Sum-product phenomenon – Relationship of relationship

This is a note concentrate on the log average Sarnak conjecture, after the work of Matomaki and Raziwill on the estimate of multiplication function of short interval. Given a overview of the presented tools and method dealing with this conjectue.

1. Introduction

Sarnak conjecture \cite{Sarnak} assert that for any obersevable ${\{f(T^n(x_0))\}_{n=1}^{\infty}}$ come from a determination systems ${(T,X),T:X\rightarrow X}$ , where ${h(T)=0}$ , ${x_0\in X, f\in C(X)}$ . The correlation of it and the Liuvillou function is 0, i.e. they are orthongonal to each other, more preseicesly it is just to say,

$\displaystyle \sum_{n<x}\mu(x)f(T^n(x_0))=o(x) \ \ \ \ \ (1)$

This is a very natural raised conjecture, Liuville function is the presentation of primes, due to we always believe the distribution of primes in ${\mathbb N}$ should be randomness.

It has been known as observed by Landau \cite{Laudau} that the simplest case,

$\displaystyle \sum_{n<x}\mu(n)=o(x)$

already equivalent to the prime number theorem. It is not difficult to deduce the spetial case of Sarnak conjecture when with the obersevation in $latex {(1)}&fg=000000$ come from finite dynamic system is equivalent to the prime number theorem in athremetic progress by the similar argument. Besides this two classical result, may be the first new result was established by Davenport,

Theorem 1 Let ${T:S_1\rightarrow S_1, T(x)=x+\alpha}$ , ${\alpha}$ is a inrational, then the obersevation come from ${(T,S_1)}$ is orthogonal to Mobius function. due to ${\{e^{2\pi ikx}\}_{k\in \mathbb Z}}$ is a basis of ${C(S_1)}$ , suffice to proof,

$\displaystyle \sum_{n<x}e^{2\pi ikn\alpha}\mu(n)=o(x), \forall k\in \mathbb N$

There is a lots of spetial situations of Sarnak’s conjecture have been established, The parts I mainly cared is the following:

Interval exchange map.
Skew product flow.
Obersevable come from One dimensional zero entropy flow.
Nilsequences.

But in this note, I do not want to explain the tecnical and tools to establish this result, but considering an equivalent conjecture of Sarnak conjecture, named Chowla conjecture, and explain the underlying insight of the suitable weak statement, i.e. the log average Chowla conjecture and the underlying insight of it.

The note is organized as following way, in the next section $latex {(2)}&fg=000000$, we give a self-contained introduction on the tools called Bourgain-Sarnak-Ziegler critation, explain the relationship of this critation and the sum-product phenomenon, also given some more general critation along the philosephy use in establish the Bourgain-Sarnak-Ziegler critation, which maybe useful in following development combine with some other tools. The key point is transform the sum from linear sum to bilinear sum and decomposition the bilinear sum into diagonal part and off-diagonal part, use the assume in the critation to argue the off-diagonal part is small and on the orther hand the diagonal part is also small by the trivial estimate and the volume of diogonal is small, this is very similar to a suitable Caderon-Zugmund decomposition.

In section $latex {(4)}&fg=000000$, I try to give a proof sketch of the result of Matomaki and Raziwill, which is also a key tools to understanding the Sarnak conjecture, or equivalent the Chowla conjecture. The key points of the proof contains following:

Find a suitable fourier indentity
Construct a multiplication-addition dense subset ${S}$ , and proof that the theorem MR hold we need only to proof it hold for ${S\cap [1,2,...,n]}$ instead of ${[1,2,...,n]}$
Involve the power of euler product formula. divide the whole interval into a lot of small interval with smaller and smaller scale and a residue part. We look the part come from every small scale as a major term and look the residue part as minor term.
Deal with the major term at every scale, by a combitorios identity and second moments method.
find a enough decay estimate from a scale to the next smaller scale.
Deal with the minor term by the H… lemma.

