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From the previous sections we can already draw the conclusion that, by standard renormal- ization arguments [4, 86, 144], the density will behave as

ρ∼ At−12 , (4.35) in the long time limit, when the dimension d is one, for some amplitude A: The density ρ corresponds to the field ψ, such that under renormalization it scales as ρ = κρ, with the˜ “dimensionless” density ˜ρ, see Eq. (3.112), whereas time scales ast=κ−2_{˜t, see Eq. (3.111).}

To obtain a broader picture, let us also consider particle input J, which gives rise to an additional term R

dxdt J(x, t) ¯ψ(x, t) in the action and is not renormalized (since there exist no relevant one-loop diagrams, cf. Section 4.2). In one dimension it therefore scales asJ =κ3_j,

with the dimensionless particle input j. We expect that in the long time limit the density becomes independent of the initial condition (as discussed in Section 4.3). Moreover, we have just seen that the effective average action tends to a fixed point, in our approximation described by the fixed point potential u?_{. Thus, the density will not depend on the initial} action, in particular it will be independent of the couplingλ. Therefore, it is a function only of the particle input (chosen to be homogeneous in space and time), the initial action and time,

Since the result must be independent of the scale κ, the density is expected to behave as ρ_∼J13 for finite J and as ρ∼t−

1

2 when there is no particle input.

We also remark that the long time behavior of the density may be obtained after noting that, since time scales as t ∼ κ−2_{, the scaling of the coupling λ}

κ = κλ˜τ → κλ˜? may be interpreted as a time dependent couplingλκ ∼t−

1

2. Thus, inserting this into the mean-field result ρ _∼ λ−1_t−1_{, we again obtain ρ ∼ λ}−1

κ t ∼ t− 1

2. In fact, this corresponds to the first approximation in the perturbative approach, where the density is calculated by only summing over tree diagrams (diagrams which contain no loops) [77, 137].

In the following, we turn to the question of how to estimate the amplitudeA. The expectation valueρof the particle density is determined by the extremal principle, Eq. (3.93). Within our approximation, cf. Eq. (3.99), and seeking for a solution that is homogeneous in space, instead of the mean-field equation ∂tρ=−λρ2, the extremal principle yields the kinetic equation

∂tρ=−F(ρ), (4.37) with thenon-equilbrium force F =Fκ→0, where the renormalized non-equilibrium forceFκ is defined by Fκ(ρ) := ∂Uκ( ¯ψ, ρ) ∂ψ¯ _¯ ψ=0 . (4.38)

We also define its dimensionless counterpart fτ(χ) := ∂uτ( ¯χ, χ) ∂χ¯ ¯ χ=0 . (4.39)

Just as the rescaled potentialuτ flows to u?, the renormalization group flow drivesfτ to its fixed point valuef?_{, which according to Eq. (4.31) may be written as}

f?(χ) =X n≥2

1 n!˜g

?(1,n)_χn_{. (4.40)}

The kinetic equation becomes ∂tρ=−lim κ→0κ 3_f τ(κ−1ψ) =−lim κ→0κ 3_f?_(κ−1_{ψ). (4.41)}

The limit must not depend onκ, since, once the reciprocal scale 1/κis much larger than the correlation length, the right hand side of the equation should have converged well. Hence, the fixed point will be of the form

f?_(χ)∼cχ3_{, (4.42)}

when χ is large, for some universal factor c. This implies that the non-equilibrium force F(ρ) =cρ3 _{and we have for the kinetic equation}

∂tρ=−cρ3, (4.43) such that we indeed recover the decay law, Eq. (4.35), with _A= (2c)−1/2_{. We remark that,}

whereas f? _{is analytic, the non-equilibrium force F(ρ) has a singular point ρ = 0. Phase} transitions can be defined by non-analyticities in the free energy. Thus, our system displays a phase transition at vanishing particle density.

