Critical two-point function for long-range $O(n)$ models below the upper critical dimension
Martin Lohmann, Gordon Slade, Benjamin C. Wallace

TL;DR
This paper rigorously proves that in long-range $O(n)$ models below the upper critical dimension, the critical two-point function decays with a mean-field exponent, confirming a prediction from physics literature using renormalisation group and cluster expansion techniques.
Contribution
It provides the first rigorous proof of the critical two-point function decay with mean-field exponent in long-range models below the upper critical dimension.
Findings
Critical two-point function decays as $r^{-(d-rac{1}{2}(d+ ext{epsilon}))}$
Validation of physics prediction from 1972 about critical exponents
Application of rigorous renormalisation group and cluster expansion methods
Abstract
We consider the -component lattice spin model () and the weakly self-avoiding walk () on , in dimensions . We study long-range models based on the fractional Laplacian, with spin-spin interactions or walk step probabilities decaying with distance as with . The upper critical dimension is . For , and , the dimension is below the upper critical dimension. For small , weak coupling, and all integers , we prove that the two-point function at the critical point decays with distance as . This "sticking" of the critical exponent at its mean-field value was first predicted in the physics literature in 1972. Our proof is based on a rigorous renormalisation group method. The treatment of…
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Critical two-point function for long-range
models below the upper critical dimension
Martin Lohmann
Gordon Slade
Benjamin C. Wallace Department of Mathematics, University of British Columbia, Vancouver, BC, Canada V6T 1Z2. E-mail: [email protected], [email protected], [email protected].
(September 20, 2017)
Abstract
We consider the -component lattice spin model () and the weakly self-avoiding walk () on , in dimensions . We study long-range models based on the fractional Laplacian, with spin-spin interactions or walk step probabilities decaying with distance as with . The upper critical dimension is . For , and , the dimension is below the upper critical dimension. For small , weak coupling, and all integers , we prove that the two-point function at the critical point decays with distance as . This “sticking” of the critical exponent at its mean-field value was first predicted in the physics literature in 1972. Our proof is based on a rigorous renormalisation group method. The treatment of observables differs from that used in recent work on the nearest-neighbour 4-dimensional case, via our use of a cluster expansion.
1 Introduction and main result
Broadly speaking, the mathematical understanding of critical phenomena for spin systems has progressed in dimension , where exact solutions and SLE are important tools; in dimensions , where infrared bounds and the lace expansion are useful; and in dimension , where renormalisation group (RG) methods have been applied. The physically most important case of is more difficult, and mathematical methods are scarce.
In the physics literature, the -expansion was introduced to study non-integer dimensions slightly below . An alternate approach is to consider long-range models, which change the upper critical dimension from to a lower value with . By choosing and with small , it is possible to study integer dimension which is slightly below the upper critical dimension . In this paper, we consider -component spins and the weakly self-avoiding walk in this long-range context, and prove that the critical two-point function has mean-field decay also below the upper critical dimension. Our method involves a RG analysis in the vicinity of a non-Gaussian fixed point.
1.1 Introduction
We consider long-range models on for integers and dimensions . The case is the continuous-time weakly self-avoiding walk, and the case is the -component lattice spin model. For the underlying random walk model takes steps of length with probabilities decaying as with , and for the spin-spin interaction in the Hamiltonian has that same decay. More precisely, the models are based on the fractional Laplacian , whose kernel decays at large distance as .
The upper critical dimension is predicted to be for all . Thus, for , mean-field behaviour is predicted; this has been proved for self-avoiding walk, for the Ising model, for the 1-component model, and for other models [3, 17, 22, 23]. In the physics literature, it is observed that below the upper critical dimension the critical two-point function continues to exhibit the mean-field decay for , and then crosses over to decay for . Here is the exponent for the nearest-neighbour model; for this is for [35], and a recent estimate for is [18]. The earliest paper to elucidate the critical behaviour of long-range models is [20], with [33] roughly contemporaneous and [29] providing further development. A very recent paper which analyses the crossover for the two-point function in detail for is [8]. At the crossover, when , a logarithmic correction is predicted, with overall decay [11, 8]. The relationship with conformal invariance is explained in [28].
Let ; ; and . We use a rigorous RG argument to prove that for small , the critical two-point function has decay . This proves the “sticking” of the critical exponent at its mean-field value, for slightly above , or equivalently, for slightly below the upper critical dimension . Our proof extends recent results and methods used to study the -expansion for the critical exponents for the susceptibility and specific heat of the long-range models [31]. It also relies on results and techniques developed to study related problems for the 4-dimensional nearest-neighbour models [16, 5, 32]. However, our treatment of observables differs from that used in the 4-dimensional case, via our application of a cluster expansion.
Earlier mathematical work which applies RG methods to long-range models includes the construction of global RG trajectories for and [27], and for a continuum version of the model in [12, 1]. These references do not study critical exponents. The exponents for critical correlations in a certain hierarchical version of the model, for and , are computed in [2]. For a closely related continuum model with in dimensions , a proof of the “sticking” of the critical exponent for the critical two-point function was announced in a 2013 lecture [24].
1.2 Fractional Laplacian
The models we study are defined in terms of the fractional Laplacian. We now define the fractional Laplacian and list some of its properties. Further details can be found in [31, Sections LABEL:alpha-sec:fL–LABEL:alpha-sec:frd].
Let and . We write for the Euclidean norm of . Let be the matrix with if , and otherwise . Let denote the identity matrix. The lattice Laplacian on is . For , let
[TABLE]
The matrix element can be written as the Fourier integral
[TABLE]
The fractional Laplacian is the matrix defined by
[TABLE]
For , the fractional Laplacian decays as
[TABLE]
(see [31, Lemma LABEL:alpha-lem:fracLapdecay], or [10, Theorem 5.3] for a more precise and more general statement). Here, and in the following, we write to denote the existence of such that . For , , , , and , the resolvent obeys
[TABLE]
with depending on (see [31, Lemma LABEL:alpha-lem:covbd]). For , an asymptotic formula
[TABLE]
is proven in [9, Theorem 2.4], with precise constant .