Due to the theorem of MR do not exausted the method they developed, we trying to make some more result with their method, Tao and Matomaki attain the average version of Chowla conjecture is true by this way, and combine this argument and the entropy decresment argument they established the 2 partten of the log average Chowla conjecture is true. Very recently Tao and his coperator proved the odd partten case of log average chowla conjecture is true, combine an argument of frustenberg crresponding principle and entopy decresment argument. But it seems the even and large than 2 case is much difficult and seems need something new to combine with the method of MR and entropy decresment and frunstenberg corresponfing principle to make some progress.

So, in section $latex {(5)}&fg=000000$, we give a self-contain introduction to the entropy decresment argument of Tao, and combine with the frustenberg corresponding principle.

In the last section $latex {(6)}&fg=000000$, I state some result and method and phylosphy of them I get on nilsequences and wish to combine them with the previous method to make some progress on log average Chowla conjecture on the even partten case.

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2. Bourgain-Sarnak-Zieglar creation

We begin with the easiest one, this is the main result established in \cite{BSZ}, I try to give the main ideal under the proof, but with a no quantitative version is the following,

Theorem 2 (Bourgain-Sarnak-Zieglar creation, not quantitative version) if for all primes ${p,q>>1}$ we have:

$\displaystyle \sum_{n=1}^Nf(T^{pn}(x))\overline{f(T^{qn}(x))}=o(N) \ \ \ \ \ (2)$

Then for multiplication function ${g(n)}$ we have

$\displaystyle \sum_{n=1}^Ng(n)\overline{ f(T^n(x))}=o(N) \ \ \ \ \ (3)$

Remark 1 For simplify we identify ${f(T^n(x)):=F(n)}$ .

Remark 2

The idea is following, break the sum into a bilinear one, so, of course, we multiplication it with itself. i.e. we consider to control,

$\displaystyle |\sum_{i=1}^Ng(n)\overline{ F(n)}|^2=\sum_{n=1}^N\sum_{m=1}^Ng(n)g(m)\overline{F(n)F(m)} \ \ \ \ \ (4)$

To control 4, we need exhausted the mutiplication property of ${g(n)}$ , we have ${g(mn)=g(n)g(m),\forall\ m,n\in {\mathbb N}}$ . We can not get good estimate for all term,

$\displaystyle g(n)g(m)\overline{F(n)F(m)} \ \ \ \ \ (5)$

The condition in our hand if following,

$\displaystyle \sum_{n=1}^NF(pn)\overline{F(qn)}=o(N), \forall \ p,q\in \mathop{\mathbb P} \ \ \ \ \ (6)$

So, just like the situation of Cotlar-Stein lemma \cite{Cotlar-Stein lemma}, we wish to estimate like following:

$\displaystyle \begin{array}{rcl} |\sum_{p\in W}\sum_{n\in V}F(pn)g(pn)| & \leq & \sum_{n\in V}|g(n)|\cdot |\sum_{p\in W}F(pn)g(p)| \\ & \leq &\sum_{n\in V}|\sum_{p \in W}F(pn)g(p)|\\ & \overset{Cauchy-Schwarz}\leq & |V|^{\frac{1}{2}}[\sum_{n\in V}|\sum_{p\in W}F(pn)g(p)|^2]^{\frac{1}{2}}\\ & = & |V|^{\frac{1}{2}}[\sum_{p_1,p_2\in W}\sum_{n\in V}F(p_1n)\overline{F(p_2n)}g(p_1)\overline{g(p_2)}]^{\frac{1}{2}}\\ \end{array}$

Then we consider divide the sum into diagonal part and non-diagonal part, as following,

$\displaystyle |V|^{\frac{1}{2}}[\sum_{p_1\neq p_2\in W}\sum_{n\in V}F(p_1n)\overline{F(p_2n)}g(p_1)\overline{g(p_2)}]^{\frac{1}{2}}+|V|^{\frac{1}{2}}[\sum_{p\in W}\sum_{n\in V}|F(pn)|^2]^{\frac{1}{2}} \ \ \ \ \ (7)$

But the first part is small, i.e.

