\documentstyle[multicol,aps]{revtex}
\begin{document}
\title{Scale Invariance and Lack of Self-Averaging in Fragmentation}
\author{P.~L.~Krapivsky$^\ast$, I.~Grosse$^{\ast\dag}$, and
    E.~Ben-Naim$^\ddag$} 
\address{$^\ast$Center for Polymer Studies and   
    Department of Physics, Boston University, Boston, MA 02215}
\address{$^\dag$Institute for Molecular Biology and Biochemistry, Free
    University Berlin, Arnimallee 22, 14195 Berlin, Germany}
\address{$^\ddag$Theoretical Division and Center for Nonlinear Studies, Los
    Alamos National Laboratory, Los Alamos, NM 87545} 
\maketitle
\begin{abstract} 
  
  We derive exact statistical properties of a recursive fragmentation
  process. We show that introducing a fragmentation probability
  $0<p<1$ leads to a purely algebraic size distribution, $P(x)\propto
  x^{-2p}$, in one dimension.  In $d$ dimensions, the volume
  distribution diverges algebraically in the small fragment limit,
  $P(V)\sim V^{-\gamma}$, with $\gamma=2p^{1/d}$. Hence, the entire
  range of exponents allowed by mass conservation is realized.  We
  demonstrate that this fragmentation process is non-self-averaging as
  the moments $Y_\alpha=\sum_i x_i^{\alpha}$ exhibit significant
  sample to sample fluctuations.

\smallskip\noindent{PACS numbers: 05.40.+j, 64.60.Ak, 62.20.Mk}
\end{abstract}
\begin{multicols}{2}
  
  Numerous physical phenomena are characterized by a set of variables,
  say $\{x_j\}$, which evolve according to a random process, and are
  subject to the conservation law $\sum_j x_j={\rm const}$.  An
  important example is fragmentation, with applications ranging from
  geology\cite{tur} and fracture\cite{solid} to the breakup of liquid
  droplets\cite{liq} and atomic nuclei\cite{nuc,red}.  Other examples
  include spin glasses\cite{sg}, where $x_j$ represents the
  equilibrium probability of finding the system in the $j^{\rm th}$
  valley, genetic populations, where $x_j$ is the frequency of the
  $j^{\rm th}$ allele\cite{h,djm}, and random Boolean
  networks\cite{kauf,flyv}.
  
  In most cases, stochasticity governs both the way the fragments are
  produced and the number of fragmentation events they experience.
  For example, in fragmentation of solid objects due to impact with a
  hard surface fragments may bounce several times before coming to a
  rest \cite{fragm}. The typical number of fragmentation events may
  vary greatly depending on the initial kinetic energy. Another
  seemingly unrelated example is provided by DNA segmentation
  algorithms \cite{alg}, where homogeneous subsequences are produced
  recursively from an inhomogeneous sequence until a predefined
  homogeneity level is reached.  Here, the number of segmentation
  events is determined by the degree of homogeneity of the original
  sequence.
  
  In this study, we examine a fragmentation process with two types of
  fragments: stable fragments which do not break anymore and unstable
  fragments.  We show that the size distribution is algebraic, and
  that the entire range of power-laws allowed by the underlying
  conservation law can be realized by tuning the fragmentation
  probability.  Additionally, these fragmentation processes are
  characterized by large sample to sample fluctuations, as seen from
  analysis of the moments of the fragment size distribution.
  
  Specifically, we consider the following {\em recursive fragmentation
    process}.  We start with the unit interval and choose a break
  point $l$ in $[0,1]$ with a uniform probability density. Then, with
  probability $p$, the interval is divided into two fragments of
  lengths $l$ and $1-l$, while with probability $q=1-p$, the interval
  becomes ``frozen'' and never fragmented again. If the interval is
  fragmented, we recursively apply the above fragmentation procedure
  to both of the resulting fragments.
  
