The AMS-02 Experiment and the Dark Matter Search

AMS-02 is running after great scientific goals since one year and a half: a final setting up for dark matter searches has been achieved, allowing to study the so important antiparticle to particle ratios, which will probably be the first dark matter signals ever corroborated. Even if primary cosmic rays fluxes are subjected to a lot of uncertainties sources, some statements can be done and have been written down about dark matter properties: DM should be a heavy Majorana fermion or Spin 0 or 1 boson, with a mass from about 1 TeV to 10 TeV - unveiling a new TeV-ish search age - which could be able to originate antiparticle fluxes enhancements at high energies, both for positrons and antiprotons. All the observations, direct and indirect, point to these new paradigms or can be traced back to them quite easily. These enhancements perfectly fall into the research window of AMS-02, allowing the experiment to attack each today credible theory. Also an investigation of the Sommerfeld effect-associated dark boson will be possible, in terms of antiparticle to particle ratios substructures. The first great AMS-02 measurement is the positron fraction: an official paper is going to be submitted in few months, where the correct behavior of the apparatus will be reviewed and the full positron fraction rate will be analyzed up to 200 GeV. In this concern, one of the objectives of this work is to test the AMS-02 capability and versatility in doing these dark matter researches, thanks to an orbital temporal (and geomagnetic) stability. The goal has been accomplished: the experiment is very stable in time, so that the temporal error associated to the positron fraction measurement is compatible with zero, offering a beyond belief opportunity to measure CR antiparticle to particle ratios.

Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory

This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.