$\displaystyle |V|^{\frac{1}{2}}[\sum_{p_1\neq p_2\in W}\sum_{n\in V}F(p_1n)\overline{F(p_2n)}g(p_1)\overline{g(p_2)}]^{\frac{1}{2}} =o(|W||V|) \ \ \ \ \ (8)$

Because of

$\displaystyle \sum_{n\in V}F(p_1n)\overline{F(p_2n)}=o(V), \forall p_1\neq p_2\in W \ \ \ \ \ (9)$

and the second part is small, i.e.

$\displaystyle |V|^{\frac{1}{2}}[\sum_{p\in W}\sum_{n\in V}|F(pn)|^2]^{\frac{1}{2}}=o(|W||V|) \ \ \ \ \ (10)$

Because diagonal part is small in ${W\times W}$ and trivial inequality

$\displaystyle \sqrt{\sum_{n\in V}|F(pn)|^2}\leq |V|^{\frac{1}{2}} \ \ \ \ \ (11)$

But the method in remark 2 is not always make sense in any situation, we need to construct two suitable sets ${W,V}$ and then break up ${\{1,2,...,n{\mathbb N}\}}$ into ${W\times V}$ , this mean,

$\displaystyle \{1,2,...,N\}\sim W\times V+o(N) \ \ \ \ \ (12)$

But this ${W,V}$ could be construct in this situation, thanks to the prime number theorem,

Theorem 3 (Prime number theorem)

$\displaystyle \pi(n)\sim \frac{n}{ln(n)} \ \ \ \ \ (13)$

Morally speaking, this is the statement that the primes, which is the generator of multiplication function, is not very sparse.

3. Van der curpurt trick

There is the statement of Van der carport theorem:

Theorem 4 (Van der curpurt trick) Given a sequences ${ \{x_n\}_{n=1}^{\infty}}$ in ${ S_1}$ , if ${ \forall k\in N^*}$ , ${ \{x_{n+k}-x_n\}}$ is uniformly distributed, then ${ \{x_n\}_{n=1}^{\infty}}$ is uniformly distributed.

I do not know how to establish this theorem with no extra condition, but this result is true at least for polynomial flow. \newpage Proof:

$\displaystyle \begin{array}{rcl} |\sum_{n=1}^Ne^{2\pi imQ(n)}|& = &\sqrt{(\sum_{n=1}^Ne^{2\pi imQ(n)})(\overline{\sum_{n=1}^Ne^{2\pi imQ(n)}})}\\ & = &\sqrt{\sum_{h_1=1}^N\sum_{n=1}^{N-h_1}e^{2\pi imQ(n+h_1)-Q(n)}}\\ & = &\sqrt{\sum_{h_1=1}^N\sum_{n=1}^{N-h_1}e^{2\pi im \partial^1_{h_1}Q(n)}}\\ & \leq & \sqrt{\sum_{h_1=1}^N|\sum_{n=1}^{N-h_1}e^{2\pi \partial^1_{h_1}Q(n)}|}\\ & = &\sqrt{\sum_{h_1=1}^N\sqrt{ (\sum_{n=1}^{N-h_1}e^{2\pi \partial^1_{h_1}Q(n)} )(\overline{\sum_{n=1}^{N-h}e^{2\pi \partial^1_{h_1}Q(n)})}}}\leq\sqrt{\sum_{h_1=1}^N\sqrt{ \sum_{h_2=1}^N|\sum_{n=1}^{N-h_1}e^{2\pi\partial^1_{h_2} \partial^1_hQ(n)} |}}\\ & \leq ....\leq & \\ & = & \sqrt{\sum_{h_1=1}^N\sqrt{ \sum_{h_2=1}^N \sqrt{....\sqrt{\sum_{h_{k-1}=1}^{N-h_{k-2}}|\sum_{n=1}^{N-h_{k-1}}e^{2\pi\partial_{h_1h_2...h_{k-1}Q(n)}}|}}}} =o(1) \end{array}$

$\Box$

This type of trick could also establish the following result, which could be understand as a discretization of the Vinegradov lemma.

