This paper studies the spectral properties of certain self-affine measures based on Sierpinski structures, providing conditions for spectrality and orthogonality of exponential functions.
Contribution
It offers new necessary and sufficient conditions for these measures to be spectral, especially distinguishing cases where the matrix is isotropic or anisotropic.
Findings
01
Derived conditions for spectrality when
ho_1 =
ho_2.
02
Quantified maximum orthogonal exponential functions in absence of spectrality.
03
Established criteria for spectral measures with
ho_1
eq
ho_2.
Abstract
We investigate the spectral properties of a class of Sierpinski-type self-affine measures defined by \[ \mu_{M,D}(\cdot) = p^{-1} \sum_{d \in D} \mu_{M,D}(M(\cdot) - d), \] where \( p \) is a prime number, \( M = \begin{bmatrix} \rho_1^{-1} & c 0 & \rho_2^{-1} \end{bmatrix} \) is a real upper triangular expanding matrix, and \( D = \{d_0, d_1, \cdots, d_{p-1}\} \subset \mathbb{Z}^2 \) satisfying \( \mathcal{Z}(\widehat{\delta}_{D}) = \cup_{j=1}^{p-1} \left( \frac{j \bm{a}}{p} + \mathbb{Z}^2 \right) \) for some \( \bm{a} \in \mathcal{E}_{p}= \{ (i_1, i_2)^* : i_1, i_2 \in [1, p-1] \cap \mathbb{Z} \} \), where \( \mathcal{Z}(\widehat{\delta}_{D}) \) denotes the set of zeros of \( \widehat{\delta}_{D} \) with \( \delta_{D} = \frac{1}{\# D} \sum_{d \in D} \delta_d \). When ρ1=ρ2, we derive necessary and sufficient conditions for μM,D to both: (i) possess…
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Full text
On the Spectral Properties of a Class of Planar Sierpinski Self-Affine Measures
Jia-Long Chen1
and
Wen-Hui Ai2∗
1 School of Mathematics, South China University of Technology, Guangzhou, 510641, P. R. China
2 Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education),
School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, P. R. China
We investigate the spectral properties of a class of Sierpinski-type self-affine measures defined by
[TABLE]
where p is a prime number, M=[ρ1−10cρ2−1] is a real upper triangular expanding matrix, and D={d0,d1,⋯,dp−1}⊂Z2 satisfying Z(δD)=∪j=1p−1(pja+Z2) for some a∈Ep={(i1,i2)∗:i1,i2∈[1,p−1]∩Z}, where Z(δD) denotes the set of zeros of δD with δD=#D1∑d∈Dδd. When ρ1=ρ2, we derive necessary and sufficient conditions for μM,D to both: (i) possess an infinite orthogonal set of exponential functions, and (ii) be a spectral measure. When no infinite orthogonal exponential system exists in L2(μM,D), we quantify the maximum number of orthogonal exponentials and provide precise estimates. For ρ1=ρ2, with restricted digit sets D, we obtain a necessary and sufficient condition for μM,D to be a spectral measure.
The research is supported in part by the NNSF of China (Nos. 12201206 and 12371072), the Hunan Provincial NSF (No. 2024JJ6301).
∗Corresponding author.
1. Introduction
Let μ be a Borel probability measure on Rn with compact support, and let L2(μ) denote the Hilbert space associated with μ. The problem of approximating functions in L2(μ) by “well-behaved” functions has a long-standing history. This area connects various fields such as analysis, geometry, and topology. The most effective approximation occurs when L2(μ) possesses a basis consisting of complex exponentials, forming what is known as the Fourier basis. This work investigates the existence conditions for Fourier bases in L2(μ), with special attention to singular measures μ on Rn. More precisely, we say that L2(μ) has a Fourier basis if there exists a countable subset Λ⊂Rn such that the set of complex exponentials
[TABLE]
forms an orthonormal basis for L2(μ). In this case, we refer to μ as a spectral measure, Λ as the spectrum of μ, and (μ,Λ) as a spectral pair. Specifically, if the normalized Lebesgue measure restricted to a set Ω is a spectral measure, we refer to Ω as a spectral set.
In 1974, Fuglede established a significant connection between the existence of commuting self-adjoint partial differential operators and spectrality, leading to the formulation of the renowned spectral set conjecture in [14]. This conjecture is known to be false in dimensions n≥3[21, 18, 19]. However interest in this conjecture is still alive.
Let {φd(x)}d∈D be an iterated function system (IFS) defined by
[TABLE]
where M∈Mn(R) is an n×n expanding real matrix (i.e., all eigenvalues of M have absolute values greater than 1), and D⊂Rn is a finite set of digits. It is well known that there exists a unique non-empty compact set
[TABLE]
such that T=∪d∈Dφd(T) [15].
Additionally, there exists a unique probability measure μ:=μM,D supported on T that satisfies
[TABLE]
where #D denotes the cardinality of D. The set T and the measure μM,D are called the self-affine set (or attractor) and the self-affine measure, respectively. In particular, if M is a scalar multiple of an orthonormal matrix, then T and μM,D are called the self-similar set and self-similar measure, respectively.
In 1998, Jorgensen and Pedersen [17] discovered the first singular and non-atomic spectral measure. They proved that the one fourth Cantor measure μ4 supported on T={∑i=1∞4−idi:di∈{0,2}}, is a spectral measure.
Since then, the study of singular spectral measures has flourished in the field of fractal analysis. For the spectrality of Bernoulli convolution in R, it was fully resolved, see [16, 10, 8, 11].
In R2, the most work of the spectra measure was concentrated on the self-affine Sierpinski-type measures, see [9, 12, 20]. In [20], Lu et al. investigated the self-affine measure generated by the upper triangular matrices and some ternary digit sets.
Recently, Yan [23] extends the results of [12] to the Moran measure generated by an expanding real diagonal matrix and some sets of p-ary numbers satisfying a certain zero condition, where p is a prime. Fractal measures associated with this zero set have been extensively studied, yielding many significant results; see [5, 6, 7, 22] for further details.
Their results have inspired us to investigate the spectral properties of the self-affine measure
μM,D with
[TABLE]
where 0<ρ1,ρ2<1 and p is a prime number. Let μ be the Fourier transform of μ, and Z(f) the zeros of f. The technique is to make use
of some explicit expressions of Z(μM,D).
In this paper, we consider the set D satisfying
[TABLE]
Here (i1,i2)∗ denotes the transposed conjugate of (i1,i2), δD=#D1∑d∈Dδd and δd
represents the Dirac measure at d.
Actually, many digit sets satisfy the form in equation (1.3). For example, let D1={0,1,⋯,N−1} and D2={(0,0)∗,(1,0)∗,(0,1)∗}, by direct calculation, we have
Z(δD1)={Nj+Z:j=1,2,⋯,N−1}, Z(δD2)={±(31,−31)∗+Z2}.
Suppose that ρ1=ρ2. We first establish the following two results, which provide necessary and sufficient conditions for L2(μM,D) to admit an infinite set of orthogonal exponential functions. If L2(μM,D) does not possess an infinite orthogonal set, we can estimate the maximal number of orthogonal exponential functions and determine its exact value.