  First, let us examine the average total number of fragments, $N$.
  With probability $q$ a single fragment is produced, and with
  probability $p$ the process is repeated with two fragments.  Hence
  $N=q+2pN$, yielding
\begin{equation}
\label{number}
N=\cases{q/(1-2p),  & if $p<1/2$;\cr
         \infty,    & if $p\geq 1/2$.\cr}
\end{equation}
The average total number of fragments becomes infinite at the critical
point $p_c=1/2$, reflecting the critical nature of the underlying
branching process\cite{Harris}.

Next, we study $P(x)$, the density of fragments of length $x$.  The
recursive nature of the process can be used to obtain the fragment
length density
\begin{equation}
\label{pxeq}
P(x)=q\delta(x-1)+2p\int_x^1 {dy\over y}\,P\left({x\over y}\right).
\end{equation}
The second term indicates that a fragment can be created only from a
larger fragment, and the $y^{-1}$ kernel reflects the uniform
fragmentation density. Eq.~(\ref{pxeq}) can be solved by introducing
the Mellin transform
\begin{equation}
\label{mellin}
M(s) = \int dx\, x^{s-1} P(x).
\end{equation}
Eqs.~(\ref{pxeq}) and (\ref{mellin}) yield \hbox{$M(s)=q+2ps^{-1}M(s)$}
and as a result
\begin{equation}
\label{ms}
M(s)=q\left[1+{2p\over s-2p}\right].
\end{equation}
The average total number of fragments $M(1) = N$ is consistent with
Eq.~(\ref{number}), and the total fragment length $M(2)=1$ is conserved
in accord with $1=\int dx\, x P(x)$.  (Here and in the following the
integration is carried over the unit interval, i.e., $0<x<1$.)  The
inverse Mellin transform of Eq.~(\ref{ms}) gives
\begin{equation}
\label{px}
P(x)=q\left[\delta(x-1)+2p\,x^{-2p}\right].
\end{equation}
Apart from the obvious $\delta$-function, the length density is a purely
algebraic function. In particular, the fragment distribution diverges
algebraically in the limit of small fragments. Given such an algebraic
divergence near the origin, $P(x)\sim x^{-\gamma}$, length conservation
restricts the exponent range to $\gamma<2$. In our case $\gamma=2p$, and
since $0<p<1$, the entire range of acceptable exponents emerges by
tuning the only control parameter $p$.

Interestingly, there is no analytic change in the fragment length
distribution as the critical point $p_c={1\over 2}$ is passed. Yet,
the fragment length distribution becomes independent of the initial
interval length at the critical point. Starting from an interval of
length $L$, Eq.~(\ref{px}) can be generalized to yield
\begin{equation}
P(x)=q\delta(x-L)+2pq L^{2p-1}x^{-2p}.
\end{equation}
Thus, the critical point may be detected by observing the point at which
the segment distribution becomes independent of the original interval
length.
 
The recursive fragmentation process can be generalized to $d$
dimensions.  For instance, in two dimensions we start with the unit
square, choose a point $(x_1, x_2)$ with a uniform probability
density, and divide, with probability $p$, the original square into
four rectangles of sizes $x_1 \times x_2$, $x_1 \times (1-x_2)$,
$(1-x_1) \times x_2$, and $(1-x_1) \times (1-x_2)$.  With probability
$q$, the square becomes frozen and we never again attempt to fragment
it. The process is repeated recursively whenever a new fragment is
produced.

