Tunable non-equilibrium dynamics: field quenches in spin ice

We present non-equilibrium physics in spin ice as a novel setting which combines kinematic constraints, emergent topological defects, and magnetic long range Coulomb interactions. In spin ice, magnetic frustration leads to highly degenerate yet locally constrained ground states. Together, they form a highly unusual magnetic state -- a "Coulomb phase" -- whose excitations are pointlike defects -- magnetic monopoles -- in the absence of which effectively no dynamics is possible. Hence, when they are sparse at low temperature, dynamics becomes very sluggish. When quenching the system from a monopole-rich to a monopole-poor state, a wealth of dynamical phenomena occur the exposition of which is the subject of this article. Most notably, we find reaction diffusion behaviour, slow dynamics due to kinematic constraints, as well as a regime corresponding to the deposition of interacting dimers on a honeycomb lattice. We also identify new potential avenues for detecting the magnetic monopoles in a regime of slow-moving monopoles. The interest in this model system is further enhanced by its large degree of tunability, and the ease of probing it in experiment: with varying magnetic fields at different temperatures, geometric properties -- including even the effective dimensionality of the system -- can be varied. By monitoring magnetisation, spin correlations or zero-field Nuclear Magnetic Resonance, the dynamical properties of the system can be extracted in considerable detail. This establishes spin ice as a laboratory of choice for the study of tunable, slow dynamics.Comment: (16 pages, 13 figures

Status of Neutrino Factory R&D within the Muon Collaboration

We describe the current status of the research within the Muon Collaboration towards realizing a Neutrino Factory. We describe briefly the physics motivation behind the neutrino factory approach to studying neutrino oscillations and the longer term goal of building the Muon Collider. The benefits of a step by step staged approach of building a proton driver, collecting and cooling muons followed by the acceleration and storage of cooled muons are emphasized. Several usages of cooled muons open up at each new stage in such an approach and new physics opportunites are realized at the completion of each stage.Comment: 19 pages, 20 figures. To Appear in the Proceedings of the International Workshop on Neutrino Oscillations in Venice, NO-VE 200

Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty

The integration of intermittent and stochastic renewable energy resources requires increased flexibility in the operation of the electric grid. Storage, broadly speaking, provides the flexibility of shifting energy over time; network, on the other hand, provides the flexibility of shifting energy over geographical locations. The optimal control of storage networks in stochastic environments is an important open problem. The key challenge is that, even in small networks, the corresponding constrained stochastic control problems on continuous spaces suffer from curses of dimensionality, and are intractable in general settings. For large networks, no efficient algorithm is known to give optimal or provably near-optimal performance for this problem. This paper provides an efficient algorithm to solve this problem with performance guarantees. We study the operation of storage networks, i.e., a storage system interconnected via a power network. An online algorithm, termed Online Modified Greedy algorithm, is developed for the corresponding constrained stochastic control problem. A sub-optimality bound for the algorithm is derived, and a semidefinite program is constructed to minimize the bound. In many cases, the bound approaches zero so that the algorithm is near-optimal. A task-based distributed implementation of the online algorithm relying only on local information and neighbor communication is then developed based on the alternating direction method of multipliers. Numerical examples verify the established theoretical performance bounds, and demonstrate the scalability of the algorithm.Comment: arXiv admin note: text overlap with arXiv:1405.778