Theorem 1.1**.**
Suppose that ρ1=ρ2=ρ. Let μM,D be given by (1.1), (1.2)and (1.3). Then L2(μM,D) admits an infinite orthogonal set of exponential functions if and only if ρ−1=(st)r1 and c=κρ−1 for some s,t,r∈N with t∈pZ, gcd(s,t)=1 and κ∈Q.
Theorem 1.2**.**
Suppose that ρ1=ρ2=ρ. Let μM,D be given by (1.1) (1.2)and (1.3). If ρ−1=(st)r1 for some s,t,r∈N with gcd(s,t)=1. Then the following statements hold:
(i)
If c=κρ−1 for any κ∈Q, then there exist at most p mutually orthogonal exponential functions in L2(μM,D), and the number p is the best.
2. (ii)
If c=κρ−1 for some κ∈Q and t∈/pZ, then
(a)
If s∈/pZ, then there exist at most p mutually orthogonal exponential functions in L2(μM,D), and the number p is the best.
2. (b)
If s∈pZ, then there are any number of orthogonal exponential functions in L2(μM,D).
The following result establishes a necessary and sufficient condition for μM,D to be a spectral measure when ρ1=ρ2. Consequently, [3, Conjecture 4.2] is incorrect, see the counterexample 6.1.
Theorem 1.3**.**
Suppose that ρ1=ρ2=ρ. Let μM,D be given by (1.1) (1.2)and (1.3). Then, μM,D is a spectral measure if and only if ρ−1∈pZ and c∈{st:t∈pZ,gcd(t,s)=1}∪{0}.
For the case ρ1=ρ2, let d0=0, d1=(d1,1,0)∗=0, then
[TABLE]
Define
[TABLE]
and
[TABLE]
It is easy to check that μM,D and μM′,D′ have the same spectrality. Let c′=d1,1(ρ1−1−ρ2−1)c, c′′=d1,1c′. For
a=(a1,a2)∗∈Ep={(i1,i2)∗:i1,i2∈[1,p−1]∩Z}, define
[TABLE]
With this definition, we establish the following result.
Theorem 1.4**.**
Suppose that ρ1=ρ2. Let μM,D be given by (1.1), (1.2), (1.3) and (1.4). If c′′∈/Q, then μM,D is not a spectral measure.
This result also indicates that if ρ1=ρ2, a necessary condition for μM,D to be a spectral measure is c′′∈Q. Consequently, when examining the necessary conditions, we can restrict our attention to the case where c′′∈Q. Divide Q into Q∖Ea and Ea, yielding the following two results.
Theorem 1.5**.**
Suppose that ρ1=ρ2. Let μM,D be given by (1.1), (1.2), (1.3) and (1.4). Let Ea be given by (1.5) and c′′∈Q∖Ea. Then μM,D is a spectral measure if and only if ρ1−1∈pZ.
It is noteworthy that although ρ1=ρ2, we can conclude that when c′′∈Q∖Ea, the spectrality of μM,D is independent of ρ2. This is an intriguing phenomenon.
Theorem 1.6**.**
Suppose that ρ1=ρ2. Let μM,D be given by (1.1), (1.2), (1.3) and (1.4). Let Ea be given by (1.5) and c′′=c2c1∈Ea, where gcd(c1,c2)=1, c2∈pℓ(Z∖pZ) for some ℓ∈N. Then μM,D is a spectral measure if and only if ρ1−1,ρ2−1∈pZ and pℓ+1∣(ρ1−1−ρ2−1).
By synthesizing the results of Theorems 1.3 to 1.6, we obtain the following theorem, which establishes the necessary and sufficient conditions for μM,D to be a spectral measure.
Theorem 1.7**.**
Let μM,D be given by (1.1), (1.2), (1.3) and (1.4), and let Ea be given by (1.5). Then μM,D is a spectral measure if and only if M and D satisfy one of the following conditions:
(i)
ρ1=ρ2, ρ1−1∈pZ, and c′′∈Q∖Ea.
2. (ii)
ρ1=ρ2, ρ1−1,ρ2−1∈pZ, and c′′=ts∈Ea, t∈pℓ(Z∖pZ), pℓ+1∣(ρ1−1−ρ2−1) for some ℓ∈N.
3. (iii)
ρ1=ρ2∈pZ* and c∈{st:t∈pZ,gcd(s,t)=1}∪{0}.*
The paper is organized as follows. In Section 2, we introduce key definitions and results that are essential for proving our main theorems and provide a foundation for the study of spectral measures. In Section 3, we establish the necessary conditions for μM,D to be a spectral measure and prove Theorem 1.4. In Section 4, we analyze the case ρ1=ρ2, considering both the spectrality and non-spectrality of μM,D, i.e., we give the proofs of Theorems 1.1, 1.2 and 1.3. In Section 5, we examine the case ρ1=ρ2, with a focus on proving Theorems 1.5 and 1.6. Finally, in Section 6, we offer some examples.
2. Preliminaries
In this section, we present some preliminary lemmas and notations that will be used throughout the paper. Let μ be a probability measure with compact support in R2. The Fourier transform of μ is defined in the usual manner:
[TABLE]
It is straightforward to show that Λ is an orthogonal set with respect to μ if and only if
[TABLE]
In other words, the family EΛ={e−2πi⟨λ,x⟩:λ∈Λ} is an orthogonal family in L2(μ) if and only if
(Λ−Λ)∖{0}⊂Z(μ),
where Z(μ) denotes the zero set of the Fourier transform μ. We also say that Λ is a bi-zero set of μ.
Based on (1.1), we can express μM,D as an infinite convolution of the following form:
[TABLE]
where δE=#E1e∈E∑δe, δe is the Dirac measure at the point e∈E and the convergence is in weak sense.
where c′=d1,1(ρ1−1−ρ2−1)c, and D satisfies (1.4). Then,
[TABLE]
Since similarity transformations preserve the spectral properties of a self-affine measure [13], the spectral characteristics of μM,D and μM′,D′ are identical. By combining (2.2) and (2.3), it is straightforward to verify that
[TABLE]
where a=(a1,a2)∗∈Ep. Define the function
[TABLE]
The following theorem establishes a fundamental criterion for determining the spectrality of the measure μ [17].
Let μ=μ0∗μ1 be the convolution of two probability measures μi, i=0,1, and they are not Dirac measures. Suppose that Λ is a bi-zero set of μ0, then Λ is also a bi-zero of μ, but cannot be a spectrum of μ.
The following lemma is well-known.
Lemma 2.3**.**
([1, Lemma 2.2])*
Let I be a compact set in R2. Then, the set F(I), which denotes the Fourier transforms of all Borel probability measures supported on the compact set I⊂R2, is equi-continuous.*
Next, we give the definition of compatible pair, which is essential for the construction of the spectrum.
Let M∈Mn(Z) be an n×n expansive matrix with integer entries. Let B,L⊂Zn be a finite set of integer vectors with #B=#L=N and 0∈B∩L. We say that (M−1B,L) forms a compatible pair (or (M,B) is admissible, or (M,B,L) forms a Hadamard triple)
if the matrix
[TABLE]
is unitary, i.e., HH∗=I, where H∗ denotes the transposed conjugate of H.