Let $P({\bf x})$, ${\bf x}\equiv (x_1,\ldots,x_d)$, be the probability
density of fragments of size $x_1\times \cdots \times x_d$. This
quantity satisfies 
\begin{equation}
\label{pxdeq}
P({\bf x})= q\delta({\bf x}-{\bf 1})
 + 2^dp \int {d{\bf y}\over y_1\cdots y_d}
P\left({x_1\over y_1},\ldots,{x_d\over y_d}\right)\!,\!\!
\end{equation}
with $\int d{\bf y}=\int dy_1\cdots \int dy_d$. Following the steps
leading to Eq.~(\ref{ms}), we find that the $d$-dimensional Mellin
transform, defined by \hbox{$M({\bf s})= \int d{\bf x}\,
  x_1^{s_1-1}\cdots x_d^{s_d-1} P({\bf x})$} with the shorthand notation
\hbox{${\bf s}\equiv (s_1,\ldots,s_d)$} obeys
\begin{equation}
\label{msd}
M({\bf s})=q\left[1+{\gamma^d\over s_1\cdots s_d-\gamma^d}\right], 
{\quad} {\rm with} {\quad} \gamma=2 p^{1/d}.
\end{equation}
Eq.~(\ref{msd}) gives the total average number of fragments, $N=M({\bf
  1})=q/(1-2^dp)$ if $p<2^{-d}$ and $N=\infty$ if $p \geq 2^{-d}$.  One
can also verify that the total volume $M({\bf 2})=1$ is conserved.
Interestingly, there is an additional infinite set of conserved
quantities: all moments whose indices belong to the hyper-surface
$s_1^*\cdots s_d^*=2^d$ satisfy $M({\bf s}^*)=1$. In a continuous time
formulation of this process the same moments were found to be integrals
of motion \cite{kb,rh,btv}.  The existence of an infinite number of
conservation laws is surprising, because only volume conservation
has a clear physical justification.

Next, we study the volume density $P(V)$ defined by
\begin{equation}
P(V)=\int d{\bf x}\, P({\bf x})\,\delta\left(V-x_1\cdots x_d\right).  
\end{equation}
The Mellin transform, \hbox{$M(s) = \int dV V^{s-1} P(V)$}, can be
obtained from Eq.~(\ref{msd}) by setting $s_i=s$:
\begin{equation}
M(s)=q\left[1+{\gamma^d \over s^d-\gamma^d}\right].
\end{equation}
Using the $d^{\rm th}$ root of unity, $\zeta = e^{2\pi i/d}$, and the
identity $(s^d-1)^{-1}=d^{-1}\sum_{0\leq k\leq d-1}\zeta^k/(s-\zeta^k)$,
$M(s)$ can be expressed as a sum over simple poles at $\gamma\zeta^k$.
Consequently, the inverse Mellin transform is given by a linear
combination of $d$ power laws:
\begin{equation}
\label{pv}
P(V)=q\left[\delta(V-1)+
{\gamma \over d}\sum_{k=0}^{d-1} \zeta^kV^{-\gamma\zeta^k}\right].
\end{equation}
One can verify that this expression equals its complex conjugate, and
hence, it is real.  Additionally, the one-dimensional case (\ref{px}) is
recovered by setting $d=1$.
 
The small-volume tail of the distribution can be obtained by noting that
the sum in Eq.~(\ref{pv}) is dominated by the first term in the series,
which leads to
\begin{equation}
P(V)\simeq {\gamma q\over d}\, V^{-\gamma} \qquad {\mbox{as}} 
\qquad V\to 0.
\end{equation}
Although the value of the exponent $\gamma=2 p^{1/d}$ changes with the
dimension $d$, the possible range of exponents for this process remains the
same since $0<2p^{1/d}<2$ when $0<p<1$. In the infinite dimension limit,
the volume density becomes universal: $P(V)\sim V^{-2}$.

The leading behavior of $P(V)$ in the large size limit can be derived by
using the Taylor expansion and the identity $\sum_{k=0}^{d-1}
\zeta^{kn}=\delta_{n,0}$ for $n=0,\ldots,d-1$. One finds that in higher
dimensions the volume distribution vanishes algebraically near its
maximum value,
\begin{equation}
P(V)\simeq {\gamma^d\over (d-1)!}\,  (1-V)^{d-1} 
\qquad {\mbox{as}} \qquad V\to 1.
\end{equation} 

