Sequence of multipolar transitions: Scenarios for URu2Si2

d- and f-shells support a large number of local degrees of freedom: dipoles, quadrupoles, octupoles, hexadecapoles, etc. Usually, the ordering of any multipole component leaves the system sufficiently symmetrical to allow a second symmetry breaking transition. Assuming that a second continuous phase transition occurs, we classify the possibilities. We construct the symmetry group of the first ordered phase, and then re-classify the order parameters in the new symmetry. While this is straightforward for dipole or quadrupole order, it is less familiar for octupole order. We give a group theoretical analysis, and some illustrative mean field calculations, for the hypothetical case when a second ordering transition modifies the primary T(xyz) octupolar ordering in a tetragonal system like URu2Si2. If quadrupoles appear in the second phase transition, they must be accompanied by a time-reversal-odd multipole as an induced order parameter. For O(xy), O(zx), or O(yz) quadrupoles, this would be one of the components of J, which should be easy either to check or to rule out. However, a pre-existing octupolar symmetry can also be broken by a transition to a new octupole--hexadecapole order, or by a combination of O(22) quadrupole and triakontadipole order. It is interesting to notice that if recent NQR results (Saitoh et al, 2005) on URu2Si2 are interpreted as a hint that the onset of octupolar hidden order at T0=17K is followed by quadrupolar ordering at T* = 13.5K, this sequence of events may fit several of the scenarios found in our general classification scheme. However, we have to await further evidence showing that the NQR anomalies at T* = 13.5K are associated with an equilibrium phase transition.Comment: 20 pages, 7 figures, latex, uses ptptex.cls, accepted for publication in the Proceedings of YKIS2004 on the "Physics of Strongly Correlated Electron Systems" (Kyoto, November 2004) (to appear in Progr. Theor. Phys. Suppl.). Parts of the discussion revise

On the origin of Phase Transitions in the absence of Symmetry-Breaking

In this paper we investigate the Hamiltonian dynamics of a lattice gauge model in three spatial dimension. Our model Hamiltonian is defined on the basis of a continuum version of a duality transformation of a three dimensional Ising model. The system so obtained undergoes a thermodynamic phase transition in the absence of symmetry-breaking. Besides the well known use of quantities like the Wilson loop we show how else the phase transition in such a kind of models can be detected. It is found that the first order phase transition undergone by this model is characterised according to an Ehrenfest-like classification of phase transitions applied to the configurational entropy. On the basis of the topological theory of phase transitions, it is discussed why the seemingly divergent behaviour of the third derivative of configurational entropy can be considered as the "shadow" of some suitable topological transition of certain submanifolds of configuration space.Comment: 31 pages, 9 figure

Statistical Physics and Representations in Real and Artificial Neural Networks

This document presents the material of two lectures on statistical physics and neural representations, delivered by one of us (R.M.) at the Fundamental Problems in Statistical Physics XIV summer school in July 2017. In a first part, we consider the neural representations of space (maps) in the hippocampus. We introduce an extension of the Hopfield model, able to store multiple spatial maps as continuous, finite-dimensional attractors. The phase diagram and dynamical properties of the model are analyzed. We then show how spatial representations can be dynamically decoded using an effective Ising model capturing the correlation structure in the neural data, and compare applications to data obtained from hippocampal multi-electrode recordings and by (sub)sampling our attractor model. In a second part, we focus on the problem of learning data representations in machine learning, in particular with artificial neural networks. We start by introducing data representations through some illustrations. We then analyze two important algorithms, Principal Component Analysis and Restricted Boltzmann Machines, with tools from statistical physics

Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include

The use of synchrotron edge topography to study polytype nearest neighbour relationships in SiC

A brief review of the phenomenon of polytypism is presented and its prolific abundance in Silicon Carbide discussed. An attempt has been made to emphasise modern developments in understanding this unique behaviour. The properties of Synchrotron Radiation are shown to be ideally suited to studies of polytypes in various materials and in particular the coalescence of polytypes in SiC. It is shown that with complex multipolytypic crystals the technique of edge topography allows the spatial extent of disorder to be determined and, from the superposition of Laue type reflections, neighbourhood relationships between polytypes can be deduced. Finer features have now been observed with the advent of second generation synchrotrons, the resolution available enabling the regions between adjoining polytypes to be examined more closely. It is shown that Long Period Polytypes and One Dimensionally Disordered layers often found in association with regions of high defect density are common features at polytype boundaries. An idealised configuration termed a "polytype sandwich" is presented as a model for the structure of SiC grown by the modified Lely technique. The frequency of common sandwich edge profiles are classified and some general trends of polytype neighbourism are summarised