T-9: Difference between revisions

From Disordered Systems Wiki
Jump to navigation Jump to search
No edit summary
 
(56 intermediate revisions by the same user not shown)
Line 1: Line 1:
<!--
<strong>Goal:</strong> the goal of this problem is to determine when the solution of the distributional equations corresponding to localization is unstable, providing an estimate of thee mobility edge on the Bethe lattice.
=== Problem 7.2: localization-delocalization transition on the Bethe lattice ===
<br>
We now focus on the self energies, since the criterion for localization is given in terms of these quantities. In this Problem we will determine for which values of parameters localization is stable, estimating the critical value of disorder where the transition to a delocalised phase occurs.
<strong>Techniques: </strong>  stability analysis, Laplace transforms.
<br>
 
 
== Problems ==
In this Problem we determine for which values of parameters localization is stable, estimating the critical value of disorder where the transition to a delocalised phase occurs. Recall the results of Problem 8: the real and imaginary parts of the local self energy satisfy the self-consistent equations:




<ol>
<math display="block">
<li><em> The “localized" solution. </em> We set <math> z=E+ i \eta </math> and <math> \sigma^{\text{cav}}_{a}(z)= R_a(z) -i \Gamma_a(z)</math>. Show that the cavity equation for the self-energies is equivalent to the following pair of coupled equations:
\Gamma_a= \sum_{b \in \partial a} t_{ab}^2 \frac{\Gamma_b + \eta}{(E- W\, V_b - R_b)^2+ (\Gamma_b +\eta)^2}, \quad \quad R_a =  \sum_{b \in \partial a} t_{ab}^2 \frac{E- W\, V_b - R_b}{(E- W\, V_b - R_b)^2+ (\Gamma_b +\eta)^2}
<center>
<math>
\Gamma_a= \sum_{b \in \partial a} t_{ab}^2 \frac{\Gamma_b + \eta}{(E- \epsilon_b - R_b)^2+ (\Gamma_b +\eta)^2}, \quad \quad R_a =  \sum_{b \in \partial a} t_{ab}^2 \frac{E- \epsilon_b - R_b}{(E- \epsilon_b - R_b)^2+ (\Gamma_b +\eta)^2}
</math>
</math>
</center>
Justify why the solution corresponding to localization, <math> \Gamma_a=0 </math>, is always a solution when <math> \eta \to 0 </math>; moreover, in the localized phase when <math> \eta </math> is finite but small one has <math> \Gamma_a \sim O(\eta) </math>. How can one argue that this solution has to be discarded, i.e. that delocalisation occurs?
</li><br>


These equations admit the solution <math> \Gamma_a=\Gamma_b=0</math> when <math>\eta=0 </math>, which corresponds to localization. We now determine when this solution becomes unstable.
=== Problem 9: an estimate of the mobility edge ===


<li><em> Imaginary approximation and distributional equation. </em> We consider the equations for <math> \Gamma_a </math> and neglect the terms <math> R_b </math> in the denominators, which couple the equations to those for the real parts of the self energies (“imaginary” approximation). Moreover, we assume to be in the localized phase, where <math> \Gamma_a \sim \eta \ll 1 </math>. Finally, we set <math> t_{ab} \equiv t </math> and <math> E=0 </math> for simplicity. Show that under these assumptions the probability density for the imaginary part, <math> P_\Gamma(\Gamma)</math>, satisfies
<ol>
<center>
 