It is highly convenient to construct a family of orthogonal exponential functions for L2(μ) using a Hadamard triple. However, the primary challenge lies in verifying whether the constructed set Λ forms an orthonormal basis for L2(μ). For self-affine measures, we have the following well-known result.
Let (M,B,L) be a Hadamard triple. Then the self-affine measure μM,B is spectral.
For n≥1, define
[TABLE]
and
[TABLE]
Let Λn be a bi-zero set of μn with 0∈Λn and Λn⊂Λn+1 for n≥1. Then Λ=∪n=1∞Λn is an orthogonal set of μM,D.
The following proposition determines whether Λ is a spectrum of μM,D. Since the proof follows a similar approach as [2, Theorem 2.3], we omit it.
Let ρ=ts∈(0,1) with s>1 and {fn(x)}n=1∞ satisfy ∣fn(x)∣≤1 for any x∈R. Suppose that there exists a positive constant b0<1 such that ∣fn(x)∣≤b0 for any ∣<x>∣≥2t1, where <x>∈(−21,21] and x−<x>∈Z. Then there is a constant c0>0 such that
Let ρ=ts∈(0,1) with s>1 and gcd(s,t)=1. Suppose that a continuous non-negative real-valued function f(x1,x2,⋯,xn) with f(x1,x2,⋯,xn)≤1 satisfies the following conditions:
(i)
There exist non-zero integers k1,k2,⋯,kn such that
[TABLE]
2. (ii)
There exists an integer γ∈N+ such that
[TABLE]
Then, there exist two constants c0,β>0 such that
[TABLE]
for any sequence {xl,i:2≤i≤n,1≤l}⊂R.
At the end of this section, we present a conclusion that reflects the structure of the digit set satisfying condition (1.2).
Lemma 2.9**.**
Let D={d0,d1,⋯,dp−1}⊂Z2, and let Ea be defined by (1.2) and (1.5), respectively. Set di=(di,1,di,2), c′′=c2c1∈Q∖Ea with gcd(c1,c2)=1 and B={c2d0,1+c1d0,2,⋯,c2dp−1,1+c1dp−1,2}, then
[TABLE]
Proof.
Assume that B={0,1,⋯,p−1}(modp). Since #B=p, there exist i=j∈{0,1,⋯,p−1} such that
c2di,1+c1di,2≡c2dj,1+c1dj,2(modp).
This implies that
c2(di,1−dj,1)+c1(di,2−dj,2)≡0(modp).
By c′′∈Q\Ea, we have c2a2−c1a1∈pZ.
Since p is prime, gcd(c1,c2)=1 and 1≤ai≤p−1, we have gcd(p,ai)=gcd(p,ci)=1 and
[TABLE]
By Z(mD)=∪j=1p−1(pja+Z2), we obtain that ∑k=0p−1e−2πi⟨pma,dk⟩=0 for any m=1,2,⋯,p−1.
Hence
[TABLE]
By gcd(p,ai)=1 and p is prime, it is easy to see
[TABLE]
Then for k=i,j, ∑m=0p−1e−2πipm(a1(dk,1−dj,1)+a2(dk,2−dj,2))=0 or p, while the right-hand side of (2) is equal to p, resulting in a contradiction. Hence, B={0,1,⋯,p−1}(modp).
By the fundamental theorem of arithmetic, let gcd(B)=h, where h is a prime number. Now, we prove that h=1.
Clearly, h=p. Then, we may assume that c2di,1+c1di,2=hti. From B={0,1,⋯,p−1}(modp), we obtain {ti}i=0p−1={0,1,⋯,p−1}(modp). Furthermore,
[TABLE]
which implies that ph(c2,c1)∗∈Z(mD)=∪j=1p−1(pja+Z2). Therefore, (c2,c1)∗∈hZ2. Combining with gcd(c1,c2)=1, we have h=1.
∎
3. Necessary conditions
In this section, we first establish that if μM,D is a spectral measure, then ρ1 can be expressed as ts for some s,t∈N with gcd(s,t)=1 and p∣t. Subsequently, under the assumptions that μM,D satisfies conditions (1.1), (1.2), (1.3) and (1.4) with ρ1=ρ2, we further prove that if μM,D is a spectral measure, then ρ1−1∈pZ and c′′∈Q (Theorem 1.4). Prior to delving into the proofs, we introduce the concept of the selection map and present several lemmas that will be instrumental in the subsequent arguments of this section.
Denote Ξ={0,1,⋯,p−1}, Ξk={I=i1i2⋯ik:ik∈Ξ} for k≥1 and Ξ∗=∪k=0∞Ξk, where Ξ0={∅}. Notice that Ξ∗ can be thought of as a tree: the root is the empty set ∅, and for each node I, the nodes following it are IΞ. In particular,
∅I=I, I0∞=I00⋯, 0k=0⋯0∈Ξk.
Let (Λ−Λ)\{0}⊂{ρ−kℓ:k≥1,ℓ∈∪i=1p−1(pi+Z)} for a real number 0<ρ<1. Then, Λ is an infinite set if and only if ρ is the r-th root of a fraction ts, where gcd(s,t)=1, p∣t.
Dai etal. [11] obtained the following result, which illustrates the relationship between maximal orthogonal sets and selection maps.
Lemma 3.3**.**
([11, Theorem 4.5] )*
Suppose that ρ=ts∈(0,1) with p∣t, and*
[TABLE]
with 0∈Λ. Then Λ is maximal if and only if there exists j0≥1 and a selection map γ such that
Λ=ρ−j0p−1(γ∗(Ξγ)).
For convenience, for any set Λ⊂R2, we define
[TABLE]
where i=1,2. Similar to [20, Proposition 3.3], we give the structure of the spectrum.
Proposition 3.4**.**
Let ν=δ1∗δ2∗⋯∗δn∗⋯ be a spectral measure satisfying #sptν=∞ and
[TABLE]
where 0<ρ<1 and η=0. If 0∈Λ is a spectrum of ν, then ρ=ts with t∈pZ, and there exist a selection map γ and a subset Ξ′⊂Ξγ such that
[TABLE]
Moreover, the spectrum Λ can be decomposed as
[TABLE]
where Λn:={λ(I):∣I∣≤n,I∈Ξ′} is a bi-zero set of νn=δ1∗δ2∗⋯∗δn.
Proof.
By adapting the methodology used in the proof of Proposition 3.3 in [20] and leveraging Lemmas 2.2, 3.2, and 3.3, the proof of this proposition follows straightforwardly.
∎
Remark 3.5**.**
For any λ=(λ1,λ2)∗∈Λ, by the above proposition, there exists a unique word I∈Ξ′ such that
λ1=ηρ−1p−1γ∗(I).
We can denote λ1=λ1(I) and λ=λ(I).
It is straightforward to verify that the measure μM,D, defined by conditions (1.1), (1.2) and (1.3), satisfies the requirements of Proposition 3.4. Thus, we conclude that if μM,D is a spectral measure, then ρ1=ts with t∈pZ. In the remainder of this section, we will assume that the digit set D satisfies conditions (1.2), (1.3)and (1.4). We first prove that if μM,D is a spectral measure, then ρ1∈pZ, followed by the proof of Theorem 1.4.