In order to derive the factor c, which determines the amplitude in Eq. (4.35) and therefore the long time kinetics, one needs to determinef?_{(χ) for large values ofχ. This in turn affords} a good knowledge of the rescaled effective average potential uτ( ¯χ, χ), whose flow is given by Eq. (4.5) with the microscopic action for coagulation as the initial condition, c.f. Eq. (4.6). Typically, the goal of the numerical calculations is to extract critical exponents, by considering the flow in the region around the fixed point. In this case, to obtain a satisfactory result, it is often sufficient to perform a series expansion of the Wetterich equation to the first few orders in ¯χ and χ and then to consider the flow of the coefficients ˜g(τm,n). For our problem this clearly will not suffice, since the lower order coefficients only describe the behavior of the forcef? _{around the origin but not for largeχ. Therefore, the question arises, if an expansion} of the fields around the origin is a reasonable approach to our problem, or if one should resort to an approach where the functional dependence in χ is retained and go to large values of this field. For instance, similar to the approach in [125], one could consider

uτ( ¯χ, χ) = ¯χu1τ(χ) + ¯ χ2 2 u 2 τ(χ) + ¯ χ3 6 u 3 τ(χ) +. . . (4.44) derive the flow equations foru1

τ, u2τ, u3τ, . . .to some order, and then solve it “directly” by finite difference methods, as demonstrated in [127, 160]. In principle, the amplitude A = (2c)−1

can then be determined by computing the non-equilibrium forcef?_{(χ) =u}1,?_{(χ) at the fixed} point up to large values ofχ and exploiting Eq. (4.50).

However, the special simplifications of our process, as discussed in Section 4.2, are only obvious in the expansion in the fields. For this reason, instead of calculatingf?_{(χ) =u}1,?_(χ) by solving the flow equation foru1

τ numerically, we have exploited these properties to calculate a large number of fixed point coefficientsg?(m,n) _{exactly (yet of course within our truncation,}

Eq. (3.99), and we could thus determine the expansion f?_{(χ) =}P

n≥2g?(1,n)χn for the first

125 coefficients with the aid of a symbolic computation program. The power series has a finite radius of convergence. To be able to determine the amplitude_A= (2c)−1_{, we enhanced}

the result by employing Pad´e extrapolation [152, 161], cf. Fig. 4.3. This method consists in calculating a rational function, with polynomials in the nominator and denominator, whose coefficients are determined by the derivatives around the origin.

For the first 125 coefficients ˜g?(1,n) _{one must obtain very many, i.e. ∼ 125}2_{/2, coefficients}

˜

g?(m,n)_{. Hence, for the concrete calculation we had to put some thought into decreasing the}

complexity of our algorithm to speed up the program. As illustrated in Figure 4.1, one can derive the flow equation for ˜gτ(m,n) step by step. In each step the stationary value ˜g?(m,n) = limτ→−∞g˜(τm,n) is obtained by resolving the ensuing linear equation (4.21). We obtained these equations not from the one-loop diagrams, as we did for the theoretical considerations in Section 4.2 but “directly” from the flow equation (4.5), for the dimensionless potential uτ( ¯χ, χ) = P_m1_!n!g˜τ(m,n)χ¯mχn. In principle, differentiating Eq. (4.5) m times with respect to ¯χand ntimes with respect to χand setting ¯χ=χ= 0, yields the flow equation for ˜g(τm,n). Yet, in practice, this approach turned out infeasible since the expressions become too lengthy with increasingm and n. However, by exploiting the analyticity of the Wetterich equation, the problem of extracting the flow equations can be reduced to polynomial multiplication. Let us rewrite the flow equation (4.5) for the rescaled potential employing the Taylor expansion

0 2 4 6 8 10 12 14 16 0 0.2 0.4 0.6 0.8 1 1.2 1.4

Figure 4.3:Dimensionless non-equilibrium forcef?_{(χ) =∂}

¯

χuτ→−∞( ¯χ= 0, χ) =P∞_n=2n1_!g˜?(1,n)χn at the fixed point. Considering the series expansion of the flow, Eq. (4.5), for the rescaled effective average potentialuτ, we have calculated the expansion off? _{exactly up to order}

125 and plotted the sumP125n=2n1!˜g?(1,n)χn (thick blue line). Evidently, f?(χ) only has a

finite radius of convergence of approximately 1.1 around the origin. To extrapolate beyond the region of convergence, we employed a Pad´e approximant (thin red line), which relies on so called rational functions, i.e. fractions of polynomials [152, 161].

