L-1: Difference between revisions

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This model neglects the correlations between the <math> M=2^N </math> configurations and assumes the <math> E_{\alpha} </math> as iid variables.
This model neglects the correlations between the <math> M=2^N </math> configurations and assumes the <math> E_{\alpha} </math> as iid variables.
* Show that the energy distribution is  
* Show that the energy distribution is  
<center><math> p(E_\alpha) =\frac{1}{\sqrt{2 \pi \sigma^2}}e^{-\frac{E_{\alpha}^2}{2 \sigma^2}}</math></center>
<center><math> p(E_\alpha) =\frac{1}{\sqrt{2 \pi \sigma^2}}e^{-\frac{E_{\alpha}^2}{2 \sigma_M^2}}</math></center>
and determine <math>\sigma^2</math>
and determine <math>\sigma_M^2</math>




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===The Gaussian case ===
===The Gaussian case ===
The asymptotic tail of<math>P^<(E)</math> is
The asymptotic tail of<math>P^<(E)</math> is
<center><math>P^<(E)=\int_{-\infty}^E dx p(x)  \sim \frac{\sigma}{\sqrt{2 \pi}|E|}e^{-\frac{E^2}{2 \sigma^2}} \; </math> for  <math>x \to -\infty</math></center>
<center><math>P^<(E)=\int_{-\infty}^E dx p(x)  \sim \frac{\sigma}{\sqrt{2 \pi}|E|}e^{-\frac{E^2}{2 \sigma_M^2}} \; </math> for  <math>x \to -\infty</math></center>
Hence, the typical value of the minimum is
Hence, the typical value of the minimum is
<center><math>E_{\min}^{\text{typ}} = -\sigma \sqrt{2 \log M}+\frac{1}{2}\sqrt{\log(\log M)} +O(1) </math> </center>
<center><math>E_{\min}^{\text{typ}} = -\sigma \sqrt{2 \log M}+\frac{1}{2}\sqrt{\log(\log M)} +O(1) </math> </center>




Let us to be more general and consider tails <math>P^<(E) \sim e^{-\frac{E^\alpha}{2 \sigma^2}} \; </math>. In the spirit of the central limit theorem we are looking   
Let us to be more general and consider tails <math>P^<(E) \sim e^{-\frac{|E|^\alpha}{2 \sigma_M^2}} \; </math>. In the spirit of the central limit theorem we are looking for a scaling form  <math>E_{\min}=a_M + b_M z <math>. The constants <math>a_M, b_M</math> are M-dependent while <math>z</math> is a random variable of order one.
Shows that
* at the leading order <math>a_M= \left[2 \sigma^2_M \log M\right]^{1/alpha}</math>
* <math>b_M= \frac{2 \sigma^2_M}{a_M^{\alpha-1}} </math>
* <math>P(z) = e^{-z} e^{-e^{-z}}</math> which is the Gumbel distribution




 
Depending on the distribution <math>p(E)</math> we have a different dependence of ''M'' of both <math>a_M, y_M </math>. It is convenient to emphasize the ''N'' dependence we  define
<math>\lim_{M\to \infty} (1-\frac{k}{M})^M =\exp(-k)</math>,
  <center><math>  b_M\equiv y_N \propto N^{-\omega}  </math></center>
<center><math>Q_M(\epsilon) \sim \exp\left(-M  P^<(\epsilon)\right)</math> </center>
Note that the  typical fluctuations of the minimum <math> \sim 1/y_N</math>. In particular we can write:  
This relation holds only when <math> \epsilon \approx a_M </math> and one hase to expand around this value.
<center><math> \overline{ \left(E_{\min} -  \overline {E_{\min}}\right)^2 }\propto N^{2\omega}</math></center> We will see that three different scenarios occur depending on the sign of  <math>  \omega  </math>. 
However, a direct Taylor  expansion does not ensures that probabilities remain positive. Hence, we define <math>  P^<(\epsilon)=\exp(-A(\epsilon)) </math> and remark that <math>  A(\epsilon) </math>  is a decreasing function. We propose the following Taylor expansion 
<center><math>  A(\epsilon) =a_M + A'(a_M)(\epsilon -a_M) = a_M - y_N(\epsilon -a_M) </math></center>
Depending on the distribution <math>p(E)</math> we have a different dependence on ''N'' or ''M'' of both <math>a_M, y_N </math>. It is convenient to define
  <center><math>  y_N \propto N^{-\omega}  </math></center>
We will see that three different scenarios occur depending on the sign of  <math>  \omega  </math>. Using this expansion we derive:
* The famous Gumbel distribution:
<center><math>Q_M(\epsilon) \sim \exp\left(-e^{ y_N (\epsilon-a_M)}\right)  </math> </center>
* the  typical fluctuations of the minimum <math> \sim 1/y_N</math>. In particular we can write:  
<center><math> \overline{ \left(E_{\min} -  \overline {E_{\min}}\right)^2 }\propto N^{2\omega}</math></center>
 


