Abstract
We prove a Central Limit Theorem for the critical points of random spherical harmonics, in the high-energy limit. The result is a consequence of a deeper characterization of the total number of critical points, which are shown to be asymptotically fully correlated with the sample trispectrum, i.e. the integral of the fourth Hermite polynomial evaluated on the eigenfunctions themselves. As a consequence, the total number of critical points and the nodal length are fully correlated for random spherical harmonics, in the high-energy limit.
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1 Introduction and Main Results
1.1 Random Spherical Harmonics and Sample Polyspectra
It is well-known that the eigenvalues \(\left\{ -\lambda _{\ell }\right\} _{\ell =0, 1, 2, \ldots }\) of the Helmholtz equation
on the two-dimensional sphere \({\mathbb {S}}^{2}\), are of the form \(\lambda _{\ell }=\ell (\ell +1)\) for some integer \(\ell \ge 1\). For any given eigenvalue \(-\lambda _{\ell }\), the corresponding eigenspace is the \( (2\ell +1)\)-dimensional space of spherical harmonics of degree \(\ell \); we can choose an arbitrary \(L^{2}\)-orthonormal basis \( \left\{ Y_{\mathbb {\ell }m}(.)\right\} _{m=-\ell ,\dots ,\ell }\) and consider random eigenfunctions of the form
where the coefficients \(\left\{ a_{\mathbb {\ell }m}\right\} \) are independent, standard Gaussian variables if the basis is chosen to be real-valued; the standardization is such that \(\text {Var}(f_{\ell }(x))=1\), and the representation is invariant with respect to the choice of any specific basis \(\left\{ Y_{\ell m},\text { }m=-\ell ,\dots ,\ell \right\} \). The random fields \(\{f_{\ell }(x),\;x\in {\mathbb {S}}^{2}\}\) are isotropic, meaning that the probability laws of \(f_{\ell }(\cdot )\) and \(f_{\ell }^{g}(\cdot ):=f_{\ell }(g\cdot )\) are the same for any rotation \(g\in SO(3)\); they are also centred and Gaussian, and from the addition theorem for spherical harmonics (see [18], Equation (3.42)) the covariance function is given by,
where \(P_{\ell }\) are the usual Legendre polynomials, \(\cos d(x,y)=\cos \theta _{x}\cos \theta _{y}+\sin \theta _{x}\sin \theta _{y}\cos (\varphi _{x}-\varphi _{y})\) is the spherical geodesic distance between x and y and \((\theta _{x},\varphi _{x})\), \((\theta _{y},\varphi _{y})\) are the spherical coordinates of x and y, respectively.
In this paper, we shall be concerned with the number of critical points of \(f_{\ell }(\cdot )\), defined as usual as
it was shown in [24] (see also [8]) that we have
whereas (see [10]) the variance of \({\mathcal {N}}_{\ell }^{c}\) is such that
We now study the limiting distribution of the fluctuations around the expected value. First recall that the sequence of Hermite polynomials \(H_{q}(u)\) is defined by
so that
We refer to [25] for a detailed discussion of Hermite polynomials, their properties and their ubiquitous role in the analysis of Gaussian processes. Below we shall also exploit the sequence of (random) sample polyspectra, which we define as (see, e.g. [8, 17, 20, 22, 23, 27])
It is readily checked that \(h_{\ell ;0}=4\pi \) and \(h_{\ell ;1}=0,\) for all \(\ell \); we also have \({\mathbb {E}}\left[ h_{\ell ;q}\right] =0\), for all \(q=1,2,\dots \) As far as variances are concerned, we have that (see [22, 23, 27])
for \(q=3, 5, 6\ldots \), where
and \(J_{0}(.)\) is the usual Bessel function of the first kind.
