γ E ) .

Still choosing γ = σ v 1 log y and σ = σ v , we have z ( 1 + κ ) γ 1 . Moreover, (2.10) implies that for some c > 0 , one has

E x ϑ F ( ( 1 ϑ ) log x ) exp c log x log log x .

This is sufficient to ensure that

E z S ϑ ( x ) log log x log x .

Finally, we estimate the term S ϑ ( x z κ 1 ) by Theorem 2. Inserting the result of Theorem 2 into the estimate for B ( x , z ) one meets a main term in which the exponent of z is 1 + κ ( 1 + κ ) ϑ = 1 . Theorem 4 follows.□

We may now complete the proof of Theorem 3. It is sufficient to prove the result for Θ 0 , since in the case Θ = 0 , one has S ϑ , Θ ( x ) = S ϑ ( x ) . We put

C = 1 + ( 1 + Θ ) ϑ and U = U ( x ) = x ( log x ) C .

Note that we have C > 0 , C ϑ 1 and Θ + C ϑ 1 .

First, consider the case Θ > 0 . Any integer n counted by S ϑ , Θ ( x ) satisfies k ( n ) n ϑ ( log x ) Θ , whence S ϑ , Θ ( x ) B ( x , ( log x ) Θ ) . Now, a lower bound is obtained by noting that the set of integers U < n x counted in S ϑ , Θ ( x ) contains the integers such that k ( n ) n ϑ ( log U ) Θ . These deliberations yield the inequalities

B ( x , ( log U ) Θ ) B ( U , ( log U ) Θ ) S ϑ , Θ ( x ) B ( x , ( log x ) Θ ) .

Now, using Theorem 4 to estimate B ( x , ( log U ) Θ ) and B ( x , ( log x ) Θ ) , and then replacing ( log U ) Θ by ( log x ) Θ at the price of an admissible error term, one obtains the main term to estimate S ϑ , Θ ( x ) . For the remaining term, Theorem 4 and the definition of U imply that

B ( U , ( log U ) Θ ) ( log U ) Θ S ϑ ( U ) ( log x ) Θ S ϑ ( U ) .

Finally, Theorem 2 with the choice z = ( log x ) C implies that

S ϑ ( U ) z ϑ S ϑ ( x ) ( log x ) ϑ C S ϑ ( x ) ( log x ) 1 S ϑ ( x ) .

Gathering these estimates, we obtain an upper bound for B ( U , ( log U ) Θ ) that provides an admissible error term, and therefore, it proves the result for Θ > 0 .

The case Θ < 0 is very similar. Any integer counted by S ϑ , Θ ( x ) either satisfies n U with k ( n ) n ϑ , or U < n x with k ( n ) n ϑ ( log U ) Θ . Moreover, any integer n x such that k ( n ) n ϑ ( log x ) Θ is counted in S ϑ , Θ ( x ) . We conclude that

B ( x , ( log x ) Θ ) S ϑ , Θ ( x ) B ( x , ( log U ) Θ ) + S ϑ ( U ) .

As before, one uses Theorem 4 to estimate B ( x , ( log U ) Θ ) and B ( x , ( log x ) Θ ) . This provides the main term. Finally, Theorem 2 with z = ( log x ) C provides

S ϑ ( U ) z ϑ S ϑ ( x ) ( log x ) ϑ C S ϑ ( x ) ( log x ) Θ 1 S ϑ ( x ) ,

which yields an admissible error term.

  1. Funding information: Jörg Brüdern was supported by Deutsche Forschungsgemeinschaft project number 462335009. Olivier Robert was supported by the joint FWF-ANR project Arithrand: FWF: I 4945-N and ANR-20-CE91-0006.

  2. Author contributions: Both authors contributed equally to the content of this paper.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2023-09-06
Revised: 2024-03-21
Accepted: 2024-03-21
Published Online: 2024-05-22

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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