Given integers , let denote the -dimensional discrete torus of side length . The torus fractional Laplacian is defined by
[TABLE]
The sum on the right-hand side of (1.7) converges, by (1.4).
1.3 The model
We first define the model on the torus , as usual for spin systems. Let and . Let . The spin field is a function , denoted , which we may regard as an element . The Euclidean norm of is , with inner product . We extend the action of the fractional Laplacian to act on the spin field component-wise, namely .
Given and , we define the interaction by
[TABLE]
The partition function is defined by
[TABLE]
where is the Lebesgue measure on . The expectation of a random variable is
[TABLE]
Given lattice points , we define the finite- and infinite-volume two-point function by
[TABLE]
On the left-hand side of (1.12) we have , and on the right-hand side we identify these points with elements of for large , by regarding the vertices of as a cube in (without boundaries identified) approximately centred at the origin. The susceptibility is defined by
[TABLE]
and can be used to identify the critical point of the model. By translation invariance, is independent of . Existence of the infinite volume limits in (1.12)–(1.13), in our context, is discussed below.
1.4 Weakly self-avoiding walk
Let and . Let denote the continuous-time Markov chain with state space and infinitesimal generator . Verification that has the attributes required of a generator is given in [31, Lemma LABEL:alpha-lem:DJ]. Let be the probability measure associated with , and the corresponding expectation; a subscript specifies . The transition probabilities are given by
[TABLE]
The local time of at up to time is the random variable . The self-intersection local time up to time is the random variable
[TABLE]
Given , , and , the continuous-time weakly self-avoiding walk two-point function is defined by the integral
[TABLE]
and the susceptibility is defined by
[TABLE]
The labels [math] on the left-hand sides of (1.16)–(1.17) reflect the fact that the weakly self-avoiding walk corresponds to the formal case of the -component model. As in earlier work on the 4-dimensional case, e.g., [32, 31], we treat both cases (spins) and (self-avoiding walk) simultaneously and rigorously, via a supersymmetric spin representation for the weakly self-avoiding walk.
1.5 Susceptibility and critical point
Let ; ; be sufficiently large; be sufficiently small; and . Let denote the diagonal element of the Green function, i.e., . One of the main results of [31] is that there exists such that, for , there exist and such that for with ,
[TABLE]
This is a statement that there is a critical point at , and that the critical exponent exists to order , with
[TABLE]
It is part of the statement that for the susceptibility is given by the infinite-volume limit (1.13), under the above hypotheses. The critical exponent for the specific heat is also computed to order in [31], for .
1.6 Main result
Our main result is the following theorem, which shows that just below the upper critical dimension, the exponent for the critical two-point function “sticks” at its mean-field value (see (1.6)), as predicted by [20]. The theorem applies for all , including the case of the weakly self-avoiding walk. The critical value is the one mentioned in Section 1.5. As part of the proof of the theorem, it is shown that for the infinite-volume limit (1.12) exists for .
Theorem 1.1**.**
Let ; ; be sufficiently large; be sufficiently small; and . For the critical two-point function obeys, as ,
[TABLE]
Note that Theorem 1.1 identifies the constant in the decay of the interacting two-point function only up to an error of order . However, the error is uniformly bounded in , so the power in the decay rate takes its mean-field value, and this is true to all orders in .
1.7 Strategy of proof
The proof is based on a rigorous RG method developed in a series of papers by Bauerschmidt, Brydges and Slade, where the focus is on the nearest-neighbour models in dimension 4. The method is adapted to the long-range setting in [31].
Fix as in the statement of Theorem 1.1. In [31], given small , a critical value is constructed, with the property that the critical point is given by . Let
[TABLE]
For , let . For , the two-point function obeys
[TABLE]
where denotes Gaussian expectation with covariance ( ensures existence of the inverse). Thus the two-point function is interpreted as a perturbation of a Gaussian expectation. A similar representation is valid for the weakly self-avoiding walk, using a Gaussian superexpectation.
Perturbation theory is performed inductively in a multi-scale fashion, using a finite-range decomposition , with of range . This is implemented via the Gaussian convolution identity , where denotes Gaussian convolution. At every step in the induction, we get a representation
[TABLE]
where the polynomial
[TABLE]
includes all Euclidean- and -invariant monomials that are relevant and marginal according to the RG philosophy. The error in this approximation is irrelevant in the RG sense and is controlled uniformly in the volume by parametrising it as a polymer gas. According to (1.23), after the final step of the induction has been performed, we obtain
[TABLE]
To control (), we need to study the RG dynamical system
[TABLE]
and its non-perturbative corrections. The initial condition is . (In fact, the coupling constant does not play an important role for the two-point function.) For , the dynamical system has a Gaussian fixed point. We use the adaptation of the RG method, as developed in [31], to the long-range setting below the upper critical dimension, where the fixed point is instead non-Gaussian. In [31] only the flow of was studied and did not appear, but the flow of remains identical when these additional coupling constants do appear. For the nearest-neighbour model on , the RG method was applied in [5, 32] to prove decay of the critical two-point function for all . We mainly follow the approach of [5, 32]. In particular, our treatment of the flow of remains the same and yields
[TABLE]
where , and where is the coalescence scale defined to ensure that when . By definition of , the right-hand side of (1.27) is zero for below the coalescence scale, and this remains true non-perturbatively as well: for scales .
The flow of was analysed recursively for the Gaussian RG fixed point in [5, 32], but for the non-Gaussian fixed point in our current setting the recursive analysis cannot be applied due to the non-summability of remainder terms, and a different approach is needed. Let and . According to (1.23),
[TABLE]
Let , which is independent of . Using Gaussian integration by parts and translation invariance, we show in (4.7) that
[TABLE]
By using (1.23) to evaluate the two terms in the above right-hand side approximately, we thus obtain
[TABLE]
This relates to the bulk coupling constants whose flow is known from [31]. In particular, it is shown in [31] that . All of the above is carried out uniformly in , which permits the limit to be taken after the infinite-volume limit. Since , all this, together with the rigorous versions of (1.27) and (1.25), implies our main result (1.20). The non-perturbative corrections to (1.30) due to the irrelevant error coordinate are controlled using a cluster expansion. This is the main innovation in the proof of Theorem 1.1.