<math>
<li><em> Imaginary approximation and distributional equation. </em> We consider the equations for <math> \Gamma_a </math> and neglect the terms <math> R_b </math> in the denominators, which couple the equations to those for the real parts of the self energies (“imaginary” approximation). Moreover, we assume to be in the localized phase, where <math> \Gamma_a \sim \eta \ll 1 </math>. Finally, we set <math> t_{ab} \equiv t </math> and <math> E=0 </math> for simplicity. Show that under these assumptions the probability density for the imaginary part, <math> P_\Gamma(\Gamma)</math>, satisfies for <math> \tau=t/W</math>
P_\Gamma(\Gamma)= \int \prod_{b=1}^k d\epsilon_b\,p(\epsilon_b)\int  \prod_{c=1}^k d\Gamma_b \, P_\Gamma(\Gamma_b) \delta \left(\Gamma - t^2 \sum_{b \in \partial a} \frac{\Gamma_b + \eta}{ \epsilon_b^2}  \right)  
<math display="block">
P_\Gamma(\Gamma)= \int \prod_{b=1}^k dV_b\,p(V_b)\int  \prod_{b=1}^k d\Gamma_b \, P_\Gamma(\Gamma_b) \delta \left(\Gamma - \tau^2 \sum_{b \in \partial a} \frac{\Gamma_b + \eta}{ V_b^2}  \right)  
</math>
</math>
</center>
Show that the Laplace transform of this distribution, <math> \Phi(s)=\int_0^\infty d\Gamma e^{-s \Gamma} P_\Gamma(\Gamma) </math>, satisfies
Show that the Laplace transform of this distribution, <math> \Phi(s)=\int_0^\infty d\Gamma e^{-s \Gamma} P_\Gamma(\Gamma) </math>, satisfies
<center>
<math display="block">
<math>
\Phi(s)= \left[ \int dV\, p(V) e^{-\frac{s \tau^2 \eta}{V^2}} \Phi \left(\frac{s \tau^2 }{ V^2} \right)  \right]^k
\Phi(s)= \left[ \int d\epsilon\, p(\epsilon) e^{-\frac{s t^2 \eta}{\epsilon^2}} \Phi \left(\frac{s t^2 }{\epsilon^2} \right)  \right]^k
</math>
</math>
</center>
</li><br>
</li><br>


<li><em> The stability analysis. </em> We now wish to check the stability of our assumption to be in the localized phase, <math> \Gamma_a \sim \eta \ll 1 </math>, which led to the identity above for <math> \Phi(s) </math>. Our assumption is that the typical value of <math> \Gamma_a </math> is small, except for cases in which one of the descendants <math> b </math> is such that <math> \epsilon_b </math> is very small, in which case <math> \Gamma_a \sim 1/ \epsilon_b^2 </math>.  
<li><em> The stability analysis. </em> We now assume to be in the localized phase, when for <math> \eta \to 0 </math> the distribution <math> P_\Gamma(\Gamma) \to \delta (\Gamma)</math>. We wish to check the stability of our assumption. This is done by controlling the tails of the distribution <math> P_\Gamma(\Gamma)</math> for finite <math> \eta </math>.
<ul>
<ul>
<li> Show that if <math> \Gamma \sim 1/ \epsilon^2 </math> and <math>p(\epsilon)</math> is not gapped around zero, then <math>P_\Gamma(\Gamma) \sim \Gamma^{-3/2}</math>, i.e. the distribution has tails contributed by these events in which the local fields happen to be very small.  </li>
<li>  
<li> Assume more generally that  <math>P_\Gamma(\Gamma) \sim \Gamma^{-\alpha}</math> for large <math> \Gamma </math> and <math> \alpha \in [1, 3/2]</math>. Show that this corresponds to <math> \Phi(s) \sim 1- A |s|^\beta </math> for <math> s </math> small, with <math> \beta= \alpha-1 \in [0, 1/2] </math>.  </li>
For finite <math> \eta</math>, we expect that typically  <math> \Gamma_a \sim \eta \ll 1 </math>, and thus <math> P_\Gamma(\Gamma)</math> should have a peak at this scale; however, we also expect <sup>[[#Notes|[*] ]]</sup> some power law decay <math>P_\Gamma(\Gamma)\sim \Gamma^{-\alpha} </math> for large <math> \Gamma </math>.
<li>  Show that the equation for <math> \Phi(s) </math> gives for <math> s </math> small <math>1- A s^\beta =1- A k \int d\epsilon \, p(\epsilon) \frac{s^\beta t^{2 \beta}}{\epsilon^{2 \beta}}+ o(s^\beta) </math>, and therefore this is consistent provided that there exists a <math> \beta \in [0, 1/2] </math> solving
<!--and <math> \alpha \in (1, 3/2]</math>. These tails are contributed by the events  in which one of the descendants <math> b </math> is such that <math> \epsilon_b </math> is very small, in which case <math> \Gamma_a \sim 1/ \epsilon_b^2 \gg 1 </math>.
<center>
Show that if <math> \Gamma \sim 1/ \epsilon^2 </math> and <math>p(\epsilon)</math> is not gapped around zero, then <math>P_\Gamma(\Gamma) \sim \Gamma^{-3/2}</math>, i.e. the distribution has tails contributed by these events in which the local random potential happen to be very small.  </li>
<math>
<li> Assume more generally that  <math>P_\Gamma(\Gamma) \sim \Gamma^{-\alpha}</math> for large <math> \Gamma </math> and-->
1=k \int d\epsilon \, p(\epsilon) \left(\frac{t}{\epsilon}\right)^{2 \beta} \equiv k I(\beta).
Show, using a dimensional analysis argument, that this corresponds to a non-analytic behaviour of the Laplace transform, <math> \Phi(s) \sim 1- A |s|^\beta </math> for <math> s </math> small, with <math> \beta= \alpha-1 </math>.  </li>
 
