# Entropic Measure for Localized Energy Configurations: Kinks, Bounces, and Bubbles

**ABSTRACT** We construct a configurational entropy measure in functional space. We apply

it to several nonlinear scalar field models featuring solutions with

spatially-localized energy, including solitons and bounces in one spatial

dimension, and critical bubbles in three spatial dimensions, typical of

first-order phase transitions. Such field models are of widespread interest in

many areas of physics, from high energy and cosmology to condensed matter.

Using a variational approach, we show that the higher the energy of a trial

function that approximates the actual solution, the higher its relative

configurational entropy, defined as the absolute difference between the

configurational entropy of the actual solution and of the trial function.

Furthermore, we show that when different trial functions have degenerate

energies, the configurational entropy can be used to select the best fit to the

actual solution. The configurational entropy relates the dynamical and

informational content of physical models with localized energy configurations.

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**ABSTRACT:**Spatially-bound objects across diverse length and energy scales are characterized by a binding energy. We propose that their spatial structure is mathematically encoded as information in their momentum modes and described by a measure known as configurational entropy (CE). Investigating solitonic Q-balls and stars with a polytropic equation of state $P = K{\rho}^{\gamma}$, we show that objects with large binding energy have low CE, whereas those at the brink of instability (zero binding energy) have near maximal CE. In particular, we use the CE to find the critical charge allowing for classically stable Q-balls and the Chandrasekhar limit for white dwarfs $({\gamma} = 4/3)$ with an accuracy of a few percent.Physics Letters B 07/2013; · 4.57 Impact Factor - SourceAvailable from: Marcelo Gleiser[Show abstract] [Hide abstract]

**ABSTRACT:**We propose a measure of order in the context of nonequilibrium field theory and argue that this measure, which we call relative configurational entropy (RCE), may be used to quantify the emergence of coherent low-entropy configurations, such as time-dependent or time-independent topological and nontopological spatially-extended structures. As an illustration, we investigate the nonequilibrium dynamics of spontaneous symmetry-breaking in three spatial dimensions. In particular, we focus on a model where a real scalar field, prepared initially in a symmetric thermal state, is quenched to a broken-symmetric state. For a certain range of initial temperatures, spatially-localized, long-lived structures known as oscillons emerge in synchrony and remain until the field reaches equilibrium again. We show that the RCE correlates with the number-density of oscillons, thus offering a quantitative measure of the emergence of nonperturbative spatiotemporal patterns that can be generalized to a variety of physical systems.Physical review D: Particles and fields 05/2012; 86(4).

Page 1

Entropic Measure for Localized Energy Configurations: Kinks, Bounces, and Bubbles

Marcelo Gleiser1, ∗and Nikitas Stamatopoulos1, †

1Department of Physics and Astronomy, Dartmouth College, Hanover, NH 03755, USA

(Dated: November 24, 2011)

We construct a configurational entropy measure in functional space.

nonlinear scalar field models featuring solutions with spatially-localized energy, including solitons

and bounces in one spatial dimension, and critical bubbles in three spatial dimensions, typical of

first-order phase transitions. Such field models are of widespread interest in many areas of physics,

from high energy and cosmology to condensed matter. Using a variational approach, we show that

the higher the energy of a trial function that approximates the actual solution, the higher its relative

configurational entropy. Furthermore, we show that when different trial functions have degenerate

energies, the configurational entropy can be used to select the best fit to the actual solution. The

configurational entropy relates the dynamical and informational content of physical models and can

be applied to any nonlinear field model.

We apply it to several

PACS numbers: 11.10.Lm, 03.65.Ge,05.65.+b

INTRODUCTION

Hamilton’s principle of least action states that out of

the infinitely many paths x(t) connecting two fixed points

in time, nature always chooses the one that leaves the ac-

tion S[x] stationary [1]. This path, as is well known, is

the solution to the Euler-Lagrange equation of motion,

obtained from imposing that the action’s first variation

vanishes, δS = 0. As we move from point particles to

continuous systems, say, as described by a scalar field

φ(x,t), Hamilton’s principle will generate the partial dif-

ferential equation (or equations, for vector- or tensor-

valued fields) describing the motion that leaves the ac-

tion functional S[φ] stationary. In this letter, we will

explore the relation between information and dynamics,

proposing an entropic measure that quantifies the infor-

mational content of physical solutions to the equations

of motion and their approximations–the configurational

entropy in functional space. As we will see, this mea-

sure may offer new insight into how spatially-localized

ordered structures such as topological defects emerge in a

widespread class of natural phenomena, from high-energy

physics and cosmology[2] to condensed matter [3].