1.2 Main Results
Our first main result in this paper is to show that the number of critical points and the sample trispectrum \(\left\{ h_{\ell ;4}\right\} \) are asymptotically fully correlated: as \(\ell \rightarrow \infty \)
In fact, our result is slightly sharper than that, as shown in the statement of Theorem 1.1. Recall first that the variance for the total number of critical points was computed in [10] to be asymptotic to (1.1). Let us now introduce the random sequence
for which it is readily seen that
because
It is convenient to write
We can now formulate the following
Theorem 1.1
As \(\ell \rightarrow \infty \)
and hence
As a consequence of the previous theorem, for \(\ell \rightarrow \infty \), we have that (1.2) holds, so that the total number of critical points is fully correlated in the limit with \(\left\{ h_{\ell ;4}\right\} \). The limiting distribution of \(\left\{ h_{\ell ;4}\right\} \) was already studied in [23], where it was shown that a (quantitative version of the) Central Limit Theorem holds. Our next main result hence follows immediately; recall first that the Wasserstein distance between the probability distributions of two random variables (X, Y) is defined by
where
Theorem 1.2
As \(\ell \rightarrow \infty \), for Z a standard Gaussian variable, we have that
and hence
Remark 1.3
The previous theorems include actually two separate results, namely:
-
(i)
The asymptotic behaviour of the total number of critical points is dominated by its projection on the fourth-order chaos term (see Sect. 2);
-
(ii)
The projection on the fourth-order chaos can be expressed simply in terms of the fourth-order Hermite polynomial, evaluated on the eigenfunctions \(\left\{ f_{\ell }\right\} \), without the need to compute Hermite polynomials evaluated on the first and second derivatives of \(\left\{ f_{\ell }\right\} \), despite the fact that the latter do appear in the Kac–Rice formula and they are not negligible in terms of asymptotic variance.
As we shall discuss in the following section, both these findings have analogous counterparts in the behaviour of the boundary and nodal length, as investigated, i.e. in [21]. Due to the nature of our proof, we do not see any easy path to extend our results to non-smooth distances such as the Kolmogorov one, apart from the bound one obtains by standard inequalities between probability metrics such as (C.2.6) in the book [25]. We recall here also that the Wasserstein distance can be equivalently expressed in terms of couplings as
where \(\Gamma (X,Y)\) is the set of all bivariate probability measures having the same marginal laws as X and Y.
1.3 Discussion: Correlation Between Critical Points and Nodal Length
The results in our paper should be compared with a recent stream of the literature which has investigated the relationship between geometric features of random spherical harmonics and sample polyspectra. The first results in this area are due to [22], which studied the excursion area of \(\left\{ f_{\ell }\right\} \) above a threshold \(u\in {\mathbb {R}}\) (which we label \({\mathcal {L}}_{2}(u;\ell ) \)), and showed that it is asymptotically dominated (after centering) by a term of the form \(-u\phi (u)h_{\ell ;2}/2;\) in particular, they showed that
-
(i)
There is full correlation, in the high-energy limit, between \(h_{\ell ;2}\) and the excursion area, for all \(u\ne 0\);
-
(ii)
For \(u=0\) (the case of the so-called Defect) this leading term vanishes, and the asymptotic behaviour is radically different: all the odd-order chaoses of order greater or equal to 3 are correlated with the excursion area.
The same pattern of behaviour was later established for the boundary length \({\mathcal {L}}_{1}(u;\ell )\) (for \(u\ne 0)\) (see also [33]) and the Euler characteristic \({\mathcal {L}}_{0}(u;\ell )\) (for \(u\ne 0,\pm 1,\) see, i.e. [11] and the references therein), thus covering the behaviour of all three Lipschitz–Killing Curvatures (see [1]). More explicitly, we have that, as \(\ell \rightarrow \infty \), (see, i.e. [11])
whence
and
Loosely speaking, it can be concluded that these three Lipschitz–Killing curvatures are asymptotically proportional to \(h_{\ell ;2}\) in the high-energy limit for \(u\ne 0\) (and also for \(u\ne \pm 1\) in the case of Euler characteristics), and thus, they are fully correlated at different thresholds and among themselves.