The remainder of the paper is organised as follows. In Section 2, we provide some background and definitions needed for the RG method. In Section 3, we formulate the RG map and state the main theorem which provides estimates on the RG map; this is an adaptation of the main result of [16] as applied to the long-range model in [31]. The main difference, compared to [31], is the inclusion of observables in the RG map. The flow of the observable coupling constant is analysed in Section 4. The flow of the observable coupling constant is then analysed in Section 5.1, where the proof of Theorem 1.1 is completed.
2 Set-up for RG method
In this section, we summarise some notation and background for the RG method, needed for the proof of Theorem 1.1. Additional details can be found in [31].
2.1 Formula for two-point function
We begin with a formula for the two-point function that serves as our starting point.
2.1.1 The case
For , we define
[TABLE]
Given , , , we set
[TABLE]
Given , we introduce observable fields , and define and by
[TABLE]
with .
Given a covariance matrix , let denote the Gaussian expectation with covariance . Let . By shifting part of the term into the covariance, the expectation (1.10) can be rewritten as
[TABLE]
where denotes the evaluation of at . When is a monomial, it is standard to write this ratio of expectations as a derivative of a logarithmic generating function. Let denote the operator , and similarly for higher derivatives. Then the two-point function is given, for , by
[TABLE]
2.1.2 The case
For , as in several previous papers (e.g., [6, 5, 32]) we formulate the weakly self-avoiding walk model as the infinite-volume limit of a supersymmetric version of the model. The supersymmetric model involves a complex boson field and a fermion field given by the 1-forms , . For , in place of (2.1), we set
[TABLE]
and we replace in (2.3) by .
For , a formula closely related to (2.5) is given, e.g., in [32, (LABEL:phi4-e:DaDbPNn0)], with in (2.5) replaced by the Gaussian superexpectation. As in [32], our formalism applies to the supersymmetric model with only notational changes, with interpreted as in formulas such as (1.19), and with the Gaussian expectation replaced by a superexpectation. For notational simplicity, we concentrate throughout the paper on the case .
2.2 Progressive integration
In our version of the RG method, the expectation of (2.5) is evaluated in a multi-scale fashion, via a finite-range decomposition of the covariance . We use the same finite-range decomposition of the covariance that is described and analysed in [31, Section LABEL:alpha-sec:frd]. A closely related decomposition was first introduced in [25] and subsequently developed in [26]. The covariances are translation invariant, and have the finite-range property
[TABLE]
Thus, we may regard either as a covariance on or on , as long as . Viewing the as covariances on , we also have a decomposition of the infinite-volume covariance given by . We leave implicit the dependence of the covariance on . According to [31, (LABEL:alpha-e:scaling-estimate)], for bounded, the covariances satisfy the estimates
[TABLE]
For , and for an integrable , we define the convolution by
[TABLE]
where the expectation on the right-hand side acts on and leaves fixed. A similar construction is used for (see, e.g., [6, Section LABEL:log-sec:ga]). By [13, Proposition LABEL:norm-prop:conv], the Gaussian convolution can be evaluated as
[TABLE]
with an abuse of notation where means . To compute the expectation in (2.5), we use (2.10) to evaluate progressively, as follows. We write and let
[TABLE]
with as in (2.3). By (2.10), is obtained by setting in
[TABLE]
This leads us to study the recursion .
2.3 Function space
The observable fields are needed only for the purpose of evaluating the second derivative in (2.5). Therefore, dependence on the observable fields which is higher order than quadratic plays no role. We make use of this by defining the function space as explained below. We also define the seminorm on . These definitions are as in, e.g., [14, 32]. We focus on the case ; the modifications needed for are as in, e.g., [15].
2.3.1 The space
Given , let
[TABLE]
As in [31, Section LABEL:alpha-sec:tfnorm], we fix any . For , is instead a space of even differential forms with -times differentiable coefficients.
In order to treat functions of the observable fields , we define an extension of exactly as in [32, Section LABEL:phi4-sec:phi4observables_representation]. Namely, let be the space of real-valued functions of which are in and in . An ideal in is formed by those elements of whose formal power series expansion in the observable fields to order is equal to zero. We define as the quotient algebra . Then has a direct sum decomposition
[TABLE]
where elements of are given by elements of multiplied by , by , and by respectively. Thus, elements of can be identified with polynomials over in the observable fields with terms only of order , i.e. can be written as
[TABLE]
with for each . There are natural projections defined for such by , , , and . We set . The expectation acts term-by-term on , namely for as in (2.15).
2.3.2 Seminorms
A family of seminorms is used to control the size of elements of . Let denote the set of sequences of any finite length (including length [math]), composed of elements of . Let be a field, and let . Given , we write and let
[TABLE]
A test function is a mapping , written . We define the -pairing of with a test function by
[TABLE]
Given a parameter , a scale-dependent norm is defined on test functions in [31, (LABEL:alpha-e:Phinorm)]. The norm controls the size of a test function and its discrete gradients up to order , but its precise definition is immaterial for the present discussion. With the unit ball in , we define the seminorm on by
[TABLE]
Given an additional parameter , we extend this definition to all of exactly as in [14], i.e., the seminorm of of the form (2.15) is defined to be
[TABLE]
2.4 Blocks, polymers and scales
2.4.1 Blocks and polymers
The finite-range covariance decomposition is well-suited to a block decomposition of the torus of period into disjoint blocks of side , for scales . This decomposition is an important ingredient in our choice of the coordinates in which we represent the RG map. We now describe it in detail, along with a number of useful related definitions, as in [16].
We partition the torus , which has period , into disjoint -blocks of side (). Each -block is a translate of the block . We denote the collection of -blocks by .
A -polymer is any (possibly empty) union of -blocks, and denotes the set of -polymers. Given , we denote by the set of -blocks in , and denote by the set of -polymers in . A nonempty polymer is connected if for any , there is a sequence with for . Let denote the set of connected -polymers and, for any , let be the set of connected components of . The empty set is not in .
We say that two polymers do not touch if . We call a connected polymer a small set if it consists of at most -blocks, and write for the collection of small sets in . The small-set neighbourhood of a polymer is .