===Density of states above the minimum===
===Density of states above the minimum===
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     \overline{n(x)} = M (M-1) \int  dE \; p(E) \left[P^>(E) -  P^>(E+x)  \right] P^>(E)^{M-2}   
     \overline{n(x)} = M (M-1) \int  dE \; p(E) \left[P^>(E) -  P^>(E+x)  \right] P^>(E)^{M-2}   
</math></center>
</math></center>
In the above integral, <math> E </math> is the energy of the minimum. Hence, we can use the Taylor expansion <math> A(E) = a_M -y_N (E -a_M)</math>. In particular we can write
In the above integral, <math> E </math> is the energy of the minimum. Hence, we can scaling form  <math> A(E) = a_M -y_N (E -a_M)</math>. In particular we can write
* <math>  p(E) = \frac{d}{d E} P^<(E)= -A'(E) e^{-A(E)} \sim y_N e^{y_N (E -a_M)} /M</math>
* <math>  p(E) = \frac{d}{d E} P^<(E)= -A'(E) e^{-A(E)} \sim y_N e^{y_N (E -a_M)} /M</math>
* <math> P^>(E) -  P^>(E+x)  = e^{-A(E+x)}-e^{-A(E)}\sim  e^{y_N (E -a_M)} \left(e^{y_N x}-1\right)/M </math>
* <math> P^>(E) -  P^>(E+x)  = e^{-A(E+x)}-e^{-A(E)}\sim  e^{y_N (E -a_M)} \left(e^{y_N x}-1\right)/M </math>
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* For  <math> \omega=0</math> there is for sure  a freezing transition. One important feature of this transition that we will see in the next tutorial is that the condensation does not occur only in the ground state but in a large (but not extensive) number of low energy exctitations.
* For  <math> \omega=0</math> there is for sure  a freezing transition. One important feature of this transition that we will see in the next tutorial is that the condensation does not occur only in the ground state but in a large (but not extensive) number of low energy exctitations.


== Exercise L1-A: the Gaussian case ==
== Exercise L1-A: More on extreme values ==
Specify these results to the Guassian case and find
<math>P^<(E)=\int_{-\infty}^E dx p(x)  \sim \frac{\sigma}{\sqrt{2 \pi}|E|}e^{-\frac{E^2}{2 \sigma^2}} \; </math> for  <math>x \to -\infty</math>
* the typical value of the minimum
%<center><math>a_M = \sigma \sqrt{2 \log M}-\frac{1}{2}\sqrt{\log(\log M)} +O(1) </math> </center>
* The expression <math>  A(\epsilon) =\frac{\epsilon^2}{2\sigma^2} -\frac{\sqrt{2 \pi}}{\sigma} \log|\epsilon|+\ldots </math>
*The expression of the Gumbel distribution for the Gaussian case
<center><math>Q_M(\epsilon) \sim \exp\left(-e^{- \frac{\sqrt{2 \log M}}{\sigma} (\epsilon-a_M)}\right)  </math> </center>


=References=
=References=

Revision as of 18:14, 20 January 2024

Goal: Spin glass trasnsition. From the expeirments with the anomaly on the magnetic susceptibility to order parameter of the transition. We will discuss the arguments linked to extreme value statistics