1/√1 +x=P j

−1/2

j

xj_{. A the fixed point}

0 =−(d+ 2)u?( ¯χ, χ) +dχu?(0,1)( ¯χ, χ) + Ved u ?(1,1)_{( ¯χ, χ) + 1} q u?(1,1)_{( ¯χ, χ) + 1}2 −u?(2,0)_{( ¯χ, χ)u}?(0,2)_{( ¯χ, χ)} =−(d+ 2)u?( ¯χ, χ) +dχu?(0,1)( ¯χ, χ) +P( ¯χ, χ)· ∞ X j=0 −1/2 j Q( ¯χ, χ)j, (4.45) with P( ¯χ, χ) = 1 +u?(1,1)( ¯χ, χ), (4.46) and Q( ¯χ, χ) = 2u?(1,1)( ¯χ, χ) +u?(1,1)( ¯χ, χ)2₋u?(2,0)( ¯χ, χ)u?(0,2)( ¯χ, χ). (4.47) Eq. (4.45) holds for a general reaction-diffusion process with one type of particles. It tells us that the expansion in the fields can be carried out, in practice, by polynomial multiplication. In general, the Fast Fourier Transform would provide an efficient algorithm to carry out these multiplications [161].

For our specific case, we have devised the following algorithm: The coefficients ofu?_{( ¯χ, χ) =} P

m,n m1!n!g˜

?(m,n)_χ¯m_χn _{are calculated successively, as illustrated in Fig. 4.1. In every step} of the calculation we not only compute one new coefficient ˜g?(m,n)_{, but we also obtain the}

coefficientqj,(m,n)_{in the expansion of the powers ofQ,Q( ¯χ, χ)}j ₌P

m,nqj(m,n)χ¯mχn, and the coefficient s(m,n) _{in the expansion S( ¯χ, χ) :=} P∞

j=0 −1/2 j Q( ¯χ, χ)j ₌ P m,ns(m,n)χ¯mχn. All other coefficients which are not yet known, need not be summed over and can provisionally be set to zero. Since the lowest order terms of Q( ¯χ, χ) are χ and ¯χχ, we only need to look at powers Q( ¯χ, χ)j _{up to order j = nfor calculation of ˜g}?(m,n)_{. Notice, that in contrast to}

the potentialu?_{( ¯χ, χ), the functionsQ( ¯χ, χ)}j _{and S( ¯χ, χ) include monomials χ}n_{, which lack} the response field ¯χ. Although in the flow equation for ˜gτ(0,n) they cancel, such that ˜gτ(0,n)≡ ˜

g?(0,n) _{= 0, the coefficients q}j(0,n)_{, s}(0,n) _{enter the equations for higher order coefficients}

˜

g?(m,n0) _(n0 _{> n). Therefore, after completing a vertical row in Fig. 4.1 for the coefficients}

˜

g?(n,n)_,g˜?(n−1,n)_{, . . . ,g˜}?(1,n) _{one must compute p}j(0,n) _{for 1 ≤ j ≤ n and s}(0,n) _{before one}

turns to the next vertical row of coefficients ˜g?(n+1,n+1)_,˜g?(n,n+1)_{, . . . ,˜g}?(1,n+1)_.

It was our experience that if we solve the ensuing linear equations (4.30) for ˜g?(m,n) _numer-

ically, the results become unstable quickly. This was the case although we used a stable method for each linear equation [162]. For this reason, the equations were solved exactly with a symbolic computation program. The behavior off(χ) for largeχwas evaluated in a double logarithmic plot, cf. Fig. 4.4. For large values ofχ, the terms in the expansion indeed add up a to power law_∼χ3_{. We find that approximately}

f(χ)_∼4.2χ3 _{⇒ ρ(t)∼0.35t}−1/2_{. (4.48)}

As compared to the perturbative two-loop result _A = 1 2π +

2 ln(8π)−5

8π ≈ 0.217 [77] (with difference=dc−d= 2−1), this is much closer to the exact decay amplitude √1_2π ≈0.399 [163].

Finally, we demonstrate that with our approach one can also derive the behavior of the coag- ulation process with particle input ∅ −→J A directly from Eq. (4.50). The extremal principle,

Eq. (3.93), yields 0 =∂tρ=−cρ2+J. Thus, the density of the stationary state scales as

ρ_∼(cJ)13 , (4.49)

in agreement with the corresponding result in [164].