For , we define the scale- circle product by
[TABLE]
We only consider maps with the property that . The identity element for the circle product is the map defined by , if .
2.4.2 Mass and coalescence scales
Two scales play an important role for the nature of the RG recursion (2.11). We define the mass scale by
[TABLE]
By definition, is the smallest scale for which . The mass scale is the scale beyond which the mass plays a significant helpful role in the decay of the covariance . Indeed, by (2.8) and the elementary inequality (with notation )
[TABLE]
we have
[TABLE]
We also define the coalescence scale by
[TABLE]
By definition, is the unique integer such that
[TABLE]
By (2.7), for all , and hence
[TABLE]
Ultimately, we take the limit before considering large , so we can and do assume that .
2.5 Localisation operator
We use the operator defined and analysed in [14], to extract a local polynomial from an element . For appropriate , the local polynomial extracts the parts of that are relevant and marginal in the RG sense.
2.5.1 Local polynomials
The range of the operator is a certain vector space of local polynomials in the field. We now define this vector space, taking into account that the elements to which will be applied obey Euclidean covariance and invariance on .
Given bulk coupling constants ; ; observable fields ; and the observable coupling constants ; let
[TABLE]
The symbol denotes the bulk. (For , we can take in due to supersymmetry; see [7].) For as in (2.28) and , we write
[TABLE]
Let denote the space of polynomials of the form (2.29). Let be the subspace for which . Note that of (2.3) obeys with .
2.5.2 Definition of Loc
To define , we must first define a set of polynomial test functions, as in [14, Section LABEL:loc-sec:oploc]. Let , let with each , and let with each . Let be a coordinate patch as defined in [14, Section LABEL:loc-sec:oploc] (e.g., can be any small set as defined in Section 2.4). Recall the set of sequences, defined in Section 2.3.2. We define a test function , supported on sequences with each , by
[TABLE]
We include the case by interpreting (2.30) as the constant number in this case. The role of the coordinate patch, which cannot “wrap around” the torus, is to permit polynomial test functions such as (2.30) to be well-defined. We define the field dimension . The dimension of the test function is defined to equal . Given , we let denote the span of all test functions of dimension at most .
Let denote the space of functionals of the field that only depend on field values at points in . By [14, Proposition LABEL:loc-prop:LTsymexists], for , there is a unique operator (independent of the choice of ) such that
[TABLE]
with the pairing given by (2.17) with . To extend from to , suppose we are given for . We let denote the operator with and, for as in (2.15), define
[TABLE]
It remains to specify the .
In [31], for scales below the mass scale the range of the restriction of to is specified as the span of . This corresponds to the choice (using symmetry considerations to disregard non-symmetric monomials). Above the mass scale, a different range for is used, namely , due to the enhanced decay of the covariance decomposition (see [31, Section LABEL:alpha-sec:Loc]).
As in [7, Section LABEL:pt-sec:loc-specs], we set when acts at scales strictly less than , and set for larger scales. We always take .
The following elementary lemma will be useful. Let denote the constant test function supported on sequences of length and defined by . Likewise, let denote the constant test function supported on sequences of length and defined by . Note that are each of the form (2.30), with respective dimensions and .
Lemma 2.1**.**
Given and a coordinate patch , suppose that . For and ,
[TABLE]
Moreover, if , then for ,
[TABLE]
Proof.
The first statement is an immediate consequence of the definition of together with the fact that the test function has dimension for . The second statement follows similarly using the fact that the dimension of is equal to if .
3 RG map
In the absence of observables, i.e., with , the RG map for the long-range models is constructed and bounded in [31] using the main theorem of [16]. The result is given in [31, Theorem LABEL:alpha-thm:step-mr]. The extension of this construction to the case of nonzero observable fields follows a similar route as in the 4-dimensional nearest neighbour case in [5, 32], as we now explain. The coordinates for the RG map are discussed in Section 3.1, the domain of the RG map is discussed in Section 3.2, and the main estimates for the RG map are given in Theorem 3.3. These estimates, combined with a new estimate derived from a cluster expansion, are used in Sections 4–5 to control the flow generated by the RG map.
3.1 RG coordinates
The RG map will be defined so as to express the sequence defined by (2.11) as
[TABLE]
for a real sequence and sequences of maps and . The perturbative coordinate is an explicit function of , and
[TABLE]
The nonperturbative coordinate is discussed in detail below. By (2.3), (3.1) holds at scale with , , and . We sometimes write an element of as with , where encodes .
We express the map of (2.11) via a map , the renormalisation group (RG) map, in such a manner that
[TABLE]
with and . This ensures that has the form (3.1) with .
3.1.1 Perturbative coordinate
The form of the perturbative coordinate is as follows. Given a matrix , we define the operator . Recall the projections defined in Section 2.3.1. Given , we also define
[TABLE]
For , we write the partial sums of the covariance decomposition as
[TABLE]
As in [7, (LABEL:pt-e:WLTF)], for we define
[TABLE]
The polynomial in the fields is thus an explicit quadratic function of . In particular, is an even polynomial in the fields, and is quadratic in the coupling constants and is irrelevant in the RG sense. Finally, for , we define by
[TABLE]
As in (2.5), we write . We also write and . We will later make use of the following corollary of Lemma 2.1.
Corollary 3.1**.**
For and , and with ,
[TABLE]
Moreover, if , then
[TABLE]
Proof.
The fact that is immediate since is even in the fields. Also, since , it follows from Lemma 2.1 that . To see that , note that , with the coefficient of in . Thus, by definition (2.17) of the pairing, , which vanishes by Lemma 2.1 when . This completes the proof.
3.1.2 Nonperturbative coordinate
We now define the space of maps which contains the nonperturbative RG coordinate. With replaced by , such a space is defined in [31, Definition LABEL:alpha-def:Kspace], and, as in [16], we extend it here to include observables. The symmetries (Euclidean covariance, gauge invariance, supersymmetry, and -invariance) used in Definition 3.2 are defined in [16, Section LABEL:step-sec:coordinates] and [4, Section LABEL:phi4-log-sec:margrel]. For , as a replacement for the gauge invariance which holds for we also introduce sign invariance, which is invariance under the map . Note that of (2.3) is sign invariant. It can be verified that the property of sign invariance is preserved by the map of [16].
