On the value-distribution of log L and L! /L
Yasutaka Ihara1 and Kohji Matsumoto2
RIMS, Kyoto University, Kitashirakawa-Oiwakecho,
Sakyo-ku, Kyoto 606-8502, Japanihara@kurims.kyoto-u.ac.jp
2
Graduate School of Mathematics, Nagoya University,
Furocho, Chikusa-ku, Nagoya 464-8602, Japankohjimat@math.nagoya-u.ac.jp
1

Abstract. ...
Key words: ...
2000 Mathematics subject classification: ...

1 The general formulation
The purpose of this article is to survey recent results of the authors on the valuedistribution of log L and L! /L for certain L-functions. Details [9, 10, 11] will be
published elsewhere. We begin with the general formulation of the problem.
Let K be a global field, X a certain family of infinitely many “characters” χdefined on K, and L(s, χ) the “L-function” associated with χ, where s = σ + iτ
is a complex variable. Our aim is to construct some “density function” Mσ (w)(resp. Mσ (w)) which describes the value-distribution of L! /L (resp. log L) inthe sense that
! !
" #
L
Avgχ Φ(s, χ) =Mσ (w)Φ(w)|dw|
(1.1)
L
C
or

Avgχ Φ (log L(s, χ)) =#C

Mσ (w)Φ(w)|dw|(1.2)

holds, where s is fixed, Avgχ is an average with respect to χ ∈ X in some suitablesense, Φ is a “test function” (some function with good properties defined on C),
and |dw| = (2π)−1 dudv (w = u + iv).
In the case of the Riemann zeta-function ζ(s), the value-distribution theory
was cultivated by H. Bohr and his colleagues in the first half of the 20th century.
Bohr treated the average with respect to the imaginary part of the variable; in!
other words, he considered the case when characters are defined by χτ ! (p) = p−iτfor each prime p and real τ ! . In this case the associated L-function is$$!
(1 − χτ ! (p)p−s )−1 =(1 − p−s−iτ )−1 = ζ(s + iτ ! ),
(1.3)
p

p

86

Y. Ihara and K. Matsumotoand the average with respect to χτ ! implies the mean value with respect toτ ! . In the log ζ case, Bohr and Jessen [1] proved the following theorem. Let
σ > 1/2, T > 0, R an arbitrary rectangle in C with the edges parallel to theaxes, and Vσ (T, R) the Lebesgue measure of the set of all τ ! ∈ [−T, T ] for which
log ζ(σ + iτ ! ) ∈ R holds (under a certain fixed choice of the branch). Then they
proved that there exists a continuous, everywhere non-negative function Fσ (w)for which#1lim
Vσ (T, R) =Fσ (w)|dw|(1.4)
T →∞ 2TR
holds for any R. Therefore, if we define the average by
1
T →∞ 2T

Avgχ φ(χτ ! ) = lim

# T

−T

φ(χτ ! )dτ !

(1.5)(where φ(χτ ! ) is any integrable function of τ ! ), we can see that (1.4) of Bohr andJessen is a special case of (1.2), with Φ = 1R , the characteristic function of R,
and Mσ = Fσ .
On the other hand, it is also important to consider some different type of
averages. In particular, various averages of Dirichlet L-functions with respect toDirichlet characters have been studied by many mathematicians. Some probabilistic limit theorems in this direction were obtained by P. D. T. A. Elliott andE. Stankus in 1970s. (See Chapter 8 of Laurinčikas [14].)
Recently, motivated by a study on Euler-Kronecker constants of global fields([5], [6], [12]), the first author [8] searched for the density function (the “M function”) Mσ (w) which satisfies (1.1) in various situations. In the next section
we review this work briefly.
In what follows, the symbol |A| means the cardinality of the set A. The
meaning of Vinogradov’s symbol f # g is the same as that of Landau’s symbol
f = O(g).
Acknowledgement. The authors would like to thank the referee for useful comments.