<li>  Show that the equation for <math> \Phi(s) </math> gives for <math> s </math> small <math>1- A s^\beta =1- A k \int dV \, p(V) \frac{s^\beta \tau^{2 \beta}}{V^{2 \beta}}+ o(s^\beta) </math>, and therefore this is consistent provided that there exists a <math> \beta </math> solving
<math display="block">
1=k \int dV \, p(V) \left(\frac{\tau}{|V|}\right)^{2 \beta} \equiv k I(\beta).
</math>
</math>
</center> </li>
</li>
</ul>  
</ul>  
</li><br>






<li><em> The critical disorder. </em> Consider now local fields <math> \epsilon </math> taken from a uniform distribution in <math> [-W/2, W/2] </math>.  Compute <math> I(\beta) </math> and show that it is non monotonic, with a local minimum <math> \beta^* </math> in the interval <math> [0, 1/2]</math>. Show that  <math> I(\beta^*) </math> increases as the disorder is made weaker and weaker, and thus the transition to delocalisation occurs at the critical value of disorder when  <math> I(\beta^*)=k^{-1} </math>. Show that this gives
[[File:Bethe I(beta).png|thumb|left|x140px|Behaviour of the integral <math> I(\beta)</math> in the case of uniformily distributed disorder, for <math>W< W_c </math> .]]
<center>
 
<math>
<li><em> The critical disorder. </em> Consider now local fields <math> V_x </math> taken from a uniform distribution in <math> [-1/2, 1/2] </math>.  Compute <math> I(\beta) </math> and show that it is non monotonic, with a local minimum <math> \beta^* </math> in the interval <math> [0, 1/2]</math>. Show that  <math> I(\beta^*) </math> increases as the disorder is made weaker and weaker, and thus the transition to delocalisation occurs at the critical value of disorder when  <math> I(\beta^*)=k^{-1} </math>. Show that this gives the following estimate for the critical disorder <math>(W/t)_c=1/\tau_c </math> at which the transition to delocalisation occurs:
W_c = t \, 2 k e \log \left( \frac{W_c}{2 t}\right) \sim   t \, 2 k e \log \left(k\right)
<math display="block">
\frac{1}{\tau_c} = \, 2 k e \log \left( \frac{1}{2 \tau_c}\right) \sim   \, 2 e \, k \log \left(k\right)
</math>
</math>
</center>
Why the critical disorder increases with <math> k </math>?
Why the critical disorder increases with <math> k </math>?
   </li>
   </li>
</ol>
</ol>
<br>
<br>
-->