Definition 3.2**.**
For , let denote the real vector space of functions with the following properties:
- •
Field Locality: For all , . Also, (i) unless , (ii) unless , and (iii) unless and or vice versa, and if and .
- •
Symmetry: (i) is Euclidean covariant, (ii) if , is supersymmetric and is gauge invariant and has no constant part; if , is -invariant and is sign invariant.
Let be the real vector space of functions which have the above field locality and symmetry properties, and, in addition:
- •
Component Factorisation: for all polymers , .
The nonperturbative coordinate appearing in (3.1) is an element of . An element of determines an element of by restriction to . Also, an element of determines an element of by the factorisation property. The same symbol is used for both elements related by this correspondence. Since the empty set is not a connected set, becomes under this correspondence.
3.2 Norms and RG domain
We now specify the domain of the RG map, which requires specification of norms on the spaces and . Without the observables fields, the norms are discussed in [31, Section LABEL:alpha-sec:nr]. For the nearest-neighbour 4-dimensional case, the adaptation of the norms to include observables is discussed, e.g., in [32, Section LABEL:phi4-sec:par]. For our current long-range setting, we need only adjust some norm parameters, compared to [32, Section LABEL:phi4-sec:par].
As in [31, (LABEL:alpha-e:sfix)], the small number in Theorem 1.1 is given by
[TABLE]
with the constant specified in [31, Lemma LABEL:alpha-lem:beta-a0] (not to be confused with the point used for the two-point function). Recall the mass scale defined in (2.21). Following [31, (LABEL:alpha-e:alphapdef)–(LABEL:alpha-e:elldefa), (LABEL:alpha-e:hdef)], we fix
[TABLE]
and define the bulk parameters
[TABLE]
Here can be chosen large (depending on ) and is a fixed (small) constant. We use to refer to either of the bulk parameters .
Now that observables are present, the pair of parameters is supplemented by the pair
[TABLE]
We only use for . Recall that we assume that the coalescence scale is smaller than the mass scale , since the limit will be taken before considering arbitrarily large .
For , we define the scale-dependent norm
[TABLE]
We denote the restriction of to by the same symbol. Given , we define the domain
[TABLE]
Note that is a domain in , and as such, does not involve the coupling constants or .
A sequence of Banach spaces is defined in terms of the seminorms in [31] (they are denoted there). We extend to a space whose definition is the same with the exception that the seminorms are defined on the extended space . As in [31, Remark LABEL:alpha-rk:chiL], we define a sequence
[TABLE]
Given a parameter , the domain of the RG map is defined by
[TABLE]
where is the open ball of radius in the Banach space .
3.3 Estimates on RG map
We now specify the RG map and state our bounds on it. To shorten notation, we condense indices and write, e.g., for and for . The definition of the maps is described in a general setup in [7, 16], and is adapted to the long-range model with in [31]. The same definitions extend to include observables.
In particular, the map is explicit and consists of a perturbative part , incorporating second-order perturbation theory, and a nonperturbative, third-order error . The explicit map is the one defined in [7] for , extended in [4] to , and used in [32] for general . Let denote or , and let denote or . We denote the components of the map by . For , and with given by (3.6), let
[TABLE]
By [31, (LABEL:alpha-e:Greeknoprime) and Lemma LABEL:alpha-lem:wlims],
[TABLE]
Let
[TABLE]
Recall the definition of the coalescence scale in (2.24). Then, as in [32, Proposition LABEL:phi4-prop:pt], for general the observable part of the map is the map given by
[TABLE]
Note that for all scales , i.e., the flow of stops evolving after scale . Conversely, since for , nonzero can occur only at scales . The map is now defined by
[TABLE]
The localisation operator is defined in [14, Definition LABEL:loc-def:LTXYsym]. The higher-order correction to the perturbative calculation is then defined by
[TABLE]
so that . We do not need the explicit form of and only use the bounds of Theorem 3.3 below.
The map is also given explicitly in [16], but it is complicated to write down. Like , this nonperturbative part of the RG map is of order . It is part of the statement of Theorem 3.3 below that the formula for constructed in [16] is well-defined on the domain specified in Theorem 3.3. We do not need to know more here about than the estimates provided by Theorem 3.3.
The RG map depends on the mass through its dependence on the covariance . We require continuity in the mass in the limit , which can only be taken after the infinite-volume limit . Given small , we define the mass domain for the RG map by
[TABLE]
The special attention to is due to the fact that the final covariance is only defined for , and it obeys good estimates for .
The following extends [31, Theorem LABEL:alpha-thm:step-mr] to allow for the presence of observables. Its estimates appear identical to [31, Theorem LABEL:alpha-thm:step-mr], but it is in fact an extension since the domain and range of the RG map now include observables in , as well as in and . Note that the map , which acts on with , produces a polynomial in which in particular contains the nonperturbative contributions to . The bound (3.29) on controls these nonperturbative contributions to . Note that the estimates (3.29) hold for , but the continuity is in the smaller interval . A restriction like this on the continuity interval is essential, because larger will put above the mass scale, at which point the spaces themselves become dependent on through their dependence on and a continuity statement becomes meaningless.
Theorem 3.3**.**
Let ; ; and . Let and be sufficiently large, and let be sufficiently small. There exist , , such that, with the domain defined using , the maps
[TABLE]
are analytic in , provide a solution to (3.3), and satisfy the estimates
[TABLE]
The coordinate in corresponding to is identically zero for , and the coordinate corresponding to is identically zero for . In addition, are jointly continuous in .
Proof.
The theorem is a consequence of the main result of [16], which focusses on the 4-dimensional nearest-neighbour case. For the long-range model, the appropriate modifications for the bulk part of the RG map are discussed in [31], and we assume familiarity with both the methodology and the modifications. In order to include observables, only minor further modifications are required, compared to [15, 16].