In fact, the entire multivariate fragment length density can be also obtained
explicitly. This can be achieved by expanding the geometric series,
\begin{eqnarray*}
{\gamma^d\over  s_1\cdots s_d-\gamma^d} = \sum_{n\geq 0}
\prod_{i=1}^d \left({\gamma \over s_i}\right)^{n+1},
\end{eqnarray*}
and performing the inverse Mellin transform for each variable
separately.  Using the identity \hbox{$\int dx\,x^{s-1}\left(\ln
    {1\over x}\right)^{n} =n!s^{-n-1}$} gives
\begin{equation}
\label{psd} 
P({\bf x})=q\left[\delta({\bf x}-{\bf 1})+\gamma^d F_d(z)\right],
\end{equation}
with the shorthand notations
\begin{eqnarray}
\label{fd} 
F_d(z)=\sum_{n=0}^\infty \left(z^n\over n!\right)^d 
\qquad {\rm and} \qquad 
z = \gamma \left(\prod_{i=1}^d \ln{1\over x_i}\right)^{1/d}.
\end{eqnarray}
In two dimensions, $F_2(z)=I_0(2 z)$ where $I_0$ is the modified Bessel
function.

The small size behavior of $P({\bf x})$ can be obtained by using the
steepest decent method. The leading tail behavior, \hbox{$F_d(z)\simeq
  (2\pi z)^{1-d\over 2} e^{z d}$} for $z\gg 1$, corresponds to the case
when at least one of the lengths is small, i.~e.~$x_i\ll 1$.  Returning
to the original variables we see that the fragment distribution exhibits
a ``log-stretched-exponential'' behavior
\begin{eqnarray}
P({\bf x})\sim 
\left[\prod_{i=1}^d \ln {1\over x_i}\right]^{-{d-1\over 2d}} 
\!\!\!\!\!
\exp\left[d\gamma\left(\prod_{i=1}^d \ln {1\over x_i}\right)^{1/d}\right].
\end{eqnarray}

The fragment size distribution represents an average over infinitely many
realizations of the fragmentation process, and hence, it does not capture
sample to sample fluctuations.  These fluctuations are important in
non-self-averaging systems, where they do not vanish in the
thermodynamic limit.  Useful quantities for characterizing such
fluctuations are the moments \cite{der,df}
\begin{eqnarray}
\label{defY}
Y_\alpha=\sum_i x_i^\alpha,
\end{eqnarray}
where the sum runs over all fragments.

We are interested in the average values $\langle Y_\alpha \rangle$ and
$\langle Y_\alpha Y_\beta\rangle$.  For integer $\alpha$, $\langle
Y_\alpha \rangle$ is the probability that $\alpha$ points randomly
chosen in the unit interval belong to the same fragment.  The expected
value of $Y_\alpha$ satisfies
\begin{equation} 
\label{Yav}
\langle Y_\alpha \rangle=q+p\langle Y_\alpha \rangle 
\int dy \left[y^\alpha+(1-y)^\alpha\right].
\end{equation}
The first term corresponds to the case where the unit interval is not
fragmented, and the second term describes the situation when at least
one fragmentation event has occurred. Eq.~(\ref{Yav}) gives
\begin{equation}
\label{Yaver}
\langle Y_\alpha \rangle=q\left[1+{2p\over \alpha+1-2p}\right]
\end{equation}
if $\alpha>2p-1$, and $\langle Y_\alpha \rangle=\infty$ if $\alpha\leq
2p-1$.  As expected, Eq.~(\ref{Yaver}) agrees with the moments of $P(x)$
obtained by integrating Eq.~(\ref{px}), $\langle Y_{\alpha}\rangle=\int
dx\, x^{\alpha} P(x)$.