2 The situations we are working in
In [8], the following three situations are considered:(A) K = Q (the rational number field), or an imaginary quadratic field, or afunction field over any finite field. In the last case, we fix one prime divisor ℘∞of degree 1 which plays the same role as the unique archimedean prime in thefirst two cases. The characters are Dirichlet characters χ on K, normalized by
the condition χ(℘∞ ) = 1 in the last case.
(B) K is a number field with at least two archimedean primes, and thecharacters are from some family of unramified Grössencharacters.
(C) K = Q, and the characters are χτ ! defined in Section 1.

On the value-distribution of log L and L! /L87

We explain the meaning of “average” in Case (A). Let f be a prime divisorof K, and N (f ) its norm. By X(f ) we mean the set of all Dirichlet characterswith conductor f . Let%
1
AvgX(f ) φ(χ) =
φ(χ)
(2.1)
|X(f )|
χ∈X(f )

(where φ is any complex-valued function) and&(1)

AvgN (f )≤m φ(χ) =N (f )≤m AvgX(f ) φ(χ)&

N (f )≤m 1

,

(2.2)

where m is a positive integer, and f runs over all prime divisors with norm ≤ m.
Then the meaning of Avgχ in Case (A), studied in [8], isAvg(1)χ φ(χ) = lim

m→∞

'

(
(1)
AvgN (f )≤m φ(χ) .

(2.3)(We attach the suffix (1) here, because later we will also consider different typesof averages.) The meaning of the average in Case (C) is (1.5). As for the meaningof the average in Case (B), we refer to [8].
The associated L-function is defined as the usual Euler product, but in thefunction field case, without the ℘∞ -component; that is,$L(s, χ) =(1 − χ(℘)N (℘)−s )−1 ,℘'=℘∞where ℘ runs over prime divisors and N (℘) is the norm of ℘.
In the domain of absolute convergence of L-functions, the following theorem
is proved in [8]:Theorem 2.1. There exisits an explicitly constructable “M -function” Mσ (w) on
C for any σ > 1/2, such that when σ = Re s > 1, (1.1) holds for all the cases(A), (B) and (C), for any continuous function Φ.
Needless to say, more interesting is to consider the situation in the critical
strip, which is surely much more difficult. One obvious obstacle is the generalized
Riemann hypothesis (GRH), because in the L! /L case the L-function is in the
denominator. Therefore, in [8], the function field case is mainly studied, because
in this case the GRH has been solved.
In [8], formula (1.1) is proved in Case (A) for function fields K, in each of
the following two cases.
(a) σ > 3/4, and Φ is any character C → T = {t ∈ C ; |t| = 1}, i.e., Φ = ψzwith some z ∈ C defined by
ψz (w) = exp(iRe (zw));

(2.4)

88

Y. Ihara and K. Matsumoto(b) σ > 1/2, and Φ(w) = P (w, w), where P (w1 , w2 ) is any polynomial in two
variables w1 , w2 with complex coefficients;
see Theorem 7(ii), (iii) of [8]. The point is that instead of the characteristic
function of rectangles as in previous investigations, we consider characters andpolynomials. The result for characters will have some applications to more general case of Φ, as we shall see.
However, we already mentioned in Section 1 that the result (1.4) of Bohr andJessen is proved for any σ > 1/2. Therefore we may expect that results such as(a) are also valid for any σ > 1/2.