One requirement is to verify that, for , the basic small parameters and obey appropriate estimates when observables are present, as in [15, Sections LABEL:IE-sec:epVW–LABEL:IE-sec:epdV-app]. We verify this here; this verification validates our choice (3.15) for the norm parameters. (In fact, somewhat larger domains are used in [15, Sections LABEL:IE-sec:epVW–LABEL:IE-sec:epdV-app]; the main ideas are present for , which we consider here, and the extension to the larger domains is a matter of bookkeeping.) A second requirement is to verify that the “crucial contraction” is maintained in the presence of observables, and we also verify this here.
Bound on . Let . For , it suffices to observe that for ,
[TABLE]
which implies stability on the domain of (3.17), and complements the arguments of [16, 31].
Bound on . We must also verify the analogue of [15, Lemma LABEL:IE-lem:epdV]. To state the desired estimate, as in [31, (LABEL:alpha-e:ellhatdef)] we define the norm parameter
[TABLE]
and as in [31, (LABEL:alpha-e:epdVdef)] we define the small parameter
[TABLE]
We write and let . Our goal then is to show that, for ,
[TABLE]
where is an -dependent constant.
It is argued in [31, Section LABEL:alpha-sec:epdV] that (3.33) holds with replaced by . Thus, it suffices to establish (3.33) with replaced by . This can be done by writing
[TABLE]
and applying the triangle inequality to estimate each of the two terms on the right-hand side.
For instance, if , then the term of is . By definition of the norm, by (3.31), (3.15), (3.32), (3.18), and by the fact that ,
[TABLE]
The term of is zero above the coalescence scale, whereas if then it is , by (3.23). Thus, by (3.30), it is sufficient to show that
[TABLE]
By its definition in (3.22),
[TABLE]
By (3.17) and (2.23), and the finite-range property (2.7), the first term is bounded by
[TABLE]
and the second term is bounded by
[TABLE]
These bounds do better than what is required by (3.36).
For the term, we can take . The term of is always [math] and the coefficient of in is at most . By (3.17), (2.23), and (3.13) and the fact that ,
[TABLE]
When (hence ) this is , and when it is . This is better than what is required for (3.33).
Crucial contraction. The adaptation of the crucial contraction to the long-range model is provided for the bulk in [31, Section LABEL:alpha-sec:kappabms–LABEL:alpha-sec:kappapms]. We now extend the adaptation to include observables.
Below the mass scale, the least irrelevant of the sign invariant monomials involving the observable fields each have two additional spin fields compared to their marginal counterparts and (the latter occurs only above the coalescence scale), so have dimension which is larger by . Compared to [31, (LABEL:alpha-e:kappa)], this gives rise to , and there is no factor for observables, so the gain here is proportional to . The worst occurs for , where we have . This is consistent with the values of reported for the bulk in [31, (LABEL:alpha-e:kappa)].
Above the mass scale, we extend the discussion in [31, Section LABEL:alpha-sec:kappapms], as follows. For the perturbative contribution to , we have already verified that we can continue to use the given by (3.32) when observables are present, and there is therefore no change to [31] concerning this issue. It remains to consider the crucial contraction.
We recall and invoke our assumption that . Now , so the least irrelevant monomial in is . This scales as
[TABLE]
A change from scale to scale in the above right-hand side gives rise to a factor . As in [31, (LABEL:alpha-e:kappajm)–(LABEL:alpha-e:kappa-above-jm-5)], the essential condition here is that the product of this factor with should be bounded above by an inverse power of . This condition is indeed satisfied, since
[TABLE]
Similarly, the least irrelevant monomial in that is sign invariant is of the form , and has scaling dimension twice that considered in the previous paragraph, so twice as good. Thus the crucial contraction is not harmed by the presence of observables.
Estimate for above the mass scale. Finally, we consider the extension of [31, Section LABEL:alpha-sec:Rbound] to include observables. The observable terms have the same and norms: and . This leads to an extension to [31, Lemma LABEL:alpha-lem:monnormcomp], as follows. Let
[TABLE]
The estimates of [31, Lemma LABEL:alpha-lem:monnormcomp] now become
[TABLE]
i.e., the bound remains the same for but loses a helpful factor for . The bound on then implies, as in [31, (LABEL:alpha-e:Rnormcomp)], that
[TABLE]
and the bound (3.29) follows from this as in [31, Section LABEL:alpha-sec:Rbound].
The introduction of observables does lead to a change in the bounds on in [31, Lemma LABEL:alpha-lem:W1], due to the weakened estimate for in (3.45). The change is to replace the factors and in the three upper bounds of [31, Lemma LABEL:alpha-lem:W1] by the worse factor . Since we seek an upper bound which includes the factor in , the weakened bounds remain more than good enough.
For general reasons, [15, Proposition LABEL:IE-prop:Wbounds], so there can be no such term in . Thus, in the proof of [31, Lemma LABEL:alpha-lem:W1], only one factor can be lost by application of (3.45), not two. Also, by direct calculation, the relevant contribution to is , whose norm is given as in the first inequality of (3.40) to be at most , which is better than the required . The bound on follows from the bounds on as in [15, Proposition 4.1].
In the absence of observables, Theorem 3.3 is used in [31] to construct a global RG flow that remains in the RG domain for all . This requires tuning the initial to a mass-dependent critical value ; this value converges to the critical point as (see [31, (LABEL:alpha-e:nuceta)–(LABEL:alpha-e:nuc)]). Throughout the remainder of the present paper, we always take to be this global flow of coupling constants. For general reasons this flow is the same in the presence of observables as in their absence: see [16, (LABEL:step-e:piVKplus)–(LABEL:step-e:plusindep)]. The main task for the proof of Theorem 1.1 is to apply the estimates of Theorem 3.3 to control, in addition, the flow of the observable coupling constants and , and the observable part of the coordinate . The flow of and is analysed as in the 4-dimensional nearest-neighbour case [5, 32].
The flow of is marginal, for the same reasons as in the 4-dimensional case. In [5, 32], the perturbative approximation (3.23) to the recursion for is solved along the lines of the rough computation
[TABLE]
In [5, 32], the errors introduced by the map into (3.23) were summable over all scales because of the decay of the marginal coupling constant with the scale (Gaussian fixed point), and the above computation survives the introduction of these errors.
For the long-range model, the fixed point is non-Gaussian, and the corrections due to are not summable. Instead of trying to follow the route laid out in [5, 32], we derive an exact relation between and the known bulk coupling constants, similar to (3.47), which gives better control of its flow than the recursion. This is done in Section 4.
