Search for solar axion emission from 7Li and D(p,gamma)3He nuclear decays with the CAST gamma-ray calorimeter

We present the results of a search for a high-energy axion emission signal from 7Li (0.478 MeV) and D(p,gamma)3He (5.5 MeV) nuclear transitions using a low-background gamma-ray calorimeter during Phase I of the CAST experiment. These so-called "hadronic axions" could provide a solution to the long-standing strong-CP problem and can be emitted from the solar core from nuclear M1 transitions. This is the first such search for high-energy pseudoscalar bosons with couplings to nucleons conducted using a helioscope approach. No excess signal above background was found.Comment: 20 pages, 8 figures, final version to be published in JCA

Statistical mechanics of budget-constrained auctions

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). Based on the cavity method of statistical mechanics, we introduce a message passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.Comment: Minor revisio

Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study

Phase transitions play an important role in understanding search difficulty in combinatorial optimisation. However, previous attempts have not revealed a clear link between fitness landscape properties and the phase transition. We explore whether the global landscape structure of the number partitioning problem changes with the phase transition. Using the local optima network model, we analyse a number of instances before, during, and after the phase transition. We compute relevant network and neutrality metrics; and importantly, identify and visualise the funnel structure with an approach (monotonic sequences) inspired by theoretical chemistry. While most metrics remain oblivious to the phase transition, our results reveal that the funnel structure clearly changes. Easy instances feature a single or a small number of dominant funnels leading to global optima; hard instances have a large number of suboptimal funnels attracting the search. Our study brings new insights and tools to the study of phase transitions in combinatorial optimisation

Low Energy Neutrino Physics after SNO and KamLAND

In the recent years important discoveries in the field of low energy neutrino physics (E$_\nu$ in the $\approx$ MeV range) have been achieved. Results of the solar neutrino experiment SNO show clearly flavor transitions from $\nu_e$ to $\nu_{\mu,\tau}$. In addition, the long standing solar neutrino problem is basically solved. With KamLAND, an experiment measuring neutrinos emitted from nuclear reactors at large distances, evidence for neutrino oscillations has been found. The values for the oscillation parameters, amplitude and phase, have been restricted. In this paper the potential of future projects in low energy neutrino physics is discussed. This encompasses future solar and reactor experiments as well as the direct search for neutrino masses. Finally the potential of a large liquid scintillator detector in an underground laboratory for supernova neutrino detection, solar neutrino detection, and the search for proton decay $p \to K^+ \nu$ is discussed.Comment: Invited brief review, World Scientific Publishing Compan

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

The Short Path Algorithm Applied to a Toy Model

We numerically investigate the performance of the short path optimization algorithm on a toy problem, with the potential chosen to depend only on the total Hamming weight to allow simulation of larger systems. We consider classes of potentials with multiple minima which cause the adiabatic algorithm to experience difficulties with small gaps. The numerical investigation allows us to consider a broader range of parameters than was studied in previous rigorous work on the short path algorithm, and to show that the algorithm can continue to lead to speedups for more general objective functions than those considered before. We find in many cases a polynomial speedup over Grover search. We present a heuristic analytic treatment of choices of these parameters and of scaling of phase transitions in this model.Comment: 11 pages, 9 figures; v2 final version published in Quantu