Homework: Difference between revisions
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<li> <em> | <li> <em> Power laws.</em> Compute the distribution of the variables <math> \delta E_\alpha </math> and show that for <math> (\delta E)^2/N \ll 1 </math> this is an exponential. Using this, compute the distribution of the <math> z_\alpha </math> and show that it is a power law, | ||
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p(z)= \frac{c}{z^{1+\mu}} \quad \quad \mu= \frac{2 \sqrt{\log 2}}{\beta} | p(z)= \frac{c}{z^{1+\mu}} \quad \quad \mu= \frac{2 \sqrt{\log 2}}{\beta} | ||
</math></center> | </math></center> | ||
For which values of temperature the second moment of z exists? And the first moment? | |||
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<li> <em> When <math> T < T_c </math> the distribution of <math> z </math> becomes heavy tailed. What does this imply for the sum <math> Z </math>? How fast does it scale with <math> M=2^N </math>? Discuss in which sense this is consistent with the behaviour of the partition function and of the entropy discussed in Problem 1.2. In particular, intuitively, why can one talk about a localization or condensation transition? | |||
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IPR= \frac{\sum_{\alpha=1}^{2^N} z_\alpha^2}{[\sum_{\alpha=1}^{2^N} z_\alpha]^2}= \sum_{\alpha=1}^{2^N} \omega_\alpha^2 \quad \quad \omega_\alpha=\frac{ z_\alpha}{\sum_{\alpha=1}^{2^N} z_\alpha}. | IPR= \frac{\sum_{\alpha=1}^{2^N} z_\alpha^2}{[\sum_{\alpha=1}^{2^N} z_\alpha]^2}= \sum_{\alpha=1}^{2^N} \omega_\alpha^2 \quad \quad \omega_\alpha=\frac{ z_\alpha}{\sum_{\alpha=1}^{2^N} z_\alpha}. | ||
</math></center> | </math></center> | ||
When <math> z </math> is power law distributed with exponent <math> \mu </math>, | When <math> z </math> is power law distributed with exponent <math> \mu </math>, then | ||
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IPR= \frac{\Gamma(2-\mu)}{\Gamma(\mu) \Gamma(1-\mu)}. | IPR= \frac{\Gamma(2-\mu)}{\Gamma(\mu) \Gamma(1-\mu)}. | ||
</math></center> | </math></center> | ||
Discuss how this quantity changes across the transition at <math> \mu=1 </math>, and how this fits with what you expect in general in a localized phase. | Check this identity numerically (with your favourite program: mathematica, python...) Discuss how this quantity changes across the transition at <math> \mu=1 </math>, and how this fits with what you expect in general in a localized phase. | ||
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Revision as of 19:51, 27 December 2023
Problem H.1: freezing as a localization/condensation transition
In this problem, we show how the freezing transition can be understood in terms of extreme valued statistics (discussed in the lecture) and localization. We consider the energies of the configurations and define , so that
We show that is a sum of random variables that become heavy tailed for , implying that the central limit theorem is violated and this sum is dominated by few terms, the largest ones. This can be interpreted as the occurrence of localization (or condensation).
- Power laws. Compute the distribution of the variables and show that for this is an exponential. Using this, compute the distribution of the and show that it is a power law,
For which values of temperature the second moment of z exists? And the first moment?
- When the distribution of becomes heavy tailed. What does this imply for the sum ? How fast does it scale with ? Discuss in which sense this is consistent with the behaviour of the partition function and of the entropy discussed in Problem 1.2. In particular, intuitively, why can one talk about a localization or condensation transition?
- Inverse participation ratio. The low temperature behaviour of the partition function an be characterized in terms of a standard measure of localization (or condensation), the Inverse Participation Ratio (IPR) defined as:
When is power law distributed with exponent , then
Check this identity numerically (with your favourite program: mathematica, python...) Discuss how this quantity changes across the transition at , and how this fits with what you expect in general in a localized phase.