To illustrate our point, we will investigate several non-

linear scalar field models in one and three spatial di-

mensions that have spatially-localized energy configura-

tions such as solitons and critical bubbles. After defining

the configurational entropy, we will compute it for mod-

els that admit spatially-localized energy solutions such

as kinks, bounces, and critical bubbles. We will then

compute the configurational entropy for several ansatze

that approximate the solutions to the eom with vary-

ing degrees of coarseness. To compare solutions and ap-

proximations, we will define the relative configurational

entropy–a measure of relative ordering in field configu-

ration space–and show that it correlates well with the

energy: the coarser the approximation to the solution,

the higher its energy and its relative configurational en-

tropy. On the basis of these results, we propose that

nature is the ultimate optimizer, not only in extremiz-

ing energy through its myriad motions, but also from an

ordering perspective. We will also show how the configu-

rational entropy can resolve ambivalent situations where

the energies of different trial functions are degenerate,

thus providing a useful criterion to obtain optimized an-

alytical approximations to the solutions of the equations

of motion, as may be needed in different applications.

MATHEMATICAL PRELIMINARIES: DEFINING

THE CONFIGURATIONAL ENTROPY

Since we are interested in spatially-localized structures

(and/or their related energy densities), consider the set

of square-integrable functions f(x) ∈ L2(R) and their

Fourier transforms F(k).Plancherel’s theorem states

that [4]

?∞

Now define the modal fraction f(k),

−∞

|f(x)|2dx =

?∞

−∞

|F(k)|2dk.(1)

f(k) =

|F(k)|2

?|F(k)|2ddk,

(2)

where the integration is over all k where F(k) is defined

and d is the number of spatial dimensions. f(k) mea-

sures the relative weight of a given mode k. For periodic

functions where a Fourier series is defined, f(k) → fn=

|An|2/?|An|2, where An is the coefficient of the n-th

Let us the define the configurational entropy SC[f] as

?

We call the integrand f(k)ln[f(k)] the configura-

tional entropy density.We note that SC[f] is sim-

Fourier mode.

SC[f] = −

f(k)ln[f(k)]ddk.(3)

arXiv:1111.5597v1 [hep-th] 23 Nov 2011

Page 2

2

ilar to the Gibbs entropy of nonequilibrium thermo-

dynamics, although the latter is defined for a statis-

tical ensemble with microstates with probability pi =

exp(−Ei/kBT)/?exp(−Ei/kBT), where Ei is the en-

stant [5]. Note also that the modal fraction has physi-

cal dimensions of m−d, where m is the relevant mass or

energy scale of the system. Thus, when computing the

configurational entropy in physical systems, the entropy

density will be calculated using f(k)ln[mdf(k)]. SC[f]

is dimensionless. For a discrete set of amplitudes, the

configurational entropy is

?

In analogy with Shannon’s information entropy, SS =

−?pilog2pi, which represents an absolute limit on the

[6], we can think of the configurational entropy as provid-

ing the informational content of configurations compat-

ible with the particular constraints of a given physical

system. When all N modes k carry the same weight,

fn = 1/N and the discrete configurational entropy has

a maximum at SC= lnN. If only one mode is present,

SC= 0.

As an example, consider a Gaussian in d dimensions,

f(r) = N exp(−αr2), and its Fourier transform, F(k) =

N exp(−k2/4α)

[2α](d/2)

. Using the definition of Eq. 2,

ergy of the i-th microstate and kB is Boltzmann’s con-

SC[f] = −

fnln(fn).(4)

best possible lossless compression of any communication

f(k) =exp(−k2/2α)

[2πα]d/2

.(5)

Note that the zero mode carries the most weight. Impos-

ing that f(k) ≤ 1 for all modes, we obtain the consistency

condition 2πα ≥ 1. Similar conditions can be imposed

for other functions. Eq. 3 gives

SC(α) =d

2[1 + ln(2πα)].(6)

As we go up in dimensions, SC(α) increases in multiples

of [1 + ln(2πα)]/2. The consistency condition for f(k)

implies in a lower bound for the configurational entropy

of Gaussians, SC≥ d/2. As we shall see, Gaussians that

violate this lower bound don’t seem to play a role on

physically interesting solutions. Defining the dispersion

of a function f(x) as

?∞

we know that for f(x) and F(k) normalized to unity, they

satisfy the minimum uncertainty relation D0(f)D0(F) =

1. We thus expect the configurational entropy of a Gaus-

sian to represent an absolute minimum for spatially-

localized functions f(x) parameterized by the same spa-

tial dispersion with one (or more) maxima for x ∈

(−∞,∞). In this case, we can write, SC[f] ≥ SC(α).