Remark 3

Uniformly distribution result of ${ F_p}$ : Given ${Q(n)=a_kn^k+...+a_1n+a_0}$ , ${\{Q(0),Q(1),...,Q(p-1)\}}$ coverages to a uniformly distribution in ${\{0,1,...,p-1\}}$ as ${p \rightarrow \infty}$ .

Remark 4 But I definitely do not know how to establish the similar result when ${Q(n)=n^{-1}}$ .

Remark 5

This trick could also help to establish estimate of correlation of low complexity sequences and multiplicative function, such as result:

$\displaystyle S(x)=\sum_{n\le x}\left(\frac{n}{p}\right)\mu(n)=o(n)$

Maybe with the help of B-Z-S theorem.

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4. Matomaki and Raziwill’s work

In this section we explain the main idea underlying the paper \cite{KAISA MATOMA 虉KI AND MAKSYM RADZIWILL}. But play with a toy model, i.e. the corresponding corollary of the original result on Liouville鈥檚 function.

Definition 5 (Lioville’s function)

$\displaystyle \lambda(n)=(-1)^{\alpha_1+\alpha_2+...+\alpha_k}, \forall \ n=p_1^{\alpha_1}...p_k^{\alpha_k}. \ \ \ \ \ (14)$

Remark 6

$\displaystyle |\int_{X}^{2X}\lambda(n)dx|=o(x) \ \ \ \ \ (15)$

is equivalent to the prime number theorem 3.

The most important beakgrouth of analytic number theory is the new understanding of multiplication function on share interval, this result is established by Kaisa Matom盲ki and Maksym Radziwill. Two very young and intelligent superstars.

The main theorem in them article is :

Theorem 6 (Matomaki,Radziwill) As soon as ${H\rightarrow \infty}$ when ${x\rightarrow \infty}$ , one has:

$\displaystyle \sum_{x\leq n\leq x+H}\lambda(n)= o(H) \ \ \ \ \ (16)$

for almost all ${1\leq x\leq X}$ .

In my understanding of the result, the main strategy is:

Parseval indetity, transform to Dirchelet polynomial.
Involved by multiplication property, spectral decomposition.
From linear to multilinear , Cauchy schwarz inequality.
major term estimate.
Estimate the contribution of area which is not filled.

4.1. Parseval indetity, transform to Dirchelet polynomial

We wish to establish the equality,

$\displaystyle \frac{1}{X}\int_{X}^{2X}|\sum_{x\leq n\leq x+H}\lambda(n)|dx=o(H) \ \ \ \ \ (17)$

This is the ${L^1}$ norm, by Chebyschev inequality, this could be control by ${L^2}$ norm, so we only need to establish the following,

$\displaystyle \frac{1}{X}\int_X^{2 X}|\sum_{x\leq n\leq x+H}\lambda(n)|^2dx=o(H^2) \ \ \ \ \ (18)$

We wish to transform from the discretization sum to a continue sum, that is,

$\displaystyle \int_{{\mathbb R}}|\sum_{xe^{-\frac{1}{T}}\leq n\leq xe^{\frac{1}{T}}}\lambda(n)1_{X\leq n\leq 2X}|^2\frac{dx}{x} \ \ \ \ \ (19)$

Remark 7 There are two points to understand why 19 and 18 are the same.

${[xe^{-\frac{1}{T}},xe^{\frac{1}{T}}]\sim [x-H,x+H]}$ .

${1_{x\leq n\leq 2x}}$ and ${\frac{1}{x}}$ is to make that ${x=O(X)}$ .

So the Magnitude of 18 and 19 are the same. i.e.