However, higher order averages such as $\langle Y_\alpha Y_\beta\rangle$
do not follow directly from the fragment size density.  For integer $\alpha$
and $\beta$, $\langle Y_\alpha Y_\beta\rangle$ is the probability that,
if $\alpha+\beta$ points are chosen at random, the first $\alpha$ points
all lie on the same fragment, and the last $\beta$ points all lie on
another (possibly the same) fragment.  This quantity satisfies
\begin{eqnarray}
\langle Y_\alpha Y_\beta\rangle&=&q+p\langle Y_\alpha Y_\beta\rangle 
\int dy\left[y^{\alpha+\beta}+(1-y)^{\alpha+\beta}\right]\\
&+&p\langle Y_\alpha \rangle \langle Y_\beta \rangle \int dy
\left[y^\alpha(1-y)^\beta+(1-y)^\alpha y^\beta\right],\nonumber
\end{eqnarray}
which yields
\begin{eqnarray}
\label{yab}
\langle Y_\alpha Y_\beta\rangle&=&q+{2pq\over \alpha+\beta+1-2p}\\
&+&2p\,{\Gamma(\alpha+1)\Gamma(\beta+1)\over \Gamma(\alpha+\beta+1)}\,
{\langle Y_\alpha \rangle \langle Y_\beta\rangle
\over \alpha+\beta+1-2p}\nonumber
\end{eqnarray} 
if $\alpha,\beta,\alpha+\beta>2p-1$, and $\langle Y_\alpha
Y_\beta\rangle =\infty$ otherwise.

Eq.~(\ref{yab}) shows that $\langle Y_\alpha Y_\beta\rangle\neq\langle
Y_\alpha\rangle\langle Y_\beta \rangle$, and in particular, $\langle
Y^2_\alpha \rangle\neq\langle Y_\alpha\rangle^2$. Therefore,
fluctuations in $Y_\alpha$ are significant and the recursive
fragmentation process is non-self-averaging.  Hence, statistical
properties obtained by averaging over all realizations are
insufficient to probe sample to sample fluctuations.  This behavior is
reminiscent of the lack of self-averaging found in fragmentation
processes that exhibit a shattering transition \cite{maslov}.

In principle, higher order averages such as $\langle Y_\alpha^n\rangle$
can be calculated recursively by the procedure outlined above. The
resulting expressions are cumbersome and not terribly illuminating.
Instead, one may study the distribution $Q_{\alpha}(Y)$ which obeys
\begin{eqnarray}
\label{QY}
&&Q_\alpha(Y_\alpha)=q\delta(Y_\alpha-1)\\
&&+p\int dl\int_0^{Y_\alpha} dZ\, 
{1\over l^{\alpha}}\,Q_\alpha\left({Z\over l^{\alpha}}\right)
{1\over (1-l)^{\alpha}}\,
Q_\alpha\left({Y_\alpha-Z\over (1-l)^{\alpha}}\right).\nonumber
\end{eqnarray}
In addition to the recursive nature of the process, we have employed
extensivity, i.e., $\langle Y_\alpha\rangle\propto L^{\alpha}$ in an
interval of length $L$.


Clearly, $Y_0=N$ and $Y_1=1$, and therefore, $Q_1(Y_1)=\delta(Y_1-1)$
and $Q_0(N)$ can also be determined analytically as well.  Generally,
different behaviors emerge for $\alpha>1$ and $\alpha<1$. We
concentrate on the former case where the support of the distribution
$Q_\alpha(Y)$ is the interval [0,1]. The Laplace transform,
$R_\alpha(\lambda)=\int_0^1 dY_\alpha\,e^{-\lambda
  Y_\alpha}Q_\alpha(Y_\alpha)$, obeys
\begin{equation}
\label{Main}
R_\alpha(\lambda)=q\,e^{-\lambda}+p\int_0^1 dl\,
R_\alpha\left[\lambda l^\alpha\right]   
R_\alpha\left[\lambda (1-l)^\alpha\right].  
\end{equation}
The behavior of $Q_\alpha(Y_\alpha)$ in the limit $Y_\alpha\to 0$ is
reflected by the asymptotics of $R_\alpha(\lambda)$ as $\lambda\to
\infty$. Substituting $R_\alpha(\lambda)\sim \exp(-A\lambda^{\beta})$
into both sides of Eq.~(\ref{Main}), evaluating the integral using the
method of steepest decent, and equating the left and right hand sides gives
$\beta=1/\alpha$.  Consequently, we find that the distribution has an
essential singularity near the origin,
\begin{equation}
\label{Pasymp}
Q_\alpha(Y_\alpha) \sim \exp\left(-BY_\alpha^{-{1\over \alpha-1}}\right) 
\qquad {\rm as} \qquad Y_\alpha\to 0.
\end{equation}