D0(f) =

−∞

x2|f(x)2|dx,(7)

In physical applications where the function f(x) rep-

resents a spatially-confined structure (e.g. a φ4or a sine-

Gordon kink [7]), the parameter α or its equivalent de-

termining the spatial extent of the structure will be re-

stricted by the relevant interactions or constraints. For

example, a free particle of mass m confined to a rigid-wall

box of length 2R has momentum pn= n?π/R. If mod-

eled by a Gaussian with dispersion σ ≡ (2α)−1/2centered

at R, the uncertainty relation gives α ≤ π2/2R2. On

the other hand, σ2= (1/2α) < R2and so, α ≥ 1/2R2.

We thus have, 1/2 ≤ αR2≤ π2/2. Since 2R cannot be

smaller than the reduced Compton wavelength of the par-

ticle, 2R ≥ 2π/m, we get, 1/2π2≤ α/m2≤ 1/2.

FREE FIELD IN A BOX

As an application of the configurational entropy for

periodic boundary conditions, consider a free massive

scalar field in a rigid-wall box of size L.

φ(0,t) = φ(L,t) = 0, we obtain the familiar quanti-

zation of momentum modes, kn = nπ/L, and disper-

sion relation ω2

Further imposing that˙φ(x,0) = 0, the general solu-

tion is φ(x,t) =

ficients An are given in terms of the initial configura-

tion φ0(x) = φ(x,0) as An= (2/L)?L

butions from different normal modes to a given precision

of the series expansion. The configurational entropy of a

general field profile, obtained from Eq. 4, is given by

??(|An|2)(|An|2)

Imposing

n= (nπc/L)2+ m2c2, with n = 1,2....

?Ansinknxcosωnt, where the coef-

0φ0(x)sin[knx]dx.

Thus, the Fourier amplitudes {An} determine the contri-

SC[φ] = −

1

?|An|2ln

(?|An|2)(?|An|2)

?

.(8)

If An= A for n = 1,...,N, then SC[φ] = lnN, and the

configuration entropy is maximal [5]. Also, if only a single

mode is present, SC[φ] = 0: the most ordered state is a

pure state. From an initial configuration with a certain

SC[φ0], the configurational entropy will evolve in time as

the Fourier coefficients An(t) = Ancosωnt change.

CONFIGURATIONAL ENTROPY OF KINKS

Consider a scalar field model in d = 1 with energy

density ρV[φ] = (˙φ)2/2 + (φ?)2/2 + V (φ), where the dot

and the prime denote time and spatial derivative, respec-

tively. (We set c = ? = 1). We are interested in situ-

ations where the Euler-Lagrange equations admit static

solutions with localized energy density. (So, the energy

density, and not necessarily the field, must be square-

integrable.) This is the case, for example, for the kink

solution for the double-well and for the sine-Gordon po-

tentials [7], which we now examine in turn.

Page 3

3

Case 1: Symmetric double-well potential

If V (φ) = (λ/4)(φ2− m2/λ)2, the kink (or antikink)

is the static solution interpolating the two minima of the

potential at φmin= ±(m/√λ) as x → ±∞, and is given

by φk(x) = ±(m/√λ)tanh(mx/√2). The kink’s energy

density is ρ[φk](x) = (m4/2√λ)sech4(mx/√2) and its

energy is Ek=√8/3m3/λ ? 0.9428m3/λ. From the en-

ergy density and its Fourier transform, Eq. 2 gives the

modal fraction

f(k) =35πk2(4α2+ k2)2

2304α7

csch2

?kπ

2α

?

,(9)

where α ≡ m/√2. Using f(k) into Eq. 3, we obtain the

configurational entropy for the φ4kink, SC[φk] = 1.3588.

How does this value compare to other configurations

satisfying the same boundary conditions that approxi-

mate the kink? To check this, we use a variational ap-

proach. As an illustration, consider the function h(x) =

(2/π)tan−1(αx) expressed in units of m/√λ. The energy

E[h] is minimized for αc= [24ζ(3)/π2]1/2m ? 1.7097m.

With this value, the energy is E[h] = 1.0884m3/λ and the

configurational entropy–computed, as for the kink, from

the energy density–is SC[h] = 1.8828 > SC[φk]. Hence,

for this first example, we see that the trial function h(x)

has both larger energy (as it should) and larger entropy

than the kink solution.