$\displaystyle \int_{{\mathbb R}}|\sum_{xe^{-\frac{1}{T}}\leq n\leq xe^{\frac{1}{T}}}\lambda(n)1_{X\leq n\leq 2X}|^2\frac{dx}{x}\sim \frac{1}{X}\int_X^{2 X}|\sum_{x\leq n\leq x+H}\lambda(n)|^2dx \ \ \ \ \ (20)$

Now we try to transform 19 by Parseval indetity, this is something about the ${L^2}$ norms of the quality we wish to charge. It is just trying to understanding 19 as a quantity in physical space by a more chargeable quality in frequency space. Image,

$\displaystyle \int_{{\mathbb R}}|\sum_{xe^{-\frac{1}{T}}\leq n\leq xe^{\frac{1}{T}}}\lambda(n)1_{X\leq n\leq 2X}|^2\frac{dx}{x}:=\int_{{\mathbb R}}|f_X(x)|^2dx \ \ \ \ \ (21)$

Then ${f_X(x)=\int_{xe^{-\frac{1}{T}}\leq n\leq xe^{\frac{1}{T}}}\lambda(x)1_{X\leq n\leq 2X}}$ . Note that,

$\displaystyle \begin{array}{rcl} \widehat{f_X(\xi)} & = & \int_{{\mathbb R}}f_X(x)e^{2\pi ix\xi}dx\\ & = & \sum_{x\leq n\leq 2x}\lambda(x)\int_{logn-\frac{1}{T}}^{logn+\frac{1}{T}}e^{2\pi ix\xi}dx, \ T=\frac{X}{H}\\ & = & \sum_{X\leq n\leq 2X}\lambda(x)e^{2\pi ilog(n)\cdot \xi}\cdot\frac{e^{2\pi i\frac{\xi}{T}}-e^{2\pi i-\frac{\xi}{T}}}{2\pi i\xi}\\ \end{array}$

So by Parseval identity, we have,

$\displaystyle \begin{array}{rcl} \int_{{\mathbb R}}|f_X(x)|^2dx & = & \int_{{\mathbb R}}|\widehat{f_X(\xi)}|^2d\xi \\ & = & \int_{{\mathbb R}}|\sum_{X\leq n\leq 2X}\lambda(n)\cdot n^{2\pi i\xi}|^2(\frac{e^{2\pi i\frac{\xi}{T}}-e^{2\pi i\frac{-\xi}{T}}}{2\pi i\xi})^2d\xi\\ & \sim & \int_{{\mathbb R}}|\sum_{X\leq n\leq 2X}\lambda(n)\cdot n^{2\pi i\xi}|^2\frac{1}{T^2}1_{|\xi|^2\leq T}\\ \end{array}$

Remark 8 We know the Fejer kernel satisfied,

$\displaystyle (\frac{e^{2\pi i\frac{\xi}{T}}-e^{2\pi i\frac{-\xi}{T}}}{2\pi i\xi})^2\sim \frac{1}{T^2}1_{|\xi|\leq T} \ \ \ \ \ (22)$

So morally speaking, we get the following identity.

$\displaystyle \frac{1}{X}\int_{X}^{2X}|\sum_{x\leq n\leq x+H}\lambda(n)|^2dx\sim \frac{1}{(x/H)^2}\int_{0}^{\frac{X}{H}}|\sum_{x\leq n\leq 2x}\lambda(x)x^{2\pi i\xi}|^2d\xi \ \ \ \ \ (23)$

In fact we do a cutoff, the quality we really consider is just:

$\displaystyle \frac{1}{X^2}\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt \ \ \ \ \ (24)$

established the monotonically inequality:

Theorem 7 (Paserval type identity)

$\displaystyle \frac{1}{X}\int_{X}^{2X}|\frac{1}{H}\sum_{x\leq n\leq x+H}\lambda(n)|^2dx \sim聽\frac{1}{X^2}\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt \ \ \ \ \ (25)$

Remark 9

In my understanding, This is a perspective of the quality, due to the quality is a multiplicative function integral on a domain ${ \mathbb N^*}$ with additive structure, it could be looked as a lots of wave with the periodic given by primes, so we could do a orthogonal decomposition in the fractional space, try to prove the cutoff is a error term and we get such a monotonically inequality.

But at once we get the monotonically inequality, we could look it as a聽compactification process and this process still carry most of the information so lead to the inequality.

