N-flation in Supergravity

We have constructed a large field N-flation model in the supergravity framework. In this simple set-up, N fields collectively drive inflation where each field traverses sub-Planckian field values. This has been realised with a generalisation of the single field chaotic inflation in Supergravity. Interestingly, despite of the presence of the field interactions, the dynamics can be described in terms of an effective single field. The observable predictions of our model i.e. tensor to scalar ratio r and scalar spectral index n_s are akin to the chaotic inflation.Comment: Minor corrections added, Published Versio

Inflationary Predictions and Moduli Masses

A generic feature of inflationary models in supergravity/string constructions is vacuum misalignment for the moduli fields. The associated production of moduli particles leads to an epoch in the post-inflationary history in which the energy density is dominated by cold moduli particles. This modification of the post-inflationary history implies that the preferred range for the number of e-foldings between horizon exit of the modes relevant for CMB observations and the end of inflation $(N_k)$ depends on moduli masses. This in turn implies that the precision CMB observables $n_s$ and $r$ are sensitive to moduli masses. We analyse this sensitivity for some representative models of inflation and find the effect to be highly relevant for confronting inflationary models with observations.Comment: 17 pages, 3 figures; v2 minor additions in tex

Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

N-flation with Hierarchically Light Axions in String Compactifications

We present an explicit embedding of axionic N-flation in type IIB string compactifications where most of the Kahler moduli are stabilised by perturbative effects, and so are hierarchically heavier than the corresponding N >> 1 axions whose collective dynamics drives inflation. This is achieved in the framework of the LARGE Volume Scenario for moduli stabilisation. Our set-up can be used to realise a model of either inflation or quintessence, just by varying the volume of the internal space which controls the scale of the axionic potential. Both cases predict a very high scale of supersymmetry breaking. A viable reheating of the Standard Model degrees of freedom can be achieved after the end of inflation due to the perturbative decay of the N light axions which drive inflation.Comment: 28 pages, no figures, Journal versio