3 Unconditional results for the log L case, K = Q
The reason why Bohr and Jessen succeeded in proving their result for any σ >1/2 is that they used some mean value results on relevant Dirichlet series. Since
then, mean value theorems have been frequently used successfully in the valuedistribution theory (cf. Laurinčikas [14], Steuding [17]). Therefore it is natural
to expect that a suitable usage of mean value results will improve the above (a)and (b), or will give some progress even in the number field case.
One possible way of research is to develop the log L analogue of the theory in[8] and combine it with the Bohr-Jessen theory. The original argument of Bohrand Jessen is rather geometric, based on various properties of planar convex
curves, but later, more analytic (or Fourier-theoretic) approaches to Bohr-Jessen
type theorems have been developed; see Jessen-Wintner [13], Borchsenius-Jessen[2], the second author [15] etc. Therefore a strategy is to apply the methods inthose papers to the log L analogue of the theory of [8] which is also rather
Fourier-theoretic. This has been carried out in [10], and the following theorems
were proved.
First, in the domain of absolute convergence, as an analogue of Theorem 1.1,
we obtain:
Theorem 3.1. There exists an explicitly constructable “M -function” Mσ (w) on
C for any σ > 1/2, such that when σ = Re s > 1, (1.2) holds for all the cases(A), (B) and (C), for any continuous function Φ.
In the strip 1/2 < σ ≤ 1, in [10] we prove the result only in the case K = Q, so
only the case (A) (with K = Q) or (C).
First of all we have to fix the branch of the logarithm. Let D = {s ; 1/2 <σ ≤ 1}, and remove from D all segments
Bj (χ) = {s = σ + iτj ; 1/2 < σ ≤ σj },where σj + iτj are possible (hypothetical) zeros, and a possible pole, of L(s, χ)in D, and denote the remaining set by Gχ . At any point s0 = σ0 + iτ0 ∈ Gχ , wedefine the value of log L(s0 , χ) by the analytic continuation along the horizontal
path {s = σ + iτ0 ; σ ≥ σ0 }.

On the value-distribution of log L and L! /L

89

In Case (A) (with K = Q), if we use definition (2.3) of the average, then weshould exclude all points s ∈ Bj (χ) for all j and all χ from our consideration,
which is quite unsatisfactory. To avoid this trouble, we modify the definition of
the average as follows. Let X(f ) be the set of all primitive Dirichlet characters
whose conductor is f , and X ! (f ) be a subset of X(f ) for which|X ! (f )|=1
f →∞ |X(f )|limholds. Let
AvgX ! (f ) φ(χ) =1|X(f )|

(3.1)

%

φ(χ),

χ∈X ! (f )(2)and define Avgf ≤m φ(χ) by replacing AvgX(f ) in (2.2) by AvgX ! (f ) , and define
(1)

(2)

Avg(2)χ φ(χ) by replacing Avgf ≤m in (2.3) by Avgf ≤m . It is important that, if φis bounded, then the value of Avg(2)χ φ(χ) will not change when we choose smaller
X ! (f ), keeping condition (3.1).
By using a zero density theorem for L-functions (Montgomery [16]), we cancheck that, for any s with 1/2 < Re s ≤ 1, the set
X ! (f ) = X ! (f, s) := {χ ∈ X(f ) ; s ∈ Gχ }(3.2)

satisfies (3.1). Under this choice of X ! (f ) and the above definition of the average,
we can show the following theorem.
Theorem 3.2. Let 1/2 < σ = Re s ≤ 1. In Case (A) (with K = Q) or Case (C),(1.2) holds for Φ which is one of the following (or any finite linear combinationof them):(i) Φ is any bounded continuous function.
(ii) Φ is the characteristic function of either a compact subset of C or thecomplement of such a subset.
The special case Φ = ψz (see (2.4)) of (i) is actually the basic case in our proof.
In Case (A) (with K = Q), (1.2) can be read as
#
%
%
1
1
Φ(log L(s, χ)) =Mσ (w)Φ(w)|dw|, (3.3)
limm→∞ π(m)f −2
C!
2<f ≤mf :primeχ∈X (f )where π(m) is the number of primes not larger than m.
In Case (C), the meaning of the average is (1.5), and (1.2) can be read as
1lim
T →∞ 2T# T−T

Φ(log ζ(s + iτ ))dτ =
!

!

#

C

Mσ (w)Φ(w)|dw|.

(3.4)

If s+iτ ! ∈/ G1 (where 1 is the trivial character), then log ζ(s+iτ ! ) is not defined,
but still the integral is well-defined for the above type of Φ, so it is not necessary
to exclude such situation.

