Racetrack Model Research Articles

Racetrack inflation and assisted moduli stabilisation

We present a model of inflation based on a racetrack model without flux stabilization. The initial conditions are set automatically through topological inflation. This ensures that the dilaton is not swept to weak coupling through either thermal effects or fast roll. Including the effect of non-dilaton fields we find that moduli provide natural candidates for the inflaton. The resulting potential generates slow-roll inflation without the need to fine-tune parameters. The energy scale of inflation must be near the GUT scale and the scalar density perturbation generated has a spectrum consistent with WMAP data.

Moduli-mixing racetrack model

We study supersymmetric models with double gaugino condensations in the hidden sector, where the gauge couplings depend on two light moduli of superstring theory. We perform a detailed analysis of this class of model and show that there is no stable supersymmetric minimum with finite vacuum values of moduli fields. Instead, we find that the supersymmetry breaking occurs with moduli stabilized and negative vacuum energy. That yields moduli-dominated soft supersymmetry breaking terms. To realize slightly positive (or vanishing) vacuum energy, we add uplifting potential. We discuss uplifting does not change qualitatively the vacuum expectation values of moduli and the above feature of supersymmetry breaking.

Adaptive learning mechanisms for ordering actions using random races

Consider a learning machine (LM) interacting with an environment epsilon . The environment offers the machine M actions. Traditionally, learning systems endeavor to compute the best action that the environment offers, and this is done without any estimation procedure. In this paper, we consider the problem of the LM computing not only the optimal action offered but also the ordering of the actions in terms of their optimality. The problem is posed in its generality and various norms of learning in this setting are formalized. Also various learning strategies are presented that use a new mathematical model called the random race. In this model the learning is modeled using M racers that are running toward a goal. At each instant, racer R/sub i/ moves toward the goal with a probability of s/sub i/ and stays where he is with a probability of (1-s/sub i/). In the simplest learning model, the learning multiple race track (LMRT) model, the racers run on multiple tracks, and in this scenario, each racer has his own track, thus disallowing interference between the racers. However, in a more general setting, the learning single race track (LSRT) model, the racers run on a single track, and in this case, interferences between racers are specified in terms of overtaking rules. In this paper, we first examine the learning multiple race track (LMRT) model, and we have shown that in the absence of a priori information the LMRT is permutationally epsilon -optimal in all suggestive random environments. Other results are proven or conjectured. >