Let Q=[d1,1−1001]. Then,
[TABLE]
and μM′′,D′′ shares the same spectral property as μM,D. Write
[TABLE]
It is readily verified that
[TABLE]
fulfilling the conditions of Proposition 3.4. Consequently, we establish the following lemma.
Proposition 3.6**.**
Let ρ1=ts with s>1 and t∈pZ. Suppose there exist α,β>0 such that for all n≥1 and ξ=(ξ1,ξ2)∗∈R2 with ∣ρ1nξ1∣>1,
[TABLE]
Then μM,D is not a spectral measure.
Proof.
Suppose that μM,D is a spectral measure and that 0∈Λ is a spectrum of μM′′,D′′. From Proposition 3.4, we know that there exists a selection map γ and a subset Ξ′⊂Ξγ such that Λ(1)=d1,1ρ1−1p−1(γ∗(Ξ′))⊂d1,1ρ1−1p−1(γ∗(Ξγ)). Additionally, Λ=∪n=1∞Λn, where Λn:={λ(I):∣I∣≤n,I∈Ξ′}.
For any n≥1 and λ=(λ1,λ2)∗∈Λ∖Λn, there exists I∈Ξ′ with ∣I∣≥n+1 such that
λ1=λ1(I)=d1,1ρ1−1p−1γ∗(I).
This implies that
[TABLE]
Let K>β−1 and αn=nK. Then, there exists a positive integer n0 such that
(n+1)K≤(1+21logts)nKfor any n≥n0.
For any ξ=(ξ1,ξ2)∗∈[0,∣psd1,1∣]2 and nK<∣I∣≤(n+1)K, it follows from (3.3) that
[TABLE]
for some constants C,N0>0, and for all n≥N0≥n0. Since ∑n=1∞n2C2<∞ and by applying Lemma 2.6 (ii), we conclude that Λ cannot be a spectrum of μM′′,D′′, which leads to a contradiction. This completes the proof that
μM,D is not a spectral measure.
∎
Lemma 3.7**.**
Let μM,D be given by (1.1), (1.2), (1.3)and (1.4). If μM,D is a spectral measure, then ρ1−1∈pZ.
Proof.
If μM,D is a spectral measure, μM′′,D′′ is also a spectral measure. From Proposition 3.4, it follows that ρ1−1=s1t1, where t1∈pZ and gcd(s1,t1)=1. Therefore, we only need to prove that s1=1. By contradiction, assume that s1>1. Let ξ=(ξ1,ξ2)∗, and define fn(ξ1):=∣mM′′−nD′′((ρ1−nξ1,ξ2)∗)∣. Then, it follows that μM′′,D′′(ξ)=Πj=1∞fj(ρ1jξ1). Note that
[TABLE]
For convenience, we can denote
[TABLE]
where ϑn is determined by ρ1, ρ2, and cd1,1−1.
Furthermore, we can estimate fn(ξ1) as
[TABLE]
It is easy to see that if ∣⟨ξ1⟩∣≥2t11, then fn(ξ1)<1. For each n>1, by Lemma 2.7, there exist α,β>0 such that
[TABLE]
for any ∣ρ1nξ1∣>1. It follows from Proposition 3.6 that μM′′,D′′ is not a spectral measure, a contradiction. Hence ρ1−1∈pZ.
∎
At the end of this section, we will prove that when ρ1=ρ2 and c′′∈/Q, μM,D is not a spectral measure. From (2.2), we define Zk:=M′∗kZ(mD′), which will be used frequently later.
Lemma 3.8**.**
Let ρ1−1=t1∈pZ, and let μM′,D′ be a spectral measure with spectrum Λ. Then, there exists λ∈Λ such that (Λ−λ)∩Zk=∅ for all k=1,2,3, where Zk=M′∗kZ(mD′).
Proof.
From Proposition 3.4, we obtain the decomposition Λ=∪n=1∞Λn, where each Λn:={λ(I):∣I∣≤n,I∈Ξ′} forms a bi-zero set for the measure μn′. If (Λ−Λ)∩Z3=∅, then (Λ−Λ)∖{0}⊆∪k=3Zk. By Lemma 2.2, it follows that Λ cannot be a spectrum of μM′,D′. This contradiction enables us to choose an element λ∈Λ such that (Λ−λ)∩Z3=∅. We claim that λ(I)−λ(J)∈Zsfor any I,J∈Ξ′,
where s=min{i:I∣i=J∣i}. In fact
[TABLE]
This shows that the claim is true.
If {iI∈Ξ′:i=1,2,⋯,p−1}=∅, then it follows that λ(I)−λ(J)∈∪k=2∞Zk for any I,J∈Ξ′. Using Lemma 2.2 again, we conclude that Λ is not a spectrum of μM′,D′, which leads to a contradiction. Thus, {iI∈Ξ′:i=1,2,⋯,p−1}=∅. Since 0∈Λ and by the claim, we have λ(I)∈Z1 for any I∈Ξ′ with I∣1=0. Therefore,
[TABLE]
Let ξ0:=d1,1p−1t1(t1,−ρ2−1c′)∗, then
[TABLE]
Furthermore, we have the following equation:
[TABLE]
where μ>1′ is defined similarly to (3.2).
We assert that ΛI:={λ(IJ):IJ∈Ξ′} is a bi-zero set of μ>n′ for any I∈Ξn. For any IJ=IJ′∈ΛI, by the first Claim, it follows that
[TABLE]
where s=n+min{j:IJ∣j=IJ′∣j}. Therefore, ΛI is a bi-zero set of μ>n′, and
[TABLE]
for any ξ∈R2. Assume that {0iI∈Ξ′:i=1,2,⋯,p−1}=∅. Then, from (3.4), it follows that
[TABLE]
From (3.5), the expression above is bounded as follows
[TABLE]
According to Lemma 2.9, we obtain p∤d1,1, and thus the last inequality holds. From Lemma 2.1, Λ is not a spectrum of μM′,D′, which leads to a contradiction.
∎
Suppose that μM′,D′ is a spectral measure. By Lemma 3.7, ρ1−1=t1∈pZ. According to Lemma 3.8, we can assume that 0∈Λ is a spectrum with Λ∩Zj=∅ for j=1,2,3. For each n=1,2,3 and λn∈Zn∩Λ, from (2.4), let
[TABLE]
where sn=(sn,1,sn,2)∗∈Ep and kn=(kn,1,kn,2)∗∈Z2. For any n>ℓ, the difference λn−λℓ can be written as
[TABLE]
Since λn−λℓ∈∪j=1∞Zj, it follows from the first component that λn−λℓ∈Zℓ=M′∗ℓR∗−1Z(mD). Therefore, the second component satisfies the following relationship
[TABLE]
We can then derive the following equation
[TABLE]
Note that t1n−ℓsnℓ,2+pknℓ,2+sℓ,2+pkℓ,2∈Q. This implies that for any ℓ1<ℓ2<n,
[TABLE]
Set ℓ1=1, ℓ2=2, and n=3. We obtain
[TABLE]
Returning to (3), we obtain c′,c′′∈Q, which leads to a contradiction of the initial assumption. Hence, μM′,D′ is not a spectral measure.