4 Flow of
According to (3.23) and Theorem 3.3, the flow of under the RG map is nontrivial only until scale , and stops beyond this scale. Conversely, for , and the flow of is nontrivial only for scales . Our goal now is to determine the form of the flow until scale . Since we later take the limit before studying large , we can and do assume that . We will prove the following proposition.
Proposition 4.1**.**
Let , let be sufficiently large, let be the torus of period , and let be sufficiently small. Let , and let with sufficiently small. Let . Let , let be the critical value constructed in [31], and let . Then the RG map can be iterated to scale , i.e., it produces a sequence with initial condition , such that (3.1) holds for all with and . Moreover, , and for the component of this flow we have the stronger statement
[TABLE]
The proof of Proposition 4.1 is given in Section 4.1 below. Its statement holds trivially at , and will be established inductively for higher scales. The induction for the bulk quantities is the result of [31], and is unaffected by the presence of observables.
The main additional ingredient for the induction of the observable parts is to establish the flow (4.1) of . To achieve this, in Lemma 4.2 we use integration by parts to obtain a relation between , quantities of the bulk flow, and the observable parts of the coordinate . This is achieved by taking suitable derivatives of the identity . The contribution due to is bounded uniformly in the volume using a cluster expansion, in Section 4.2.
The formula (4.1) for has a natural counterpart for the nearest-neighbour 4-dimensional case, with error term instead of . In that context, as . This provides insight into the fact that in [5, Lemma LABEL:saw4-lem:lamlim] and [32, Corollary LABEL:phi4-cor:vx]. For the long-range model considered in Proposition 4.1, the non-Gaussian fixed point leads to a limit which is not equal to .
4.1 Integration by parts
For notational convenience we restrict attention to ; small modifications apply for . Recall that and are defined above Corollary 3.1, and that . Let
[TABLE]
Then we have
[TABLE]
The existence of the logarithm is discussed in Section 4.2, where it is constructed as an element of a Banach space which only examines derivatives at zero field, using a cluster expansion. Bounds on and its derivatives at zero field are proved in Proposition 4.4 below.
Lemma 4.2**.**
The functions and are related by the identity
[TABLE]
Proof.
By definition, followed by Gaussian integration by parts,
[TABLE]
On the right-hand side, can be replaced by , and the latter commutes with the expectation. Then application of gives
[TABLE]
which by translation invariance and by definition of is the same as
[TABLE]
Now we divide both sides of (4.7) by and use (4.3). Since , and since by symmetry, the result is (4.4).
Note that the right-hand side of (4.4) involves only bulk quantities, while the left-hand side depends on through and , and also on the observable part of the irrelevant coordinate (through ). For the explicit terms, we have the following identities.
Lemma 4.3**.**
For and ,
[TABLE]
and if then
[TABLE]
Proof.
We differentiate the formula , which is (3.8). We apply the product rule, Corollary 3.1, and the facts that and , to obtain
[TABLE]
Similarly, for , we also use to obtain
[TABLE]
and the proof is complete.
We now state our bounds for the terms in (4.4) involving . The hypothesis of Proposition 4.4 will be established inductively.
Proposition 4.4**.**
Let , let be defined as in (4.2), and assume that with and . Then there is a constant such that
[TABLE]
We defer the proof of Proposition 4.4 to Section 4.2.
Proof of Proposition 4.1.
The proof is by induction on . The statement of Proposition 4.1 for is trivial. Without loss of generality, we consider the case . We assume that we have (3.1) for with constructed inductively using the RG map for , and we make the constant in the hypothesis (4.1) explicit by assuming that, with from (4.12),
[TABLE]
Then we have (3.1) with a pair of RG coordinates , satisfying in addition (4.13). Theorem 3.3 guarantees the existence of RG coordinates at scale such that obeys (3.1), with , and bounds on and as in (3.29).
It has been proved in [31] that the bulk part of lies in . The second bound in (3.29) is sufficient to guarantee that also lies in . Therefore, to complete the proof that , we only need to show that , where is the constant in (3.17). By (3.23), and by the first bound of (3.29) together with the definition of the norm in (3.16), we have . It now follows immediately from (4.13) that , since can be chosen small enough. This proves that .
To complete the induction, we must prove (4.13) with replaced by . Since (4.1) is only required for scales below the coalescence scale, we may assume here that . The bounds of Proposition 4.4 at scale can be applied, since the hypothesis has now been verified. Also, the hypothesis of Lemma 4.3 is satisfied. We use Lemma 4.3 in conjunction with (4.4), and apply Proposition 4.4. This gives
[TABLE]
by (3.21) and by taking sufficiently small. This advances (4.13) to scale , and completes the proof.
4.2 Cluster expansion
In this section, we use a cluster expansion to construct a formula for and prove Proposition 4.4. Let . By (3.1), (4.2), and by definition of the circle product,
[TABLE]
where the term in the sum with is interpreted as . In the sum, we decompose into its connected components , which may be labelled in different ways. For , we set if and touch, and otherwise set . Using the component factorisation property of , we obtain
[TABLE]
where the term is again interpreted as . This has the form of the partition function of a polymer system, as defined, e.g., in [34, (1)]. It is a standard result, e.g., [34, 19], that can be written as a cluster expansion and accurately bounded, provided the polymer activities obey suitable estimates. In the following proof, we discuss this in detail and invoke a convergence criterion from [34]; see also [21, 30] for pedagogical introductions to the cluster expansion. The verification of the criterion from [34] is an almost immediate consequence of the norm estimates in the definition of the domain .
Since we are interested only in the derivatives of at zero external and observable fields, we do not construct as a function of these fields (even though this would also be possible for suitably small fields), but rather as a Taylor polynomial (jet) of order in the fields around zero. In other words, we work on the quotient of by the ideal of elements of with . On this quotient, the seminorm becomes a norm, and the quotient becomes a finite-dimensional Banach algebra. This is discussed in detail in [16, Section LABEL:step-sec:Knorms, Appendix LABEL:step-sec:Banach]. We adopt the point of view in the following that we work in this normed space, and write simply for . Although the results of [34] are stated for complex-valued , the proofs hold verbatim for values in any Banach algebra. The completeness of the Banach algebra is important for the existence of , which is defined in terms of an infinite sum.