Spin glass Transition

Experiments

Spin glass behviour was first reported in non-magnetic metals (Cu, Fe, Au,...) doped with a few percent of a magnetic impurities, typically Mn. At low doping, Mn magnetic moments feel the Ruderman–Kittel–Kasuya–Yosida (RKKY) inetraction which has a random sign because of the random location of Mn atoms in the non-magnetic metal. A freezing temparature Tf seprates the high-temperature paramagnetic phase from the low temeprature spin glass phase:

  • Above Tf the susceptibility obeys to the standard Curie law χ(T)1/T.
  • Below Tf, a strong metastability is observed: at the origin of the difference between the field cooled (FC) and the zero field cooled (ZFC) protocols. In zero field cooled ZFC, the susceptibility decays with T. In FC, the susceptibility freezes at Tf: χFC(T<Tf)=χ(Tf)

Edwards Anderson model

The first important theoretical attempt for spin glasses in the Edwards Anderson model. We consider for simplicity the Ising version of this model.

Ising spins takes two values σ=±1 and live on a lattice of N sites i=1,2,,N. The enregy is written as a sum between the nearest neighbours <i,j>:

E=<i,j>Jijσiσj

Edwards and Anderson proposed to study this model for couplings J that are i.i.d. random variables with zero mean. We set π(J) the coupling distribution and we indicate the average over the couplings, called disorder average, with an overline:

JdJJπ(J)=0

We will discuss two distributions:

  • Gaussian couplings: π(J)=exp(J2/2)/2π
  • Coin toss couplings, J=±1, selected with probability 1/2.

Edwards Anderson order parameter

Since J¯=0, the model does not display spatial magnetic order, such as ferro/antiferro order. The idea is to distinguish:

  • a paramagnet that explores configurations with all possible orientations
  • a glass where the orientation are random, but frozen (i.e.immobile).

The glass phase is then characterized by long range correlation in time without any long range correlation in space. The order parameters is

qEA=limtlimN1NiSi(0)Si(t)

In the paramagnetic phase qEA=0, in the glass phase qEA>0. One can wonder is this transition is thermodynamic. For example, the magnetic susceptibility does not diverge at the freezing temperature, but the magnetization is not the order parameter M=iSi=0. Here the order parameter is qEA and it can be proved that its susceptibility is the non-linear susceptibility.

MH=χ+a3H2+a5H4+

Experiments showed that a3,a5 are indeed singular. This means that agreement the existence of a thermodynamic transition at Tf is an experimental fact.

The SK model

Sherrington and Kirkpatrik considered the fully connected version of the model with Gaussian couplings:

E=i,jJij2Nσiσj

At the inverse temperature β, the partion function of the model is

Z=α=12Nzα,withzα=eβEα

Here Eα is the energy associated to the configuration α. This model presents a thermodynamic transition.

Random energy model

The solution of the SK is difficult. To make progress we first study the radnom energy model (REM) introduced by B. Derrida. This model neglects the correlations between the M=2N configurations and assumes the Eα as iid variables.

  • Show that the energy distribution is
p(Eα)=12πσ2eEα22σM2

and determine σM2


We provide different solutions of the Random Energy Model (REM). The first one focus on the statistics of the smallest energies among the ones associated to the M=2N configurations. For this, we need to become familiar with the main results of extreme value statistic of iid variables.

Specify these results to the Guassian case and find

  • The expression A(ϵ)=ϵ22σ22πσlog|ϵ|+
  • The expression of the Gumbel distribution for the Gaussian case
QM(ϵ)exp(e2logMσ(ϵaM))

Extreme value statistics

Consider the M=2N energies: E1,...,EM as iid variables, drawn from the distribution p(E) (Gaussian for the REM). It is useful to use the following notations:

  • P<(E)=Edxp(x), it is the probability to find an energy smaller than E.
  • P>(E)=E+dxp(x)=1P<(E), it is the probability to find an energy larger than E.

We denote

Emin=min(E1,...,EM)

Our goal is to compute the cumulative distribution QM(ϵ)Prob(Emin>ϵ) for large M. To achieve this we need three key relations:

  • The first relation is exact:
QM(ϵ)=(P>(ϵ))M
  • The second relation identifies the typical value of the minimum, namely Emintyp:
P<(Emintyp)=1M

.