90

Y. Ihara and K. Matsumoto

4 The construction of “M -functions”
In this and the next section we assume K = Q, and sketch how to prove Theorems 1.2 and 3.2. In this section we explain how to construct the density function(the “M -function”). The argument is basically an analogue of that developed in[8], where the existence of the “M -function” in the L! /L case was established.
Let P be a finite set of primes, and define$
LP (s, χ) =(1 − χ(p)p−s )−1 .
p∈P

The first aim is to construct the density function Mσ,P (w) for which#
Avgχ Φ(log LP (s, χ)) =Mσ,P (w)Φ(w)|dw|

(4.1)

C

holds for any continuous function Φ.
)
Let T = {t ∈ C ; |t| = 1}, and TP = p∈P T . Define the function gσ,P :TP → C by%
gσ,P (tP ) = −log(1 − tp p−σ ),tP = (tp )p∈P ∈ TP .
(4.2)
p∈P

Then we see thatlog LP (s, χ) = gσ,P (χP P −iτ ),(4.3)

where χP = (χ(p))p∈P ∈ TP and P −iτ = (p−iτ )p∈P ∈ TP . Here we quote thefollowing lemma:#
Avgχ Ψ (χP ) =Ψ (tP )d∗ tP
(4.4)
TP

holds for any continuous function Ψ : TP → C, where d∗ tP is the normalized
Haar measure on TP (Lemma 4.3.1 of [8]; in Case (A), exclude those χ whose
conductor is ∈ P ). This lemma reflects the uniform distribution property of χP
on TP , and can be proved by using the orthogonality relation of characters inCase (A), and the Kronecker-Weyl theorem in Case (C). Applying this lemma
with Ψ = Φ ◦ gσ,P , and combining with (4.3), we obtain#
Avgχ Φ(log LP (s, χ)) =Φ(gσ,P (tP ))d∗ tP .
(4.5)
TP

Therefore, to prove (4.1), it is enough to find Mσ,P (w) which satisfies##
Φ(gσ,P (tP ))d∗ tP =Mσ,P (w)Φ(w)|dw|.
TP

(4.6)

C

When P consists of only one prime P = {p}, by direct calculations we find that

On the value-distribution of log L and L! /L|1 − rp eiθp |2
δ(rp − p−σ )rpMσ,{p} (w) =

91

(4.7)

suffices, where rp , θp are determined by w = − log(1−rp eiθp ) and δ(·) denotes the
Dirac delta distribution. (When there is no solution rp , θp then Mσ,{p} (w) = 0.)
For general (finite) P , we define Mσ,P (w) by the convolution product#
Mσ,P (w) =Mσ,P ! (w! )Mσ,{p} (w − w! )|dw! |(4.8)
C

if P = P ! ∪ {p}. Then, if |P | ≥ 2, this is a function in the usual sense, compactly
supported, non-negative, and#
Mσ,P (w)|dw| = 1.
(4.9)
CIt is not difficult to see that this Mσ,P (w) satisfies (4.6), and hence (4.1), for
any continuous Φ.
Now we choose P = Py := {p : prime ; p ≤ y}. The next step is the proof ofthe existence of the limit
Mσ (w) = lim Mσ,P (w).
y→∞

For this purpose we consider the Fourier transform$#*
Mσ,P (z) =Mσ,{p} (w)ψz (w)|dw|.
p∈P(4.10)(4.11)

C

Since each integral on the right-hand side is O((1 + |z|)−1/2 ) (Theorem 13 of*σ,P (z) = O((1 + |z|)−|P |/2 ). Using thisJessen and Wintner [13]), we have M
fact, we can show that there exists the limit*σ (z) = lim M*σ,P (z),My→∞(4.12)and the above convergence is uniform in any compact subset of C (by applying
the method in Section 3 of [15]). From (4.12) we can deduce the existence of thelimit (4.10) for any σ > 1/2. The limit function Mσ (w) is continuous in both σ
and w, non-negative, and satisfies#
Mσ (w)|dw| = 1.
(4.13)
C

Also, Mσ (w) is compactly supported when σ > 1, while it tends to 0 as |w| → ∞*σ are Fourier duals of each other.
when 1/2 < σ ≤ 1. The functions Mσ and M*
It is worthwhile noting that Mσ has the Dirichlet series expansion*σ (z) =
M

∞
%

n=1

λz (n)λz (n)n−2σ ,(4.14)

92

Y. Ihara and K. Matsumoto

where λz (n) is defined by
L(s, χ)iz/2 =∞%λz (n)χ(n)n−s .