These results were extended in [12] to critical values over the interval I. More precisely, let \(I\subseteq {\mathbb {R}}\) be any interval in the real line; we are interested in the number of critical points of \( f_{\ell }\) with value in I:
For the expectation, it was shown in [8] that for every interval \(I\subseteq {\mathbb {R}}\) we have, as \(\ell \rightarrow \infty \),
moreover, for I such that
we have that
More precisely, it was shown in [12], that
We call I nondegenerate if and only if
For instance, semi-intervals \(I=[u,\infty )\) with \(u\ne 0\) are nondegenerate. As a consequence, for the same range of values of u, we have that
For \(I=[0,\infty )\) or \({\mathbb {R}}\) (corresponding to the total number of critical points), the leading constant \(\nu ^{c}(I)\) vanishes, and, accordingly, the order of magnitude of the variance is smaller than \(\ell ^{3}\); indeed, as \(\ell \rightarrow \infty \) (see [10]),
This behaviour is again similar to what was found for \({\mathcal {L}} _{1}(0;\ell )\) (the nodal length of random spherical harmonics), for which it was shown in [32] that
actually our expression here differs from the one in [32] by a factor 1/4, because \({\mathcal {L}}_{1}(0;\ell )\) is equivalent to half the nodal length of random spherical harmonics considered in that paper. It was later shown in [21] that the following asymptotic equivalence holds:
consistent with the computation of the variance in [32], because (see [23])
In particular, we have that
Our results in this paper show that the asymptotic behaviour of the total number of critical points (i.e. \(I={\mathbb {R}}\)) is dominated by exactly the same component as the nodal length, and indeed
Summing up, the literature so far has established the full correlation of Lipschitz–Killing curvatures and critical values among themselves and with the sequence \(\left\{ h_{\ell ;2}\right\} \) for nondegenerate values of the threshold parameter u. Here, we show that in the degenerate cases (\( u=-\infty , 0\) for critical points) full-correlation still exists between nodal length and critical points, as both are proportional to the sample trispectrum \(h_{\ell ;4}=\int _{{\mathbb {S}}^{2}}H_{4}(f_{\ell }(x)){\text {d}}x\). The correlation is positive, which is to say that the realization that corresponds to a higher number of critical points are those where longer nodal lines are going to be observed. Heuristically, it can be conjectured that a higher number of critical points will typically correspond to a higher number of nodal components, and hence, nodal length will be as well larger than average. One cautious note is needed here: whereas the correlation converges to unity, it does so only at a logarithmic rate, so it may not be simple to visualize this effect by simulations with values of \( \ell \) in the order of a few hundreds. On the contrary, the correlation for values of the threshold u different from zero occurs with rate \(\ell ^{-1}\) and shows up very neatly in simulations.
A number of other papers have investigated the geometry of random eigenfunctions on the sphere and on the torus in the last few years. Among these, we recall [22] and [23] for the excursion area and the Defect; [7, 15, 19] and [5] for the nodal length/volume of arithmetic random waves; [13] for the number of intersections of random eigenfunctions; [21] for the nodal length of random spherical harmonics; [26] for the nodal length of Berry’s random waves on the plane; [28] and [29] for nodal intersections. Zeroes of random trigonometric polynomials have been considered, for instance, by [2,3,4] and the references therein. Moreover, [6, 31] and [30] study the fluctuations of nodal length and excursion area over subsets of the torus and of the sphere (see also [14] for random eigenfunctions on more general manifolds); the asymptotic behaviour of the number of critical points of spherical harmonics restricted to subsets or shrinking domains is currently under investigation. Similarly to results which were established in the above-mentioned papers, we expect asymptotic full correlation to hold between the “local” number of critical points and the “local sample trispectrum”, as introduced in [31]; the investigation of this conjecture is still ongoing.