  • The third is an approximation valid only if M
QM(ϵ)=eMlog(1P<(ϵ))exp(MP<(ϵ))

The Gaussian case

The asymptotic tail ofP<(E) is

P<(E)=Edxp(x)σ2π|E|eE22σM2 for x

Hence, the typical value of the minimum is

Emintyp=σ2logM+12log(logM)+O(1)


Let us to be more general and consider tails P<(E)e|E|α2σM2. In the spirit of the central limit theorem we are looking for a scaling form Emin=aM+bMz<math>.Theconstants<math>aM,bM are M-dependent while z is a random variable of order one. Shows that

  • at the leading order aM=[2σM2logM]1/alpha
  • bM=2σM2aMα1
  • P(z)=ezeez which is the Gumbel distribution


Depending on the distribution p(E) we have a different dependence of M of both aM,yM. It is convenient to emphasize the N dependence we define

bMyNNω

Note that the typical fluctuations of the minimum 1/yN. In particular we can write:

(EminEmin)2N2ω

We will see that three different scenarios occur depending on the sign of

ω

.

Density of states above the minimum

For a given disorder realization, we compute n(x), the number of configurations above the minimum with an energy smaller than Emin+x. The key relation for this quantity is:

Prob(d(x)=k)=M(M1k)dEp(E)[P>(E)P>(E+x)]kP>(E+x)Mk1

Taking the average n(x)=kkProb(d(x)=k), we derive

n(x)=M(M1)dEp(E)[P>(E)P>(E+x)]P>(E)M2

In the above integral, E is the energy of the minimum. Hence, we can scaling form A(E)=aMyN(EaM). In particular we can write

  • p(E)=ddEP<(E)=A(E)eA(E)yNeyN(EaM)/M
  • P>(E)P>(E+x)=eA(E+x)eA(E)eyN(EaM)(eyNx1)/M
  • P>(E)M2=QM2(E)exp(eyN(EaM))

Calling u=yN(EaM) we obtain

n(x)=(eyNx1)due2ueu=(eyNx1)withyNNω

The Glass phase

In the Glass phase the measure is concentrated in few configurations which has a finite occupation probability while in the paramagnetic phase the occupation probability is 1/M. As a consequence the entropy is extensive in the paramagnetic phase and sub-extensive in the glass phase. It is useful to evaluate the occupation probability of the ground state in the infinite system:

zαminα=1Mzα=11+ααminzα11+0dxeβx(eyNx1)
  • In the high temperature phase, for β<yN, the occupation probability is close to zero, meaning that the ground state is not deep enough to make the system glassy
  • In the low temperature phase, for β>yN, the above integral is finite. Hence, setting β=1/T,Tf=1/yN you can find
zαminα=1Mzα=11+T2TfT

This means that below the freezing temperature, the ground state is occupied with a finite probability as in Bose-Einstein Condensation.

Let us recall yNNω, so that three situations can occur

  • For ω<0, there is no freezing transition as there are too many states just above the minimum. This is the situation of many low-dimensional systems such as the Edwards Anderson model is two dimensions.
  • For ω>0 there are two important features: (i) there is only the glass phase, (ii) the system condensate only in the ground state because the excited states have very high energy. We will see that in real systems (i) is not always the case and that the exponent ω can change with temperature. This situation can be realistic (there is a very deep groud sate), but it is not revolutionary as the following one.
  • For ω=0 there is for sure a freezing transition. One important feature of this transition that we will see in the next tutorial is that the condensation does not occur only in the ground state but in a large (but not extensive) number of low energy exctitations.

Exercise L1-A: More on extreme values

References

  • "Spin glasses: Experimental signatures and salient outcomes", E. Vincent and V. Dupuis, Frustrated Materials and Ferroic Glasses 31, 2018
  • Theory of spin glasses, S. F. Edwards and P. W. Anderson, J. Phys. F: Met. Phys. 5 965, 1975
  • "Spin glass i-vii" P.W. Anderson, Physics Today, 1988