(4.15)

n=1

Since λz (n) # n& for any . > 0, the series (4.14) is absolutely convergent forσ > 1/2 (see [11]).

5 Mean value theorems play a role
Now our aim is to show that the “M -function” Mσ , constructed in the preceding
section, indeed satisfies (1.2) for any σ > 1/2 and for a reasonably large familyof test functions Φ. We briefly outline the proof for Case (C). A fundamental
idea in [10] (as well as in [8]) is to consider the case Φ = ψz first. In this case*σ (z), and so, what we have to show is thatthe right-hand side of (3.4) is M+++
+ 1 # T+*σ (z)++(5.1)
ψz (log ζ(σ + iτ ! ))dτ ! − M+++ 2T −Ttends to 0 as T → ∞. (We can easily see that ζ(s + iτ ! ) can be replaced by
ζ(σ + iτ ! ).) The quantity (5.1) can be estimated as++# T+ 1 # T
+
1
+
!
!
!
!+
≤+
ψz (log ζ(σ + iτ ))dτ −ψz (log ζP (σ + iτ ))dτ ++ 2T −T
+
2T −T
+
+
#
+ 1
+
T
+
*σ,P (z)++ + |M
*σ,P (z) − M*σ (z)|++
ψz (log ζP (σ + iτ ! ))dτ ! − M+ 2T −T+= XP (z) + YP (z) + ZP (z),

(5.2)

say.
If we choose y = y(T ) such that y → ∞ as T → ∞, then (4.12) implies that
ZP (z) → 0 as T → ∞.
To estimate XP (z), we use upper bound estimates of the mean square offP (σ + iτ ! ) =ζ(σ + iτ ! )− 1,
ζP (σ + iτ ! )which were proved in [15]. In the general theory of Dirichlet series, an asymptotic
formula for the mean square of Dirichlet series due to Carlson [3] is known. The
proof of the mean square estimates given in [15] is inspired by a proof of Carlson’s
theorem described in Titchmarsh [18].
Consider YP (z). From (4.3) (in the case L = ζ, χ = χτ ! ) we haveψz (log ζP (σ + iτ ! )) = ψz (gσ,P (χP )).
We use the Fourier expansion

On the value-distribution of log L and L! /Lψz (gσ,P (tP )) =%P
Aσ,P (nP , z)tn
P ,

93

(5.3)

nP ∈ZP

where

ZP =

$

p∈P

Z,

nP = (np )p∈P ∈ ZP ,

P
tn
P =

$

p∈P

tnp p ∈ TP ,and Aσ,P (nP , z) are the Fourier coefficients. Among them, the constant term is*σ,P (z) (where 0 = (0)p∈P ), and hence
Aσ,P (0, z) = M
+
+
+
+
+
+ %
# T $
+
+
1
−iτ ! np log p!++
YP (z) = +edτ +Aσ,P (nP , z)
2T −T+nP ∈ZP+p∈P++ nP '=0++
1 % ++ Aσ,P (nP , z) ++
#
(5.4)
+&
+.
+ p∈P np log p +T
nP ∈ZPnP '=0We can show a certain upper bound of each Fourier coefficient Aσ,P (nP , z), andalso a mean value estimate of them (by the method similar to Section 5 of [8]).
Applying those estimates to the right-hand side of (5.4), we obtain an upperbound of YP (z).
Lastly, choosing y = (log T )ω , where ω is a small positive constant, we findthat both XP (z) and YP (z) tend to 0 as T → ∞. Now we are done in the case
Φ = ψz . Moreover we can observe that, in this case, the convergence to the limit*σ (z) is uniform in |z| ≤ R for any R > 0. Noting this uniformity, we canMdeduce the general case of (i) and (ii) of Theorem 3.2 from this case by passage
to the Fourier dual and by suitable approximations.
In Case (A) with K = Q, the basic structure of the argument is similar. The
quantity corresponding to XP (z) is estimated by using the idea of Section 4 of[15], combined with a mean square estimate offP (s, χ) =L(s, χ)−1
LP (s, χ)with respect to χ. The latter estimate is reduced to a mean square estimate ofDirichlet L-functions, which can be shown as an analogue of Gallagher’s result[4]. The quantity corresponding to YP (z) is treated by the method in Section 6
of [8].