The estimates we use, for and , are:
[TABLE]
where is small; here, denotes the number of -blocks in . The bound on is a small adaptation of [15, Proposition LABEL:IE-prop:Istab] to our long-range setting, and the bound on follows from the definition of the norm and (3.19). Absorbing the factor by replacing by , replacing by , and using the fact that is sufficiently small, we conclude that the polymer activity obeys the bound
[TABLE]
The following lemma uses this bound and will be employed to verify the hypothesis of [34, Theorem 1].
Lemma 4.5**.**
If , then for sufficiently small (depending only on )
[TABLE]
where the constant depends on .
Proof.
The number of connected polymers that touch a block and have size is at most for some -dependent constant . Thus,
[TABLE]
We split the sum on the right-hand side into sums with and . The first of these is a constant that depends only on . Taking small so that , the second sum is bounded as
[TABLE]
with a -dependent constant, which suffices.
Proof of Proposition 4.4.
By Lemma 4.5, if is sufficiently small, then
[TABLE]
which verifies the hypothesis [34, (3)] with . Also by Lemma 4.5,
[TABLE]
which verifies the other hypothesis of [34, Theorem 1].
Let denote the Ursell function, defined in [34, (2)] (with written as ). We conclude from [34, Theorem 1] that is given by the absolutely convergent sum
[TABLE]
and that for all we have
[TABLE]
with the term in (4.25) interpreted as .
By (4.24)–(4.25) (the factor in (4.25) is not needed) and (4.23),
[TABLE]
Similarly, for , we use the product rule for differentiation, this time using the factor (due to the product rule) in (4.25). With the definition of the seminorm in (2.19) and of in (3.15) for , we obtain
[TABLE]
For , and , with the test functions of Lemma 2.1. These two test functions have -norms (as defined, e.g., in [31, (LABEL:alpha-e:Phinorm)])
[TABLE]
Therefore, for ,
[TABLE]
In particular, since ,
[TABLE]
and the proof is complete.
5 Full RG flow and proof of Theorem 1.1
In Proposition 4.1, the RG flow is constructed for scales . The sequence of (3.2) contains in particular the coupling constants ; recall that for . In Section 5.1, we apply Theorem 3.3 inductively to continue the RG flow to scales . Using the extended flow, we prove Theorem 1.1 in Section 5.2. The analysis proceeds as in [5, 32].
Once the RG flow has been extended to all scales, the combination of (2.12) and (3.1) gives, at the final scale , the representation
[TABLE]
From this, we apply (2.5) to to calculate the two-point function as
[TABLE]
with
[TABLE]
5.1 Flow of
The next proposition states that the RG flow exists for scales , and in particular analyses the flow of and establishes control on the terms of the right-hand side of (5.2), which is needed to prove Theorem 1.1.
Proposition 5.1**.**
Let , let be sufficiently large, let be the torus of period , and let be sufficiently small. Let , and let with sufficiently small. Suppose that . Starting with produced by Proposition 4.1, the RG map can be iterated to scale , i.e., it produces a sequence such that (3.1) holds for all with and . The component of is given by
[TABLE]
with
[TABLE]
Moreover,
[TABLE]
Proof.
For , we have by Proposition 4.1. Also, (5.4)–(5.5) hold trivially, since by Theorem 3.3 and hence by (3.24).
We fix and assume inductively that (3.1) holds with a pair of RG coordinates and that (5.4)–(5.5) hold. As in the proof of Proposition 4.1, Theorem 3.3 guarantees the existence of RG coordinates at scale , with , and bounds on and as in (3.29).
As before, it has been proved in [31] that the bulk part of lies in . The coordinate remains constant for , and thus still lies in . As before, the second bound in (3.29) is sufficient to guarantee that also lies in . This shows that .
We now show that satisfies (5.4) at scale and that (5.5) holds. Using (5.4) and (3.24), and denoting by the component of corresponding to the component , we see that
[TABLE]
verifying (5.4) at scale . By definition of the norm in (3.16) and by our assumption that , Theorem 3.3 gives the bound
[TABLE]
which proves (5.5) since by (2.25).
Finally, we write to mean no derivative for , the derivative with respect to or for , and the second derivative with respect to for . Since , it follows from (3.29), with the fact that the norm bounds the norm, that
[TABLE]
Since as , this implies (5.6) and completes the proof.
5.2 Proof of Theorem 1.1
With (5.2) and Proposition 5.1, it is now straightforward to complete the proof of our main result. In the proof, we write for the massless free two-point function on . According to (1.6), . The proof uses the following lemma.
Lemma 5.2**.**
Let . Then for any the map is continuous for . Moreover, the sequence is independent of . In particular, for any , the maps depend continuously on at .
Proof.
We show by induction that depends continuously on . The case follows from [31, (LABEL:alpha-e:nu0)] and [31, Corollary LABEL:alpha-cor:mu0]. Now suppose the inductive hypothesis holds for some . Then the case follows from (3.23), [31, Lemma LABEL:alpha-lem:wlims], and Theorem 3.3. The fact that is independent of is [16, Proposition LABEL:step-prop:VZd].
Proof of Theorem 1.1.
We first take the limit , then take the limit , and finally consider large . By (5.4) with ,
[TABLE]
By Proposition 5.1, the remainder term is bounded uniformly in and in by
[TABLE]
By dominated convergence, and by the continuity of (a component of ) at guaranteed by Lemma 5.2, exists and is bounded by . For the main term, since by Proposition 4.1, it follows from the definition of in (3.6) (together with the fact that that the covariance appearing in (3.24) is always the infinite-volume one), that
[TABLE]
The existence of the above limit as is a consequence of the fact that , together with the mass continuity of , which follows from Lemma 5.2. We apply (5.6) in (5.2), and find that the critical two-point function obeys
[TABLE]
This completes the proof.
Acknowledgements
This work was supported in part by NSERC of Canada. We thank Slava Rychkov for helpful correspondence, and an anonymous referee for useful suggestions.
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