∎
4. The case ρ1=ρ2
In this section, we explore the scenario where ρ1=ρ2:=ρ and present the proofs of Theorems 1.1, 1.2, and 1.3. Initially, we establish a necessary and sufficient condition for the existence of an infinite orthogonal set of exponential functions in L2(μM,D). When such an infinite orthogonal set does not exist in L2(μM,D), we not only provide an estimate for the number of orthogonal exponential functions but also determine its maximal cardinality. Finally, we obtain a necessary and sufficient condition for μM,D to be a spectral measure.
Lemma 4.1**.**
Suppose that σ∈{(ts)r1:s,t,r∈N,gcd(s,t)=1} satisfies the equation
[TABLE]
where i,j,l are nonnegative integers. Then the following statements hold.
(i)
If k1,k2,k3∈Z∖{0}, then i≡j≡l(modr).
(ii)
If k1,k2,k3∈Z∖pZ, and s∈pZ or t∈pZ, where p is a prime, then at least two of the integers i,j,l must be equal.
Proof.
(i) This result is an immediate consequence of [8, Lemma 2.5]. (ii) Suppose, for the sake of contradiction, that i,j,l are distinct nonnegative integers. Without loss of generality, we assume that i>j>l. By (i), we can express i, j, and l as follows: i=i1r+k,j=j1r+k,l=l1r+k,
where i1>j1>l1 and 0≤k≤r−1. From k1σi+k2σj=k3σl, we obtain the equation
[TABLE]
which can be easily verified to be impossible, thus leading to a contradiction and completing the proof.
∎
"⇒". Suppose that L2(μM,D) admits an infinite orthogonal set EΛ. Then
[TABLE]
where
[TABLE]
We claim that Λ(1) is infinite. Suppose on the contrary that Λ(1) is finite, then Λ(2) must be infinite. By the pigeonhole principle, there exist λ1=λ2∈Λ∖{0} such that λ1,1=λ2,1, where λi=(λi,1,λi,2)∗ for i=1,2. Thus,
[TABLE]
which leads to a contradiction.
Thus, Λ(1) is an infinite set satisfying
[TABLE]
According to Lemma 3.2, we have ρ−1=(st)r1 for some s,t,r∈N with t∈pZ and gcd(s,t)=1.
In the following, we will prove that c=κρ−1, where κ∈Q.
Since #Λ=∞, without loss of generality, there exist λ1=λ2∈Λ∖{0} such that λ1,λ2∈H1. From the definition of H1, we can write
[TABLE]
and
[TABLE]
where i∈{1,2,⋯,p−1}, kjs=(kjs,1,kjs,2)∗∈Z2 for s=1,2,3, and a=(a1,a2)∗∈Ep.
By (4.1), we obtain
[TABLE]
and
[TABLE]
Note that ρ−1=(st)r1 and t∈pZ. By Lemma 4.1, we conclude that j1≡j2≡j3(modr), and at least two of j1,j2,j3 are equal. Since p(kj1,1−kj2,1)∈pZ and ia1+pkj3,1∈/pZ, it follows from (4.2) that j1,j2,j3 are not all equal. Without loss of generality, we assume that j1=j2=j3. Thus, combining with (4.2) and (4), we obtain that
[TABLE]
The necessity follows.
"⇐". Suppose that ρ−1=(st)r1 and c=κρ−1, where s,t,r∈N, t∈pZ, gcd(s,t)=1, and κ∈Q. Then gcd(s,p)=1.
Let κ=uv, where u∈N, v∈Z, and gcd(u,v)=1. Define
[TABLE]
Note that t=sρ−r, we have
ptpuℓa=ρ−rpuℓpspuℓa.
Furthermore, since
[TABLE]
we get
[TABLE]
It follows from (4.4), gcd(s,p)=1, and p(a2−rpuℓκa1)spuℓ∈pa2spuℓ+Z that Λ∖{0}⊂Z(μM,D).
For any λ1,λ2∈Λ∖{0} with λ1=λ2, we can write
[TABLE]
for two positive integers ℓ1>ℓ2. Similar to (4.4),
[TABLE]
which implies that (Λ−Λ)∖{0}⊂Z(μM,D). Therefore, EΛ forms an infinite orthogonal set in L2(μM,D). In summary, we have thus established the proof of the theorem.
∎
(i) We prove the conclusion by contradiction. Suppose, for the sake of argument, that there exists an orthogonal set EΛ such that #Λ>p. Without loss of generality, we assume that #Λ=p+1, and let Λ={λ0=0,λ1,λ2,⋯,λp}. For any distinct i,j∈{0,1,2,⋯,p}, λi−λj∈Z(μM,D)=∪j=1p−1Hj. Let c=κρ−1 where κ∈/Q. We can express λi as
[TABLE]
where {ji}i=1p⊂N is a monotonically decreasing sequence, kji=(kji,1,kji,2)∗∈Z2, li∈{1,2,⋯,p−1} for i=1,2,⋯,p, and a=(a1,a2)∗∈Ep. Considering the orthogonality of Λ and its first component, we have j1≡j2≡⋯≡jp(modr), which implies that there exists 0≤b≤r−1 such that
[TABLE]
Clearly, {hi}i=1p is a monotonically decreasing sequence. We will divide the proof into two cases as follows.
Case I.s,t∈/pZ. By the pigeonhole principle, and since s,t,lia1+pkji,1∈/pZ for i=1,2,⋯,p, it is clear that at least two of the following set
[TABLE]
are congruent modulo p. We can assume, without loss of generality, that
[TABLE]
Since λ1−λ2∈∪j=1p−1Hj, there exist jp+1∈N, kjp+1=(kjp+1,1,kjp+1,2)∗∈Z2, and lp+1∈{1,2,⋯,p−1} such that
[TABLE]
Using Lemma 4.1, j1≡j2≡jp+1(modr). Let jp+1=hp+1r+b. Then, the equation
[TABLE]
implies that
[TABLE]
One can easily derive that
[TABLE]
The left-hand side of the above equation belongs to pZ as indicated in (4.6), while the right-hand side does not. This leads to a contradiction.
Case II.s∈pZ,t∈/pZ or s∈/pZ,t∈pZ. By the pigeonhole principle, at least two of λ1,λ2,⋯,λp must fall into the same Hi. We may assume that λ1,λ2∈H1, and similarly to (4.5), (a1+pkj1,1)≡(a1+pkj2,1)(modp). Since λ1−λ2∈∪j=1p−1Hj, there exist jp+2∈N, kjp+2=(kjp+2,1,kjp+2,2)∗∈Z2, and lp+2∈{1,2,⋯,p−1} such that
Given that either s or t is an element of pZ, it follows from Lemma 4.1 that j1≡j2≡jp+2(modr), with at least two of the indices j1,j2,jp+2 being identical. Note that (a1+pkj1,1)≡(a1+pkj2,1)(modp), and lp+2a1+pkjp+2,1∈/pZ. It follows from (4.8) that j1, j2, and jp+2 cannot all be equal. If j1=j2, then j1=jp+2. Since j1≡jp+2(modr), there exists hp+2∈Z∖{0} such that j1−jp+2=hp+2r. Using ρ−1=(st)r1 in conjunction with (4.8) and (4), we arrive at the conclusion that
[TABLE]
This leads to a contradiction since κ∈/Q. Similarly, if j1=jp+2 or j2=jp+2, we can also derive a contradiction. Therefore, based on the above reasoning, there can be at most p mutually orthogonal exponential functions in L2(μM,D).