1.4 Plan of the Paper
In Sect. 2, we present some background material on Kac–Rice techniques, Wiener chaos expansions and the relevant covariant matrices for our (covariant) gradient and Hessian. The proof of our main results is given in Sect. 3, where we show that the total number of critical points is asymptotically fully correlated with the integral of the fourth-Hermite polynomial evaluated on the eigenfunctions themselves. The projection coefficients on Wiener chaoses that we shall need are only three, and their computation is collected in Sect. 4. In Sect. 5, we consider the terms in the chaos expansion with odd index Hermite polynomials. The technical computations are in “Appendix A”.
2 Chaos Expansion
As discussed in [8, 9, 11] and [12], by means of Kac–Rice formula, the number of critical points can be formally written as
where the identity holds both almost surely (using, i.e. the Federer’s coarea formula, see [1]), and in the \(L^{2}\) sense, i.e.
for
The validity of this limit, in the \(L^{2}(\Omega )\) sense, was shown in [11, 12]. The approach for the proof is to start from the Wiener chaos expansion
where \(\left\{ {\mathcal {N}}_{\ell }^{c}[q]\right\} \) denotes the chaos-component of order q, or equivalently the projection of \({\mathcal {N}}_{\ell }^{c}\) on the qth order chaos components, which we shall describe below. In order to define and compute more explicitly these chaos components, let us introduce the differential operators
Covariant gradient and Hessian follow the standard definitions, discussed for instance in [11]; here, we simply recall that
We can then introduce the \(5\times 1\) vector \((\nabla f_{\ell }(x),vec\nabla ^{2}f_{\ell }(x))\); its covariance matrix \(\sigma _{\ell }\) is constant with respect to x and it is computed in [12]. It can be written in the partitioned form
where the superscript T denotes transposition, and
Let us recall that the Cholesky decomposition of a Hermitian positive-definite matrix A takes the form \(A=\Lambda \Lambda ^\mathrm{T},\) where \(\Lambda \) is a lower triangular matrix with real and positive diagonal entries, and \(\Lambda ^\mathrm{T}\) denotes the conjugate transpose of \( \Lambda \). It is well-known that every Hermitian positive-definite matrix (and thus also every real-valued symmetric positive-definite matrix) admits a unique Cholesky decomposition.
By an explicit computation, it is possible to show that the Cholesky decomposition of \(\sigma _{\ell }\) takes the form \(\sigma _{\ell }=\Lambda _{\ell }\Lambda _{\ell }^{t},\) where
in the last expression, for notational simplicity we have omitted the dependence of the \(\tau _{i}\)s on \(\ell \). matrix is block diagonal, because under isotropy the gradient components are independent from the Hessian when evaluated at the same point. We can hence define a five-dimensional standard Gaussian vector \(Y(x)=(Y_{1}(x),Y_{2}(x),Y_{3}(x),Y_{4}(x),Y_{5}(x))\) with independent components such that
Note that asymptotically
where (as usual) \(a_{\ell }\sim b_{\ell }\) means that the ratio between the left- and right-hand side tends to unity as \(\ell \rightarrow \infty \). Hence,
Thus, we obtain
where
The qth order chaos is the space generated by the \(L^{2}\)-completion of linear combinations of the form \(H_{q_{1}}(Y_{1})\cdots H_{q_{5}}(Y_{5}),\) with \( q_{1}+q_{2}+ \cdots +q_{5}=q\) (see, i.e. [25]); in other words, it is the linear span of cross-product of Hermite polynomials computed in the independent random variables \(Y_{i},\) \(i=1, 2,\dots 5\), which generate the gradient and Hessian of \(f_{\ell }\). In particular, the fourth-order chaos can be written in the following form:
where
The projection coefficients \(k_{i}\), \(h_{ij}\), \(g_{ij}\), \(p_{ijk}\), and \(q_{ijkl}\) are constant with respect to \(\ell \).