6 The function field caseIn the previous sections we have seen how to apply mean value estimates to obtain sharp results in the log L case, K = Q. The same idea can surely be applied
to other cases. In this section we consider the case when K is a function field,
and explain how to improve and generalize the results (a) and (b) mentioned in94

Y. Ihara and K. Matsumoto

Section 2. We hope this is not just a case study but will give some insight intoand prospect for the number field cases.
In a recent article [9] we have shown that in the function field case, formula(1.1) (with the meaning (2.3) of the average) holds under the condition:(c) σ > 1/2, and Φ is any continuous function of at most polynomial growth.
Of course condition (c) contains both (a) and (b). The main point of the proof
is an upper bound estimate for power mean values(k > 0 fixed) of

AvgX(f ) |g(s, χ, y)|2k

(6.1)

L!
L!
(s, χ) − P (s, χ),
(6.2)
L
LP
where P = Py := {℘ : prime divisor on K, *= ℘∞ , N (℘) ≤ y}. To evaluate (6.1),
we write,##1g(s, χ, y) =−
Γ (w)g(s + w, χ, y)X w dw
2πi
Re w=c
Re w=−&= Int+ − Int− ,(6.3)
g(s, χ, y) =say, where c > max(0, 1 − σ), . is a small positive number, X ≥ 1. We estimate|Int− | using Weil’s proof of the Riemann Hypothesis [20], and the power meanvalues of |Int+ | by using orthogonality relation for characters. A suitable choiceof X depending on N (f ) will give a simple and rather surprising result to theeffect that the mean value (6.1) tends to 0 as y → ∞, uniformly in f . Combining
this with (b) mentioned above in Section 2, we can prove (1.1) for any Φ satisfying
condition (c) directly (first for Φ ∈ C 1 with compact support, and then forgeneral Φ by approximation); that is, it is not necessary to treat the case Φ =ψz before treating the general case. This is a big difference from the proof of
Theorem 3.2.
The above result is another example of successful application of mean value
theorems. However, even condition (c) is not the weakest condition we have everobtained. To state a further stronger result, we first define another variant of
average by'(
Avg(3)
φ(χ)
=
lim
Avg
φ(χ)
.
(6.4)
X(f )
χ
f :prime

N (f )→∞

Then the following theorem is proved in [11]:Theorem 6.1. Let K be a function field, and by Avgχ we mean the average
defined by (6.4). Then both (1.1) and (1.2) hold for any σ > 1/2 and any Φsatisfying (ii) (in Theorem 3.2) or(iii) Φ is any continuous function of at most exponential growth, i.e., Φ(w) #ea|w| holds for some a > 0.
The proof of Theorem 6.1 is again different from both in [10] and in [9]. In [11]we also treat the number field case, which will be discussed in the next section.