Let k∈N, and let
[TABLE]
It is straightforward to verify that (Λ−Λ)∖{0}⊆Z(μM,D). Therefore, having p elements is optimal, and the result follows.
(ii) Using a proof similar to that in Case I of (i), we can easily complete the proof of (a), and will omit the details here. We will now prove (b). Since κ∈Q and s∈pZ, we express κ=uv for some u∈N, v∈Z with gcd(u,v)=1, and let s=pℓs1 where gcd(s1,p)=1. For any N∈N and n≤N, we define
[TABLE]
Note that t,s1∈/pZ. It follows easily that
[TABLE]
For any λ1=λ2∈ΛnN, we define
[TABLE]
where n1>n2. From s=pℓs1 and t,s1∈/pZ, it follows that
[TABLE]
which implies that
[TABLE]
Combining with (4.10) and (4.11), we can conclude that for any N, EΛnN is the orthogonal set of L2(μM,D), thus completing the proof.
∎
Next, we proceed to prove Theorem 1.3. From Proposition 3.4, it follows that when μM,D is a spectral measure, ρ=ts with gcd(s,t)=1 and p∣t. Furthermore, we can establish the following proposition.
Proposition 4.2**.**
Let μM,D be given by (1.1), (1.2) and (1.3), where ρ1=ρ2=ρ=ts and c=κρ−1 for some s,t∈N with gcd(s,t)=1, p∣t, and κ∈Q. If s>1, then μM,D is not a spectral measure.
Proof.
The proof of this proposition follows by a straightforward adaptation of the proof of Proposition 3.2 in [4], and we omit the details here.
∎
Lemma 4.3**.**
Let μM,D be defined by (1.1), (1.2) and (1.3), assume that ρ1=ρ2=ρ−1=t∈pZ, c=a′t, and a′=uv∈{lk:gcd(k,l)=1 and k∈Z}, where gcd(v,u)=1 and u=pℓ1u′∈pℓ1(Z∖pZ) for some ℓ1∈N. If pℓ1+1∣t, then μM,D is a spectral measure.
Proof.
Let P=[u′001]. Then, we can write
M1=PMP−1=[t0pℓ1vtt]andD1=PD.
The spectral properties of μM,D and μM1,D1 are identical, and
[TABLE]
where a=(a1,a2)∗.
Define
[TABLE]
Since pℓ1+1∣t, it follows that L1⊂Z2. By calculation, we find that (M1,D1,L1) forms a Hadamard triple. Therefore, by Lemma 2.5, it follows that μM1,D1 is a spectral measure. This completes the proof.
∎
Lemma 4.4**.**
Suppose that ρ1−1=ρ2−1=t∈pZ and λj∈Lij for j=1,2. If λ2−λ1∈Z(μM,D), then λ2−λ1∈Li:=Z(δM−iD), where i=min{i1,i2} if i1=i2, and i≥i1 if i1=i2.
Proof.
Based on the structure of Lj, we can assume
[TABLE]
for some kj=(kj,1,kj,2)∗,sj=(sj,1,sj,2)∗∈Z2. Then,
[TABLE]
If i1=i2 and i<i1, the first component of the above vector is given by
[TABLE]
then
[TABLE]
Since p∣t, the left-hand side is divisible by p, while the right-hand side belongs to Z∖pZ, leading to a contradiction. Therefore, we conclude that i≥i1. Similarly, we assume that i2>i1, so λ2−λ1∈Li for some i≥i1. If i>i1, then
[TABLE]
which is a contradiction. Therefore, we must have i=i1.
∎
Before proving Theorem 1.3, we define Lj=Z(δM−jD) in the same manner as in Lemma 4.4. Thus, Z(μM,D)=∪j=1∞Lj.
The sufficiency follows directly from Lemma 4.3, and we now need to consider the necessity. If μM,D is a spectral measure, then by Proposition 4.2, ρ−1=t∈pℓ1(Z∖pZ), where ℓ1≥1. If the necessity does not hold, then either a′∈/Q or a′=uv, where u∈pℓ1Z and gcd(v,u)=1. Let Λ be the spectrum of μM,D. From Lemma 2.2, it follows that the intersection of (Λ−Λ) and L2 is non-empty, i.e., (Λ−Λ)∩L2=∅. Without loss of generality, we assume that 0∈Λ and Λ∩L2=∅. Lemma 4.4 then imply that Λ∩L1=∅. Furthermore, since (Λ−Λ)∖{0}⊂∪j=1∞Lj, it follows that there exist λ1∈L1∩Λ and λ2∈L2∩Λ such that λ2−λ1∈L1. Denote
[TABLE]
for some ki=(ki,1,ki,2)∗,si=(si,1,si,2)∗∈Z2, i=1,2. Then,
[TABLE]
By examining the second component of the above expression and the structure of L1, we conclude that
[TABLE]
for some s3,2,k3,2∈Z. However, since s3,2+s1,2≡0(modp), it follows that tuv(s2,1+pk2,1)∈pZ. Thus, pℓ1+1∣t, leading to a contradiction. Therefore, the proof of necessity is complete.
∎
5. The Case ρ1=ρ2
In this section, we focus on proving Theorems 1.5 and 1.6, which investigate the spectral properties of the measure μM,D defined by conditions (1.1), (1.2), (1.3) and (1.4) when ρ1=ρ2. As established by Theorem 1.4, the spectrality of μM,D necessarily implies that c′′ must be a rational number, i.e., c′′∈Q. Recall that
[TABLE]
and
[TABLE]
Let R′=[10c′′1], D=R′D. Then
[TABLE]
Consequently, μM,D and μM,D share the same spectral properties. Define
The necessity is directly established by Lemma 3.7. We now prove the sufficiency.
Let ρ1−1=t∈pZ, c′′=c2c1∈Q\Ea with gcd(c1,c2)=1. Define
[TABLE]
From Lemma 2.9, we know that B={0,1,⋯,p−1}(modp). Let
[TABLE]
Then the triplet (t,B,L) forms a Hadamard triple. By [13, Theorem 5.4], we know the associated self-similar measure μt,B is a spectral with a spectrum in Z. Denote Ln=L+tL+t2L+⋯+tn−1L and
[TABLE]
where 0∈Jn≡Ln(modtn) and ℓk=n1+n2+⋯+nk.
By [13, Proposition 4.6], we obtain a spectrum Λ of μt,B and
[TABLE]
This shows that
[TABLE]
Define Λk′={(c2λ,0)∗:λ∈Λk} and Λ′=∪k=1∞Λk′. For any distinct λ1′,λ2′∈Λk′, since λ1−λ2∈p1tℓ(Z∖pZ) with ℓ≤ℓk, it can be concluded that
[TABLE]
Note that
[TABLE]
for any z=(z1,z2)∗⊂Z2. Given that gcd(c2,p)=1 and c′′∈Q\Ea, it follows that
λ1′−λ2′⊂Z(μℓk).