3 Proof of Theorems 1.1 and 1.2
3.1 Proof of Theorem 1.1
In this section, we give the proof of our main result. Let us start with the \(L^{2}(\Omega )\), \(\varepsilon \)-approximation to the number of critical points [12]
for every \(x\in {\mathbb {S}}^{2}\) we define
By continuity of the inner product in \(L^{2}(\Omega )\), we write
Now, note that both \(\psi _{\ell }^{\varepsilon }(x)\) and \(H_{4}(f_{\ell }(y)) \) are isotropic processes on \({\mathbb {S}}^{2}\), and hence, we have
by continuity of covariances. Moreover, because all integrands are finite-order polynomials we have
where in the last steps we used orthogonality of Wiener chaoses and isotropy; we take \({\overline{x}}=(\frac{\pi }{2},0)\) and \(y(\phi )=(\frac{\pi }{2},\phi )\). More explicitly, the previous argument allows us to perform our argument on the equator, where \(\theta \) is fixed to \(\pi /2\). Note that
and hence
We shall show below that the asymptotic behaviour of \(\text {Cov}({\mathcal {N}}_{\ell }^{c},h_{\ell ;4})\) is dominated by three terms corresponding to
and
The computation of these leading covariances is given in the three Lemmas A.1–A.3 to follow, where it is shown that
All the remaining terms in \(\text {Cov}({\mathcal {N}}_{\ell }^{c},h_{\ell ;4})\) are shown to be \(O(\ell ^{-2})\) or smaller in Sect. 5 and Lemmas A.4–A.9 below. From Proposition 4.1, we know that
Substituting and after some straightforward algebra, one obtains
Because
we find
so that our proof of our main theorem is completed, recalling that, as \(\ell \rightarrow \infty \),
Remark 3.1
A consequence of Theorem 1.1 is that, as \(\ell \rightarrow \infty \),
so that
Note that by orthogonality, we have
where the odd terms in the expansion vanish by symmetry arguments, \({\mathrm{Var}}\left( \text {Proj}[{\mathcal {N}}_{\ell }^{c}|0]\right) =0\) is obvious and \({\mathrm{Var}}\left( \text {Proj}[{\mathcal {N}}_{\ell }^{c}|2]\right) =0\) was shown in [12]. Hence, we have the bound
In fact, by a careful investigation of the asymptotic behaviour of higher-order chaotic projections it seems possible to establish the slightly stronger result
we omit this investigation for brevity’s sake.
3.2 Proof of Theorem 1.2
It was shown in [23] that
the result then follows from Theorem 1.1 and the triangle inequality
4 Evaluation of the Projection Coefficients \(h_{52},k_{2},k_{5}\)
In this section, we evaluate the three projection coefficients in the Wiener chaos expansion which are required for the completion of our arguments.
Proposition 4.1
We have that
Proof
Let us recall first the following simple result
Indeed, for example
since
Now, note that
and
Let us introduce the change of variables
so that \((Z_1, Z_2, Z_2)\) is a centred Gaussian vector with covariance matrix
and we can write
The coefficient \(k_{2}\) can be computed as follows: write
where the symmetric matrix A given by
we apply [16], Theorem 2.1, to obtain
where we have that
and computing the integral with Cauchy methods for residuals, we get
and
as claimed. We introduce now the following notation
for \(r=0,2,4\), so that,
and
The statement follows applying the results in [10] where it is proved that
\(\square \)
5 Terms with Odd Index Hermite Polynomials
In this section, we prove that the terms in the 4-th chaos formula (2.2) with odd index Hermite polynomials produce in \(\text {Cov}({\mathcal {N}}_{\ell }^{c},h_{\ell ;4})\) terms of order \(O(\ell ^{-2})\) and terms equal to zero. We first focus, in the following proposition, on the projection coefficients.