It seems something similar occur in the attack of the moments estimate of zeta function by the second author. And it is also could be looked as something similar to the 聽spectral decomposition with some basis come from multiplication generators, i.e. primes.

4.2. Involved by multiplication property, spectral decomposition

I called it is “spectral decomposition”, but this is not very exact. Anyway, the thing I want to say is that for multiplication function ${\lambda(n)}$ , we have Euler-product formula:

$\displaystyle \Pi_{p,prime}(\frac{1}{1-\frac{\lambda(p)}{p^s}})=\sum_{n=1}^{\infty} \frac{\lambda(n)}{n^s} \ \ \ \ \ (26)$

But anyway, we do not use the whole power of multiplication just use it on primes, i.e. ${\lambda(pn)=\lambda(p)\lambda(n)}$ leads to following result:

$\displaystyle \lambda(n)=\sum_{n=pm,p\in I}\frac{\lambda(p)\lambda(m)}{\# \{p|m, p\in I\}+1}+\lambda(n)1_{p|n;p\notin I} \ \ \ \ \ (27)$

This is a identity about the function ${\lambda(n)}$ , the point is it is not just use the multiplication at a point,i.e. ${\lambda(mn)=\lambda(m)\lambda(n)}$ , but take average at a area which is natural generated and compatible with multiplication, this identity carry a lot of information of the multiplicative property. Which is crucial to get a good estimate for the quality we consider about.

4.3. From linear to multilinear , Cauchy schwarz

Now, we do not use one sets ${I}$ , but use several sets ${I_1,...,I_n }$ which is carefully chosen. And we do not consider [X,2X] with linear structure anymore , instead reconsider the decomposition:

${[X,2X]=\amalg_{i=1}^n (I_i\times J_i) \amalg U}$

On every ${I_i\times J_i}$ it equipped with a bilinear structure. And ${U}$ is a very small set, ${|U|=o(X)}$ which is in fact have much better estimate.

${\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt =\sum_{i=1}^n\int_{I_i\times J_i}聽聽\frac{1}{X^2}\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt +\int_N |\sum_{n\leq X}\lambda(n)n^{it}|^2dt}$

Now we just use a Cauchy-Schwarz:

${\sum_{i=1}^n\int_{I_i\times J_i}聽聽\frac{1}{X^2}\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt +\int_N |\sum_{n\leq X}\lambda(n)n^{it}|^2dt}$

4.4. major term estimate

${=\sum_{i=1}^n\int_{I_i\times J_i}聽聽\frac{1}{X^2}\int_{|log(X)|^{100}}^{\frac{X}{H}}|\sum_{n\leq X}\lambda(n)n^{it}|^2dt}$

${\int_N |\sum_{n\leq X}\lambda(n)n^{it}|^2dt}$

4.5. estimate the contribution of area which is not filled

\newpage

5. Entropy dcrement argument

\newpage

6. Correlation with nilsequences

I wish to establish the following estimate: ${\lambda(n)}$ is the liouville function we wish the following estimate is true.

$\displaystyle \int_{0\leq x\leq X}|\sup_{f\in \Omega^m}\sum_{x\leq n\leq x+H}\lambda(n)e^{2\pi if(x)}|dx =o(XH). \ \ \ \ \ (28)$

Where we have ${ H\rightarrow \infty}$ as ${ x\rightarrow \infty}$ ,

$\displaystyle \Omega^m=\{a_mx^m+a_{m-1}x^{m-1}+...+a_1x+a_0 | a_m,...,a_1,a_0\in [0,1]\}$

is a compact space.

I do not know how to prove this but this is result is valuable to consider, because by a Fourier identity we could transform the difficulty of (log average) Chowla conjecture to this type of result.