Extremal properties can be viewed as an
additional probe of sample to sample fluctuations.  Thus, we consider
the length density of the largest fragment, ${\cal L}(x)$. Clearly,
${\cal L}(x)=P(x)$ for $x\geq 1/2$, i.e.,
\begin{eqnarray}
\label{large}
{\cal L}(x)=q\delta(x-1)+2pq\,x^{-2p}\qquad {\rm for}\quad x\geq 1/2.
\end{eqnarray}
In the complementary case of $x<1/2$, ${\cal L}(x)$ satisfies
\begin{eqnarray}
\label{L} 
{\cal L}(x)&=&2qp^2 {\cal L}_-\left({x\over 1-x}\right)+
2p^2\int\limits_{1-x}^1 {dy\over y}\,{\cal L}\left({x\over y}\right)\\
&+&2p^3\int\limits_{x}^{1-x} {dy\over y}\,
{\cal L}\left({x\over y}\right){\cal L}_-\left({x\over 1-y}\right),\nonumber 
\end{eqnarray}
where ${\cal L}_-(u)=\int_0^u dv\,{\cal L}(v)$.  The first term on the
right-hand side of Eq.~(\ref{L}) describes the situation when the unit
interval was fragmented into two intervals of lengths $x$ and $1-x$,
with stable smaller fragment and unstable longer fragment (hence the
factor $qp^2$).  The latter ${\cal L}_-$ factor guarantees that
subsequent fragmentation of the unstable interval does not lead to a
fragment longer than $x$.  If one of the 1st generation fragments is
shorter that $x$, then only the longest 1st generation fragment
contributes; this leads to the second term on the right-hand side of
Eq.~(\ref{L}).  The next term describes the situation when both 1st
generation fragments are longer than $x$, so the longest fragment can
arise out of breaking any of the two fragments.  The factor ${\cal L}_-$
guarantees that the longest fragment of length $x$ comes from the
corresponding 1st generation fragment, and the factor $p^3$ guarantees
that both 1st generation fragments remain unstable.  Since ${\cal L}(x)$
obeys different equations in different regions, it looses analyticity on
the boundaries.  Namely, ${\cal L}(x)$ possesses an infinite set of
singularities at $x=1/k$ which become weaker as $k$ increases.  Similar
singularities underly extremal properties of a number of random
processes, including random walks, spin glasses, random maps, and random
trees \cite{h,djm,der,df,lsp}.

For completeness, we note that the above results extend to a
complementary class of fragmentation processes where first
fragmentation occurs, and then fragments remain unstable with
probability $p$.  In this case, the $\delta$ function drops, and the
the size distribution becomes purely algebraic, $P(x)=2qx^{-2p}$.

In summary, we have found that recursive fragmentation  is
scale free, i.e., the fragment length distribution is purely
algebraic.  In higher dimensions, the volume distribution is a linear
combination of $d$ power laws, and consequently, an algebraic
divergence characterizes the small-fragment tail of the distribution.
A number of recent impact fragmentation experiments reported algebraic
mass distributions with the corresponding exponents ranging from $1$
to $2$ \cite{fragm}.  It will be interesting to further examine
whether our simplified model is suitable for describing fragmentation
of solid objects. 

We have also found that the recursive fragmentation process exhibits a
number of features that arise in other complex and disordered systems,
such as non-self-averaging behavior and the existence of an infinite
number of singularities in the distribution of the largest fragment.
These features indicate that large sample to sample fluctuations
exist, and that knowledge of first order averages may not be
sufficient for characterizing the system.  Our 1D model is equivalent
to applying the aforementioned DNA segmentation algorithm to a random
sequence. It will be interesting to study self-averaging and extremal
properties of genetic sequences, which are known to have commonalities
with disordered systems. Indeed, if these subtle features are found
for genetic sequences as well, this would suggest that much caution
should be exercised in statistical analysis of DNA.


\smallskip\noindent We thank S.~Redner and O.~Weiss for fruitful 
discussions, and DOE, NSF, ARO, NIH, and DFG for support.


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\end{document}