On the value-distribution of log L and L! /L95

7 Under the GRH
According to the well-known principle on the analogy between function fields
and number fields, we may expect that the analogue of Theorem 6.1 holds fornumber fields, at least under the assumption of the GRH. In fact, in [11] weprove the following theorem.
Theorem 7.1. Let K be the rational number field or an imaginary quadratic
field, and consider Case (A) with the meaning (6.4) of the average. We assume
the GRH for L(s, χ). Then both (1.1) and (1.2) hold for any σ > 1/2 and any Φsatisfying (ii) or (iii).
In [11], Theorem 6.1 and Theorem 7.1 are proved simultaneously. Therefore
hereafter we assume that K is either Q, an imaginary quadratic field, or afunction field, and assume the GRH when K is a number field. We have seenin the previous sections that mean value theorems play a key role in the proofof our theorems. In particular, in Section 5 we mentioned that a part of ourproof was inspired by Carlson’s theorem. In [11], we prove a certain variant ofCarlson’s theorem, which is quite essential in our argument.
To explain our variant of Carlson’s theorem, first recall the expression (4.14)*σ (z). Here we introduce the following more general Dirichlet series:
for M%*s (z1 , z2 ) =M
λz1 (D)λz2 (D)N (D)−2s ,(7.1)
Dwhere s, z1 and z2 are complex variables, D runs over all integral divisors of K,
N (D) is the norm of D, and λz (D) is defined by%
L(s, χ)iz/2 =λz (D)χ(D)N (D)−s .
(7.2)
D*s (z1 , z2 ) in the
It is also possible to define the same type of function M
L /L case, which was already done in [8]. Basic properties and the behaviour of*s (z1 , z2 ) (resp. M*s (z1 , z2 )) have been discussed in [7], [8] (resp. [11]).
M
We also consider the quasi-characters ψz1 ,z2 : C → C× parametrized by
z1 , z2 ∈ C defined by!
"i(z1 w + z2 w) ,
ψz1 ,z2 (w) = exp2!

which will be the "basic case" for Φ in the proof of Theorems 6.1 and 7.1. Our
Carlson’s theorem can be written as follows.
Theorem 7.2. Let K be as in Theorem 6.1 or Theorem 7.1, and assume the
GRH if K is a number field. Then! !
"L
*σ (z1 , z2 )Avg(3)
ψ
(s,
χ)
=M
(7.3)
z1 ,z2
χL

96

Y. Ihara and K. Matsumotoand*Avg(3)
χ ψz1 ,z2 (log L(s, χ)) = Mσ (z1 , z2 )(7.4)

hold uniformly in |z1 |, |z2 | ≤ R and for s = σ + iτ with σ ≥ 1/2 + . and, in thenumber field case, |τ | ≤ T .
To prove this theorem, it is necessary to obtain a sufficiently sharp upper boundof (L! /L)(s, χ) for σ > 1/2. Such an upper bound can be shown by using thepolynomial expression of the L-function in the function field case, and by using
Weil’s explicit formula ([19]) in the number field case.
The necessary estimate in the log L case can be deduced by integrating theresult in the L! /L case.
When z2 = z1 , we see that ψz1 ,z2 = ψz1 . Therefore Theorem 7.2 gives (1.1)
and (1.2) in the case Φ = ψz1 . From this result, we obtain the assertions ofTheorems 6.1 and 7.1 again by suitable approximations. The proof that Φ canbe of exponential growth follows by comparing the following two facts: first,
Mσ (w), Mσ (w) # exp(−λ|w|2 )for any λ > 0, and secondly,
+"
! + !
+L
+
AvgX(f ) exp a ++ (s, χ)++ , AvgX(f ) exp(a| log L(s, χ)|) # 1
L

(7.5)

(7.6)

for any a > 0. The first is a simple generalization of a result of Jessen and Wintner [13], while the second can be shown from Theorem 7.2 (here it is importantthat Theorem 7.2 is proved not only for z2 = z1 , but for general z1 and z2 ).
The conditions on Φ in our unconditional Theorem 3.2 is obviously much
more restrictive than the conditions in Theorem 7.1. It seems very difficult toprove the claim of Theorem 7.1 unconditionally at present, but we believe thatthe methods developed in [9, 10, 11] will give a base for further progress in the
future.

References
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97

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