There is some clue to show this type of result could be true, the first one is the result established by Matomaki and Raziwill in 2015:

Theorem 8 (multiplication function in short interval)

${f(n): \mathbb N\rightarrow \mathbb C}$ is a multiplicative function, i.e. ${ f(mn)=f(n)f(m), \forall m,n\in \mathbb N}$ . ${H\rightarrow \infty}$ as ${x\rightarrow \infty}$ , then we have the following result,

$\displaystyle \int_{1\leq x\leq X}|\sum_{x\leq n\leq x+H}f(n)|=o(XH). \ \ \ \ \ (29)$

And there also exists the result which could be established by Vinagrodov estimate and B-S-Z critation :

Theorem 9 (correlation of multiplication function and nil-sequences in long interval)

${f(n): \mathbb N\rightarrow \mathbb C}$ is a multiplicative function, i.e. ${ f(mn)=f(n)f(m), \forall m,n\in \mathbb N}$ . ${g(n)=a_n^m+...+a_1n+a_0}$ is a polynomial function then we have the following result,

$\displaystyle \int_{1\leq n \leq X}|f(n)e^{2\pi i g(n)}|=o(X) \ \ \ \ \ (30)$

\newpage {9} \bibitem{Sarnak} Peter Sarnak, Mobius Randomness and Dynamics.

\texttt{https://publications.ias.edu/sites/default/files/Mahler }. \bibitem{Laudau} JA 虂NOS PINTZ (BUDAPEST). LANDAU鈥橲 PROBLEMS ON PRIMES.

\texttt{https://users.renyi.hu/~pintz/pjapr.pdf} \bibitem{BSZ} Knuth: Computers and Typesetting,

\texttt{http://www-cs-faculty.stanford.edu/\~{}uno/abcde.html}

\bibitem{Cotlar-Stein lemma} Almost orthogonality

\texttt{https://hxypqr.wordpress.com/2017/12/18/almost-orthogonality/}

\bibitem{KAISA MATOMA 虉KI AND MAKSYM RADZIWILL} KAISA MATOMA 虉KI AND MAKSYM RADZIWIL, MULTIPLICATIVE FUNCTIONS IN SHORT INTERVALS.

\texttt{https://arxiv.org/abs/1501.04585v4/}.

semyon dyatlov的一篇文章

semyon dyatlov的文章https://arxiv.org/pdf/1710.05430.pdf，用fractional uncertainly priciple导出了hyperbolic surface上测地线诱导的zeta函数在 $Re(s)>1-\epsilon$ 只有有限个零点。

就我的理解，这件事情至少和3个事情有关系，

1.p-adic上的黎曼猜想，因为这篇文章的证明强烈依赖于markov性质，这和p adic的结构也很像，有可能可以利用p adic猜想的证明思路继续做一部分。

2.billiard的传播子，但是这里不一样，文章中的 Schottky groups本质上是对于算子的逆写成一种级数形式其中级数由Schottky group生成，但是对于billiard传播子的情况所有的涉及的热核或者波核的paramatrix不仅仅具备markov性质，起主导作用的却是某种需要X-ray估计的性质，级数和并不是对全空间求而是某种截断了的子空间里面，所以比这个证明要难。建立起billiard的传播子估计是证明inverse spectral problem的重要一步。

3.interval exchange map，但是interval exchange map的结构就好只有这里的traslation，这里有一个像的大小的指数衰减，这是interval exchange map所没有的。interval exchage map可能还需要涉及到一个拆分估计，会更难，这可能可以在interval exchange map上的sarnak猜想有进展。

下面讲一下我对文章证明主要思路的理解：首先对于hyperbolic空间 $H^2/{\Gamma}$ 我们用poincare的方式来理解为 D mod掉一个作用，那么极限集 $\Lambda_{\Gamma}$ 就是基本域在分式线性变换下在D的边界下的极限点。

关键是建立如下估计 $\int_{\Lambda_{\Gamma}}exp (i\xi\phi(x)) g(x) d\mu(x)\leq C|\xi|^{−\epsilon_1} \forall \xi, |\xi| > 1$ .