We would like to have a more general measure to com-

pare the configurational entropy of trial functions to that

of the solution of the eom which is also insensitive to the

rare cases when f(k) > 1. Writing the modal fraction for

the trial function (or for its energy density, when appro-

priate) as f(k) and the modal fraction for the solution

of the eom (or its energy density) as g(k), we define the

relative configurational entropy

?

SR[f] provides a measure of entropic distance in func-

tional space. In information theory, the Kullback-Leibler

divergence offers a similar measure [8]. In Table I we

show the results for SRfor the trial function h(x) above

and two other approximations to the kink solution. We

define ∆E = [Ea− Ek]/Ek as the relative difference

in energy between the kink solution and the various

ansatze approximating it, where the subscript a refers

to “ansatz”. In all three cases, the coarser the ansatz–in

the sense of having larger energy than the kink–the larger

its relative configurational entropy.

SR[f] = ddkf(k)|ln[f(k)/g(k)]|. (10)

Case 2: sine-Gordon potential

For another example, varying the 1d action with

the sine-Gordon potential, VsG(φ) = [1 − cos(aφ)]/a2,

Ansatzαc[m]

1.1647

∆ESR

4tan−1[exp(αx)]/π − 1

exp[αx] − 1,x < 0

−exp[−αx] + 1,x ≥ 0

2tan−1[αx]/π

1.3 × 10−3

1.55 × 10−2

0.0354

0.95740.2304

1.70970.14470.6956

TABLE I: Comparison of energy and relative configurational

entropy of the φ4kink and several trial functions.

gives the static kink (antikink) solution φsG(x)

(4/a)tan−1exp[±x/√a], with energy density ρsG[φ] =

2VsG(φ). Integrating over space we get EsG = 8/a2=

8m3/λ.We will set a = 1 from now on.

energy density, we obtain the modal fraction f(k) =

(3/8)πk2csch2(kπ/2), and Eq. 3 gives the configurational

entropy for the sine-Gordon kink, SC[φsG] = 1.3029. We

now proceed as before and compare these values to those

for trial functions that approximate φsG(x), computing

the relative configurational entropy SR. Results are dis-

played in Table II:

=

From the

Ansatzαc[m]

0.6087

∆ESR

π tanh[αx] + π

π exp[αx],x < 0

−π exp[−αx] + 2π,x ≥ 0

2tan−1[αx] + π

1.2 × 10−3

8.3 × 10−3

0.0312

0.81730.1580

1.4136 0.10240.4678

TABLE II: Comparison of energy and relative configurational

entropy of the sine-Gordon kink and several trial functions.

Again, we see that as the energy of the approxima-

tion to the solution to the eom increases, so does its rela-

tive configurational entropy. Although we haven’t proved

that this correlation between lower energy and lower rel-

ative configurational entropy holds for all possible trial

functions approximating the solutions, the examples an-

alyzed here make for a compelling case. The configura-

tional entropy offers an efficient measure of the ordering

associated with localized energy configurations. Next, we

extend our approach to bounces and critical bubbles as-

sociated with metastable decay in one and three spatial

dimensions, respectively. We will see that not only the

same correlation holds, but that the relative configura-

tional entropy provides a way to break possible energy

degeneracies between two or more trial functions.

Page 4

4

CONFIGURATIONAL ENTROPY OF BOUNCES

AND BUBBLES

Consider a one-dimensional scalar field model with po-

tential [9]

V (φ) =λ

4φ2(φ − 2φ0)2− αφ0φ3, (11)

where λ and α are positive constants. For α = 0, the

potential is a symmetric double-well as in the φ4kink

but shifted so that its minima are at φ = 0 and φ = 2φ0.

Defining˜φ = φ/φ0, ? = α/λ and dropping the tildes, the

potential can be written as

V (φ) =λφ4

0

4

φ2?φ2− 4φ(1 + ?) + 4?.(12)

As is well known [10], the bounce [φbo(x)] is a static solu-

tion to the equation of motion that vanishes as x → ±∞

and reaches a maximum at x = 0 at the classical turn-

ing point φtp= 2[(1 + ?) − (2? + ?2)1/2]. So, differently

from kinks, here it is more convenient to define the con-

figurational entropy of the field itself, as opposed to that

of the energy density. Also differently from kinks, so-

lutions for different values of ? must be found numeri-

cally. The procedure, however, is the same: first, solu-

tions to the eom and their Fourier transforms are found.