Therefore, it is not hard to conclude that Λk′ is a spectrum of μℓk. Moreover, since for any λ′=(c2λ,0)∗∈Λk′, we have
[TABLE]
the equicontinuity of the sequence {μ>ℓk}k=1∞ implies that
[TABLE]
Thus, Λ′ is the spectrum of μM,D, as established in Lemma 2.6 (ii).
∎
Next, we consider the case c′′∈Ea.
Proposition 5.1**.**
If μM,D is a spectral measure and c′′∈Ea, then ρ1−1,ρ2−1∈pZ.
Proof.
ρ1−1∈pZ follows from Lemma 3.7. Let c′′=c2c1∈Ea with gcd(c1,c2)=1. Since
[TABLE]
similar to the proof of Proposition 3.4, we can conclude that ρ2−1=st for some t∈pZ with gcd(s,t)=1. If s>1, let ξ=(ξ1,ξ2)∗∈R2 and
[TABLE]
Clearly, f(ξ1+c2,ξ2+1)=f(ξ1,ξ2) and
[TABLE]
From Lemma 2.7, for any n≥1 and ξ=(ξ1,ξ2)∗ with ∣ρ2−nξ2∣>1, there exist positive constants α,β such that
[TABLE]
Thus, by Proposition 3.6, μM,D cannot be a spectral measure, leading to a contradiction. Hence, s=1, which implies ρ2−1∈pZ.
∎
Proposition 5.2**.**
Suppose that ρ1−1,ρ2−1∈pZ and c′′=c2c1∈Ea, where gcd(c1,c2)=1 and c2∈pℓ(Z∖pZ) for some ℓ∈N. If pℓ+1∤(ρ1−1−ρ2−1), then μM,D is not a spectral measure.
Proof.
Assuming that μM,D is spectral measure with a spectrum 0∈Λ. From ρ1−1,ρ2−1∈pZ and Lemma 3.8, it follows that Λ∩Zj=∅ for j=1,2.
Denote
[TABLE]
for some k=(kj,1,kj,2)∗∈Z2, ij∈{1,2,⋯,p−1}, j=1,2. Then, λ2−λ1∈Z1 follows as
[TABLE]
Furthermore, we get
[TABLE]
From the expression i3a2+pk3,2+i1a2+pk1,2∈pZ, it follows that (ρ1−1−ρ2−1)c′′(i2a1+pk2,1)∈pZ. This implies that pℓ+1∣(ρ1−1−ρ2−1), which leads to a contradiction. Therefore, the proof is complete.
∎
Combining with Proposition 5.1 and Proposition 5.2, we can derive the necessary part of Theorem 1.6. For the sufficiency, it requires several additional technical lemmas.
For convenience,
we define
[TABLE]
and the integral periodic zero set of P′ as
[TABLE]
Lemma 5.3**.**
Suppose that
ρ1−1=t1,ρ2−1=t2∈pZ
and
c′′=c2c1,where gcd(c1,c2)=1 and c2=puc2′∈pu(Z∖pZ) for some u∈N. If
[TABLE]
then μM,D is a spectral measure.
Proof.
According to Lemma 2.3, μM,D is uniformly continuous. This implies that there exists δ>0 such that for any ξ∈G, there exists kξ∈Z2 satisfying
[TABLE]
whenever ∥y∥≤δ.
Construct the sequence {Λnℓ} with n0=0 and nℓ+1>nℓ such that
[TABLE]
For any wℓ∈{0,1,2,⋯,p−1}nℓ+1−nℓ:=Ξnℓ+1−nℓ, let
Furthermore, we will prove that Λ=∪ℓ=1∞Λnℓ is the spectrum of μM,D. For any λ1=λ2∈Λnℓ, there exist two distinct sequences {w1(i)}i=1nℓ,{w2(i)}i=1nℓ⊂{0,1,2,⋯,p−1}nℓ, such that
[TABLE]
where kxwi(j)=(kji,1,kji,2)∗ and l=min{j:w1(j)=w2(j)}∈(ns,ns+1] for some 0≤s≤ℓ−1. The expression for λ1−λ2 can be rewritten as follows:
[TABLE]
It follows from pu+1∣(t1−t2) that
[TABLE]
Therefore, we conclude that μnℓ(λ1−λ2)=0. Since #sptμnℓ=pnℓ, it immediately follows that Λnℓ is a spectrum of μnℓ for all ℓ≥1. By Lemma 2.3, the sequence {μ>nℓ}ℓ=1∞ is equicontinuous.
Therefore, for λ∈Λnℓ+1∖Λnℓ,ξ∈[0,r]2 with some r>0, using (5.1), we obtain that
[TABLE]
Moreover, by Lemma 2.6, Λ is a spectrum of μM,D.
∎
Lemma 5.4**.**
Suppose that
ρ1−1=t1,ρ2−1=t2∈pZ
and
c′′=c2c1,where gcd(c1,c2)=1 and c2=puc2′∈pu(Z∖pZ) for some u∈N. If Z(μM,D)=∅, then
[TABLE]
Proof.
For every ξ∈G, the condition Z(μM,D)=∅ implies the existence of an integer vector
kξ∈Z2
such that
[TABLE]
By the continuity of μM,D, we obtain
[TABLE]
Define
B(ξ,δξ):={x∈R2:dist(x,ξ)<δξ}.
Since G is a compact set and G⊂∪ξ∈GB(ξ,δξ), by the finite covering theorem, there exists a finite collection of balls
{B(ξi,δξi)}i=1ℓ
such that
∪i=1ℓB(ξi,δξi)⊃G.
For any ξ∈G, there exists a j with 1≤j≤ℓ such that ξ∈B(ξj,δξj). Thus,
[TABLE]
∎
According to Lemma 5.3 and Lemma 5.4, for the sufficiency of Theorem 1.6, we only need to prove that Z(μM,D)=∅. However, before proceeding, we require a key lemma.
Lemma 5.5**.**
Suppose that
ρ1−1=t1,ρ2−1=t2∈pZ
and
c′′=c2c1,where gcd(c1,c2)=1 and c2=puc2′∈pu(Z∖pZ) for some u∈N. For any k=(k1,k2)∗∈Z2, if μM,D(ξ+P′k)=0, then there exists jk such that mD(M−jk(ξ+P′k))=0.
Proof.
From the inequalities ∣z∣≥∣Re(z)∣ and cos(2πx)≥cos(2πr) for all ∣x∣≤r<41, it follows that
[TABLE]
We can choose an appropriate J such that for all i∈{0,1,⋯,p−1}, the following inequality holds:
[TABLE]
Hence,
[TABLE]
Furthermore, we know that
[TABLE]
Together with the assumption that
μM,D(ξ+P′k)=0,
this implies that
μJ(ξ+P′k)=0.
Consequently, we have mD(M−jk(ξ+P′k))=0 for some jk≤J.
∎
We now proceed to demonstrate that Z(μM,D)=∅.