为了建立这个估计，我们做的事情是：

1.研究极限集 $\Lambda_{\Gamma}$ 的结构，本质上具备某种组合上的树结构，在分式线性变换下树的上方和下方交换，而且对于象有指数级别的衰减，这很像连分数展开中的otrowoski表示。对于分式线性变换和Schottky group作用的体积形变估计是容易得到的。

2.Patterson–Sullivan测度 $\mu$ 是在 $\Gamma$ 作用下的遍历测度，特别的，和 $\Gamma$ 是compatible的，所以变量代换公式成立：

$\int_{\Lambda_{\Gamma}} f(x)d\mu(x) =\int_{\Lambda_{\Gamma}}f(\gamma(x))|\gamma'(x)|_{\delta}^B d\mu(x) \forall \gamma \in \Gamma$

这个很重要，一旦我们能够找到 $I :=\amalg_{b\in \Omega} I_b$ , 实际这可以导出一个关于f的方程：

$L_Zf(x) = \sum_{a\in Z,a\to b} f(\gamma_{a′}(x))w_{a′}(x), x ∈ I_b.$

我们关心的selberg zeta函数的零点就等于方程特征值1对应的特征函数： $L_zf(x)=f(x)$ .

（这一点很重要而且在很多问题中都有用，至少有几个例子：1.有的时候一个椭圆方程的特征值很难做，转而去考察他的发展方程。2.很多数论问题，特别是质数在某些partition集合里面的分布，对于对应的L函数的动力系统的刻画就需要这个方程）

3. 文章中3.1.是bourgain的主要贡献，是所谓的sum-product现象在这里的一个引用，为了得到foriour衰减性估计，我们需要不断拆散区间，实际上在树的每一层上面我们都很清楚怎么把这一层的积分拆散到上一层和下一层，这实际上可以看成一个renormelization方程：

$\int_{\Lambda_{\Gamma}}f d\mu =\int_{\Lambda_{\Gamma}}L_{Z(\tau)}^{2k+1} f d\mu = \sum_{A,B,A\leftrightarrow B}f(\gamma_{A∗B}(x))w_{A∗B}(x)d\mu(x)$ .

1中的形变估计(只需要估计一下交叉项带来的误差)告诉我们： $| \int_{}fdμ|^2 ≤C\tau^{(2k−1)\delta}\sum_{A,B,A\leftrightarrow B} |\int_{I_b(A)} e^{iξ\phi(\gamma_{A∗B}(x))}w_{a′_k} (x)d\mu(x)| ^2 +C\tau^2$ .

然后在x点taylor展开用围道积分得到误差项，误差项用1中的形变估计得到上界控制，主项放到一起得到：

$|\int_{\Lambda_{\Gamma}} f d\mu|^2 ≤ C\tau^{(2k+1)\delta}\sum_A sup_{\eta\in J_{\tau}}| e^{2πi\eta\xi_{1,A} (b_1)···\xi_{k,A} (b_k )}| + C\tau^{\delta/4}$ . （*）

最后为了用bourgain的sum-product现象得到的引理3.3来控制（*）RHS，我们需要对 $R ⊂ Z(τ) ^{k+1}$ 和 $Z(τ)^{k+1}-R$ 分段估计,前者是用正则性导致的收敛速度快，后者shi用minkowki维数很小，前者估计已经建立，所以只需对Z(τ)^{k+1}-R 建立minkoski维数上界估计，这是显然的。

这样我们就建立好了如下估计：

$\int_{\Lambda_{\Gamma}}exp ( i\xi\phi(x)) g(x) d\mu(x) ≤ C|\xi|^{−\epsilon_1} \forall \xi, |\xi| > 1$ .

这个估计是用来建立fractional uncertain principle的关键，一旦我们有了fractional uncertain principle，hyperbolic surface上测地线诱导的zeta函数在 $Re（s）>\frac{1}{2}-epsilon$ 只有有限个零点就只是一个fix point theorem的argument。

另外一个不需要用sum-product现象的极为简单的证明见[1710.05430] Fractal uncertainty for transfer operators。

Relationship of relationship

mathematics blog

分类： Sum-product phenomenon

Log average sarnak conjecture

The correlation of Mobius function and nil-sequences in short interval

Fractional uncertain principle

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