Then, the modal fraction and the configurational en-

tropy SC[φbo] are computed. In order to compare the

bounce solution with trial functions that approximate

it, we compute the relative configurational entropy from

Eq. 10. For example, a useful trial function is the Gaus-

sian f(x) = φtpexp[−α(?)x2], where α(?) is the varia-

tional parameter. Minimizing the energy with respect to

α we find αc(?) = 2√2[φ2

Note that the consistency condition we imposed above

for Gaussians, α > 1/2π, implies that the Gaussian ap-

proximation is only valid for ? > 3.28 × 10−4, not very

stringent. This is consistent with the fact that for very

small asymmetries (thin wall approximation) the Gaus-

sian is not a good trial function for the bounce.

Forasecondtrialfunction

φtpsech[α(?)x].In Fig. 1 we compare the energy (in-

sert top left) and relative configurational entropy as a

function of ? for the two trial functions. Notice that the

energy is degenerate at ?d= 0.46 and that for ? < [>]?d

the Gaussian [sech(x)] has smaller energy. On the other

hand, the Gaussian has lower relative configurational en-

tropy for all ?. At the degenerate point, the variational

approach is ambiguous: energy considerations alone are

insufficient to pick the best trial function there. The de-

generacy is broken by using the relative configurational

entropy, which clearly picks the Gaussian as the closest to

the bounce solution. In the top right insert we plot the

functional distance measure ∆f(?) ≡

for both trial functions as a function of ?.

the Gaussian is clearly a better approximation for the

tp/8−φtp(1+?)√3/3+√2/2)].

we usef(x)=

?dx|f(x) − φbo|

For low ?

bounce, including at ?d. As ? increases, the two trial

functions have comparable values of ∆f(?).

0.00.20.40.60.81.0

?

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

SR(?)

0.00.20.4 0.60.81.0

0.01

0.02

0.03

∆E(?)

0.00.2 0.40.60.81.0

0.2

0.4

0.6

0.8

∆f(?)

FIG. 1: Relative configurational entropy SR(?), energy differ-

ence ∆E(?) and functional distance ∆f(?) for two trial func-

tions approximating the 1d bounce solution. Continuous line

denotes the Gaussian and dashed line the Sech. The energy

difference is degenerate at ?d = 0.46, while the Gaussian al-

ways has lower relative configurational entropy.

We repeat the computations for a 3d scalar field model

with the same potential as in Eq. 11. Now, the eom is

φ??+ 2φ?/r = ∂V/∂φ. The spherically-symmetric critical

bubble solution has φ(r = 0) = φ0, φ?(r = 0) = 0 and

φ(r) = 0 as r → ∞ [11]. As with the 1d case, we use the

field to compute the modal fraction and relative config-

urational entropy as a function of the tilt ?. The results

are shown in Fig. 2. Here, there is no energy degeneracy

and the Gaussian has both lower energy and lower rela-

tive configurational entropy for all ? studied. For ? ? 0.3,

we can’t extremize the energy to find αcsince the critical

bubble has a flatter profile near the origin. Other trial

functions could be used to explore this range without

difficulty.

SUMMARY AND OUTLOOK

We have proposed an entropic measure in field configu-

ration space for nonlinear models with spatially-localized

energy solutions. We computed the relative configura-

tional entropy SRfor several trial functions approximat-

ing solutions to the eom for different models, showing

that higher SR correlates with higher energy. In cases

where there is a degeneracy in the energy of trial func-

tions, SR can be used as a discriminant. In forthcom-

ing papers we will extend our approach to nonequilib-

rium field theory and cosmology. Our measure can be

extended to models including gravity and gauge fields,

and could potentially be used to discriminate between

Page 5

5

0.30.40.50.60.70.80.9 1.0

?

0.0

0.5

1.0

1.5

2.0

2.5

SR(?)

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.2

0.6

1.0

1.4

∆E(?)

FIG. 2: Relative configurational entropy SR(?) and energy

difference ∆E(?) for two trial functions approximating the 3d

critical bubble solution. Continuous line denotes the Gaussian

and dashed line the Sech. The Gaussian has lower relative

configurational entropy and energy for all ? probed.

solutions in the superstring landscape [12]. We hope the

present work will spearhead research in these topics.

MG is supported in part by a National Science Founda-

tion grant PHY-1068027. NS is a Gordon F. Hull Fellow

at Dartmouth College.

∗Electronic address: mgleiser@dartmouth.edu

†Electronic address: nstamato@dartmouth.edu

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[10] S. Coleman, Aspects of Symmetry (Cambridge University

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[12] R. Bousso and J. Polchinski, JHEP 6 (2000) 6; S. Kachru

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