<!--<strong>Goal:</strong> in this final set of problems, we discuss the interplay between localization and glassiness, by connecting the solution to the Anderson problem on the Bethe lattice with the statistical physics problem of a directed polymer in random media on trees.
<div style="font-size:89%">
<br>
: <small>[*]</small> - Why do we expect power law tails? Recall that in first approximation  <math> \Gamma \sim 1/|V|^2</math>. If <math> V</math> is uniformly distributed, then <math>P_\Gamma(\Gamma) \sim \Gamma^{-3/2}</math>.
<strong>Techniques: </strong>
</div>
<br>
 
== Check out: key concepts ==
Linearization and stability analysis, critical disorder, mobility edge.


the directed polymer treatment:
== To know more ==
KPP (es 1)
es 2: The connection to directed polymer: linearisation and stability.
Glassiness vs localization
-->

Latest revision as of 12:38, 11 March 2026

Goal: the goal of this problem is to determine when the solution of the distributional equations corresponding to localization is unstable, providing an estimate of thee mobility edge on the Bethe lattice.
Techniques: stability analysis, Laplace transforms.


Problems

In this Problem we determine for which values of parameters localization is stable, estimating the critical value of disorder where the transition to a delocalised phase occurs. Recall the results of Problem 8: the real and imaginary parts of the local self energy satisfy the self-consistent equations:


Γa=batab2Γb+η(EWVbRb)2+(Γb+η)2,Ra=batab2EWVbRb(EWVbRb)2+(Γb+η)2

These equations admit the solution Γa=Γb=0 when η=0, which corresponds to localization. We now determine when this solution becomes unstable.

Problem 9: an estimate of the mobility edge

  1. Imaginary approximation and distributional equation. We consider the equations for Γa and neglect the terms Rb in the denominators, which couple the equations to those for the real parts of the self energies (“imaginary” approximation). Moreover, we assume to be in the localized phase, where Γaη1. Finally, we set tabt and E=0 for simplicity. Show that under these assumptions the probability density for the imaginary part, PΓ(Γ), satisfies for τ=t/W PΓ(Γ)=b=1kdVbp(Vb)b=1kdΓbPΓ(Γb)δ(Γτ2baΓb+ηVb2) Show that the Laplace transform of this distribution, Φ(s)=0dΓesΓPΓ(Γ), satisfies Φ(s)=[dVp(V)esτ2ηV2Φ(sτ2V2)]k

  2. The stability analysis. We now assume to be in the localized phase, when for η0 the distribution PΓ(Γ)δ(Γ). We wish to check the stability of our assumption. This is done by controlling the tails of the distribution PΓ(Γ) for finite η.
    • For finite η, we expect that typically Γaη1, and thus PΓ(Γ) should have a peak at this scale; however, we also expect [*] some power law decay PΓ(Γ)Γα for large Γ. Show, using a dimensional analysis argument, that this corresponds to a non-analytic behaviour of the Laplace transform, Φ(s)1A|s|β for s small, with β=α1.
    • Show that the equation for Φ(s) gives for s small 1Asβ=1AkdVp(V)sβτ2βV2β+o(sβ), and therefore this is consistent provided that there exists a β solving 1=kdVp(V)(τ|V|)2βkI(β).


    Behaviour of the integral I(β) in the case of uniformily distributed disorder, for W<Wc .
  3. The critical disorder. Consider now local fields Vx taken from a uniform distribution in [1/2,1/2]. Compute I(β) and show that it is non monotonic, with a local minimum β* in the interval [0,1/2]. Show that I(β*) increases as the disorder is made weaker and weaker, and thus the transition to delocalisation occurs at the critical value of disorder when I(β*)=k1. Show that this gives the following estimate for the critical disorder (W/t)c=1/τc at which the transition to delocalisation occurs: 1τc=2kelog(12τc)2eklog(k) Why the critical disorder increases with k?



[*] - Why do we expect power law tails? Recall that in first approximation Γ1/|V|2. If V is uniformly distributed, then PΓ(Γ)Γ3/2.

Check out: key concepts

Linearization and stability analysis, critical disorder, mobility edge.

To know more