Lemma 5.6**.**
Suppose that
ρ1−1=t1,ρ2−1=t2∈pZ
and
c′′=c2c1,where gcd(c1,c2)=1 and c2=puc2′∈pu(Z∖pZ) for some u∈N, then Z(μM,D)=∅.
Proof.
If not, we let ξ=(ξ1,ξ2)∗∈Z(μM,D). For any k=(n,ℓ)∗∈Z2, the definition of Z(μM,D) ensures that ξ+P′k∈Z(μM,D). By Lemma 5.5, there exists jk such that ξ+P′k∈Z(mM−jkD), i.e.,
[TABLE]
for some k1,k2∈Z, ik∈{1,2,⋯,p−1}. For the convenience of subsequent discussion, we define
jk=jn,ℓn,ki=ki(n,ℓn) and ik=in,ℓn.
Take n=0 and ℓ0∈Z. By equation (5.4), there exist two positive integers j0,ℓ0 and k1(0,ℓ0) such that
We claim that for any (n,ℓn)∗∈{0,1,⋯,p−1}×Z, there are exactly p−1 elements in {jn,ℓn}n=0p−1 that are equal to 1, and the remaining one is greater than 1.
Let n∈{1,2,⋯,p−1}. If jn,ℓn≤j0,ℓ0, then jn,ℓn=1 and t1∈p(Z∖pZ). Otherwise, using equation (5.5), we obtain
[TABLE]
which leads to a contradiction since c2′n∈Z∖pZ. If jn,ℓn>j0,ℓ0, then jn,ℓn≥2 and we have
[TABLE]
This implies j0,ℓ0=1 and t1∈p(Z∖pZ). Using (5.4) again, for any distinct n1,n2∈{1,2,⋯,p−1}, we obtain
[TABLE]
Together with c2′∈Z∖pZ, this leads to the conclusion that min{jn1,ℓn1,jn2,ℓn2}=1.
The arbitrariness of n1 and n2 tells us that at least p−1 elements in the set {jn,ℓn}n=0p−1 must be equal to 1. If they are all equal to 1, let hc2′=−t1pi0,ℓ0a1(modp) for some h∈{1,2,⋯,p−1}. Then,
[TABLE]
which leads to a contradiction, and the claim follows.
Building on the previous claim, we assume that j0,ℓ0>1 and j1,ℓ1=j2,ℓ2=⋯=jp−1,ℓp−1=1 for all vectors (ℓ0,ℓ1,⋯,ℓp−1)∗∈Zp. Let n=1 and ℓ1∈{0,1,⋯,p−1}. It follows from (5.4) that
[TABLE]
Through simple calculations, we get
[TABLE]
Note that ℓ1∈{0,1,⋯,p−1} and i1,ℓ1∈{1,⋯,p−1}. Then, we can select two distinct integers ℓ1′,ℓ1′′∈{0,1,⋯,p−1} such that i1,ℓ1′=i1,ℓ1′′. From (5.6),
[TABLE]
which leads to a contradiction, thus completing the proof.
∎
6. Examples
In the final section, we present some examples to emphasize our key findings.
Firstly, we give a counterexample to refute [3, Conjecture 4.2].
Example 6.1**.**
Define
[TABLE]
Then
[TABLE]
It is known that μM,D and μM1,D1 have the same spectrality. Let
[TABLE]
We can check (M1,D1,L1) forms a Hadamard triple. Thus μM,D and μM1,D1 are both spectral measure. The spectral measure μM,D can be verified to satisfy all conditions of Theorem 1.3, while c=43=81×6=κβ and pκ=2×81∈/Z. This implies that [3, Conjecture 4.2] is false. As shown in Figs. 4 to 4, these figures also illustrate the first four approximations of the Sierpinski fractal of μM,D.
Example 6.2**.**
Let
[TABLE]
Then
[TABLE]
According to Theorem 1.7, the measure μM,D is a spectral measure if and only if the matrix M satisfies one of the following conditions:
(i)
ρ1=ρ2, ρ1−1∈5Z, and c′′∈Q∖Ea, where Ea=E(1,1)∗={ts:t−s∈Z\5Z,gcd(s,t)=1}∪{0}.
2. (ii)
ρ1=ρ2, ρ1−1,ρ2−1∈5Z, and c′′=c2c1∈Ea, c2∈5ℓ(Z∖5Z), 5ℓ+1∣(ρ1−1−ρ2−1) for some ℓ∈N.
3. (iii)
ρ1=ρ2∈5Z* and c∈{ts:s∈5Z,gcd(s,t)=1}∪{0}.*
We present the following example to illustrate Theorems 1.1 and 1.2.
Example 6.3**.**
Let
[TABLE]
and
[TABLE]
Then by Theorems 1.1 and 1.2, L2(μM1,D) admits an infinite set of orthogonal exponential functions, while L2(μM2,D) contains an arbitrary number of orthogonal exponential functions. Moreover, there can be at most 5 mutually orthogonal exponential functions in both L2(μM3,D) and L2(μM4,D), with 5 being the maximum possible. Figs. 8 to 8 show the second iterative fractal graphs of the measure μM1,D, μM2,D, μM3,D, and μM4,D, respectively.
Finally, we propose two natural questions.
Question 6.4**.**
In Theorem 1.7, if we replace d1=(d1,1,0)∗ with d1=(d1,1,d1,2)∗,d1,2=0 in the condition (1.4), does the conclusion still hold?
Question 6.5**.**
How to extend our results to higher dimensions or Moran measure?
Acknowledgment
Competing interests. The authors declare that they have no competing interests.
Data availability.
Data availability is not applicable to this article as no new data were created or analyzed in this study.
Bibliography23
The reference list from the paper itself. Each links out to its DOI / PubMed record.
1[1] L.X. An, X.Y. Fu, C.K. Lai, On spectral Cantor-Moran measures and a variant of Bourgain’s sum of sine problem. Adv. Math. 349 (2019), 84-124.
2[2] L.X. An, L. He, X.G. He, Spectrality and non-spectrality of the Riesz product measures with three elements in digit sets. J. Funct. Anal. 277 (2019), 255-278.
3[3] M.L. Chen, X.Y. Wang, J. Zheng, On the orthogonal exponential functions of a class of planar self-affine measures. J. Math. Anal. Appl. 485 (2020), 11 pp.
4[4] M.L. Chen, J.C. Liu, Y.H. Yao, Fourier bases of the planar self-affine measures with three digits. Math. Nachr. 296 (2023), 4995-5011.
5[5] J.L. Chen, X.Y. Yan, P.F. Zhang, The cardinality of orthogonal exponentials for a Class of self-affine Measures on ℝ n \mathbb{R}^{n} . Acta Math. Hungar. 175 (2025), 219-235.
6[6] J.L. Chen, On the Fourier orthonormal bases of a class of Sierpinski-type Moran measures on ℝ n \mathbb{R}^{n} . preprint. https://doi.org/10.48550/ar Xiv.2505.09360.
7[7] Z.C. Chi, J.F. Lu, M.M. Zhang, A class of spectral Moran measures generated by the compatible tower. J. Geom. Anal. 34 (2024), 30 pp.
8[8] Q.R. Deng, Spectrality of one dimensional self-similar measures with consecutive digits. J. Math. Anal. Appl. 409 (2014), 331-346.