Proposition 5.1
The projection coefficients \(g_{ij}\), \(p_{ijk}\) and \(q_{ijkl}\) are such that
-
For \(i, j \ne 3, 5\), we have \(g_{ij}=0\),
-
For \(j, k \ne 1, 2, 4\), we have \(p_{ijk}=0\),
-
We have \(q_{ijkl}=0\).
Proof
Recalling that for a odd we have
from this we immediately see that the coefficients \(g_{ij}\) with \(i, j = 1, 2\) are all equal to zero. We consider now the coefficients \(g_{ij}\) with \(i=4\) or \(j=4\), for these coefficients we observe that the expectation with respect to the random variable \(Y_4\) vanishes since it is expressed as the integral of an odd function. The proof of the last two points of the statement is similar. \(\square \)
In Lemmas A.7–A.9, we prove that the terms in \(\text {Cov}({\mathcal {N}}_{\ell }^{c},h_{\ell ;4})\) that are multiplied by the projection coefficients not discussed in Proposition 5.1 are either zero or of order \(O(\ell ^{-2})\). In particular, we prove that, for \(a, b=3, 5\), \(a \ne b\),
for \(a=1, 2\),
and that
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This paper has been built out of earlier research carried over with Igor Wigman; we are very grateful to him for many suggestions and insightful discussions. DM acknowledges the MIUR Excellence Department Project awarded to the Department of Mathematics, University of Rome Tor Vergata, CUP E83C18000100006. VC has received funding from the Istituto Nazionale di Alta Matematica (INdAM) through the GNAMPA Research Project 2019 “Proprietà analitiche e geometriche di campi aleatori”.
Appendix A Auxiliary Lemmas
Appendix A Auxiliary Lemmas
In this appendix, we collect a number of technical results that were exploited for the correlation results above. We divide the results into two subsections, collecting, respectively, dominant and subdominant terms.
1.1 Dominant Terms
In this subsection, we collect the results concerning the three dominant terms.
Lemma A.1
As \(\ell \rightarrow \infty \),
Proof
Note first that, by Diagram Formula (see [18] Section 4.3.1),
Now, we have easily
and
Thus, we obtain
using, see Lemma A.10 below,
\(\square \)
Lemma A.2
As \(\ell \rightarrow \infty \)
Proof
As before, note first that
where we wrote
note that
Now,
Likewise
Thus, we obtain
Now, again using Lemma A.10 below,
and exploiting instead Lemma A.12
Noting that, for \(k=1,\dots 4\),
the proof is completed. \(\square \)
Lemma A.3
As \( \ell \rightarrow \infty \),
Proof
Again by Diagram Formula, we have that
Now, using repeatedly Lemmas A.10 and A.12 we obtain
and thus, the conclusion follows. \(\square \)
1.2 Subdominant Terms
The behaviour of subdominant terms can be characterized rather easily, as follows.
Lemma A.4
As \(\ell \rightarrow \infty \), for \(a=1, 3, 4\),
Proof
For \(a=1\), we have that
Now, we have easily
Similarly
and
whence
Finally,
where
\(\square \)
Lemma A.5
For \(a=1,4,\) we have that
Proof
It was shown in the proof of Lemma A.4 that \({\mathbb {E}}\left[ Y_{1}({\bar{x}}))f_{\ell }(y(\phi )\right] ={\mathbb {E}}\left[ Y_{4}({\bar{x}} ))f_{\ell }(y(\phi )\right] =0.\) The result is then an immediate consequence of the Diagram Formula (see (A.2.13) on page 202 of [25]). \(\square \)
We are then left with only two terms to consider.
Lemma A.6
For \(a=2,5,\) we have that
Proof
We have that
and therefore
Finally,
exploiting again Lemma A.12, the result follows. \(\square \)
Lemma A.7
As \(\ell \rightarrow \infty \), for \(a, b=3, 5\), \(a \ne b\)
Proof
By applying again the Diagram Formula (see [18] Section 4.3.1), we have
We observe that
moreover
and
The statement follows by applying Lemma A.12. \(\square \)
Lemma A.8
For \(a=1, 2\),
Proof
From Diagram Formula, we have
The statement follows by observing that (for \(a=1,2\)) \({\mathbb {E}}[Y_{a}({\bar{x}}) Y_{3}({\bar{x}}) ]=0\), \({\mathbb {E}}[ Y_{a}({\bar{x}}) Y_{5}({\bar{x}})]=0\), \({\mathbb {E}}[ Y_{3}({\bar{x}}) Y_{5}({\bar{x}}) ]=0\), and \({\mathbb {E}}[Y_{a}({\bar{x}})f_{\ell }(y(\phi ))] =0\). \(\square \)
Lemma A.9
We have
Proof
Once again, the statement follows from Diagram Formula, which gives
and \({\mathbb {E}}[Y_{4}({\bar{x}}) Y_{3}({\bar{x}}) ]=0\), \({\mathbb {E}}[ Y_{4}({\bar{x}}) Y_{5}({\bar{x}})]=0\), \({\mathbb {E}}[ Y_{3}({\bar{x}}) Y_{5}({\bar{x}}) ]=0\), \({\mathbb {E}}[Y_{4}({\bar{x}})f_{\ell }(y(\phi ))] =0\). \(\square \)
1.3 Some Useful Integrals
We write as usual
For our main arguments to follow, a key step is to recall the following results, which are proved in [8], Lemma C3. For all constants \(C>0,\) we have, uniformly over \({C}/{\ell }\le \phi \le {\pi }/{\ell }\)
where \(\psi _{\ell }^{\pm }=(\ell +{1}/{2})\phi -{\pi }/{4}\) for \( r=0, 2\), and \(\psi _{\ell }^{\pm }=(\ell +{1}/{2})\phi +{\pi }/{4}\) for \(r=1\), and
Our results will then follow from the following two lemmas:
Lemma A.10
For \(r=0, 1, 2\), we have
Likewise
Remark A.11
More compactly, for \(r_{1}, r_{2}=0, 1, 2\), we could have written the single expression
Proof
We recall first that \(P_{\ell }^{(r)}(\cos \phi )\le \ell ^{2r}\) for all \(\phi \in [0,2\pi )\). Hence,
and it suffices to consider \(\phi >{C}/{\ell }\). Hence, we have
It is not difficult to see that, for \(k=1,\dots ,4\),
indeed the previous integrals are bounded by, for \(r=2\),
Likewise, for \(r=1\),
Thus,
The following equalities can be established by simple trigonometric identities:
Thus, we have
since
The proof of the first part of the lemma is then concluded. The proof of the second result is very similar, and we can omit some details; in particular, we simply recall the identity
Because \(\cos 2x+1=2\cos ^{2}x\), it is not difficult to see that
Dealing with the lower order terms as in the first part of the lemma, we can now conclude with our second statement, i.e.
\(\square \)
In our second auxiliary result, an upper bound is given.
Lemma A.12
As \(\ell \rightarrow \infty \), we have that
Proof
As before, for the “local” component where \(\phi <C/\ell \) (some fixed constant C) we have
On the other hand, using again formulas (5.1) and (5.2), and computations analogous to Lemma A.10, we find easily that
\(\square \)
Note in particular that for \(k=4\), we obtain the bound
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Cammarota, V., Marinucci, D. On the Correlation of Critical Points and Angular Trispectrum for Random Spherical Harmonics. J Theor Probab 35, 2269–2303 (2022). https://doi.org/10.1007/s10959-021-01136-y
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DOI: https://doi.org/10.1007/s10959-021-01136-y
Keywords
- Random fields
- Critical points
- Wiener chaos expansion
- Spherical harmonics
- Berry’s cancellation phenomenon