Looking for a heavy wino LSP in collider and dark matter experiments

We investigate the phenomenology of a wino LSP as obtained in AMSB and some string models. The WMAP constraint on the DM relic density implies a wino LSP mass of 2.0-2.3 TeV. We find a viable signature for such a heavy wino at CLIC, operating at its highest CM energy of 5 TeV. One also expects a viable monochromatic $\gamma$-ray signal from its pair-annihilation at the galactic centre at least for cuspy DM halo profiles.Comment: A discussion on non-perturbative effects on annihilation cross section of TeV scale wino LSP added. Version to appear in Phys. Rev. D

Invisible charginos and neutralinos from gauge boson fusion: a way to explore anomaly mediation ?

We point out that vector boson fusion (VBF) at the Large Hadron Collider (LHC) can lead to useful signals for charginos and neutralinos in supersymmetric scenarios where these particles are almost invisible. The proposed signals are just two forward jets with missing transverse energy. It is shown that in this way one can probe a large region of the parameter space of a theory with anomaly mediated supersymmtery breaking (AMSB) at the LHC. In addition, scenarios where the lightest neutralinos and charginos are Higgsino-like can give copious signals of the above type

Q-Learning Induced Artificial Bee Colony for Noisy Optimization

The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding of the given solution. The fitness estimate and the fitness variance of a sub-population surrounding the given solution are jointly used to signify the degree of noise contamination in its local neighborhood (LN). The adaptation of sample size based on the characteristics of the fitness landscape in the LN of a solution is realized here with the temporal difference Q-learning (TDQL). The merit of the present work lies in utilizing the reward-penalty based reinforcement learning mechanism of TDQL for sample size adaptation. This sidesteps the prerequisite setting of any specific functional form of relationship between the sample size requirement of a solution and the noisy fitness profile in its LN. Experiments undertaken reveal that the proposed algorithms, realized with artificial bee colony, significantly outperform the existing counterparts and the state-of-the-art algorithms

Migration in Multi-Population Differential Evolution for Many Objective Optimization

The paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE involves N populations, each created for an objective to be optimized using MaODE. The only mode of knowledge transfer among populations in MaODE is the modified version of mutation policy of DE, where every member of the population during mutation is influenced by the best members of all the populations under consideration. The present work aims at further increasing the communication between the members of the population by communicating between a superior and an inferior population, using a novel migration strategy. The proposed migration policy enables poor members of an inferior population to evolve with a superior population. Simultaneously, members from the superior population are also transferred to the inferior one to help it improving its performance. Experiments undertaken reveal that the proposed extended version of MaODE significantly outperforms its counterpart and the state-of-the-art techniques

Some Observations on the Emulsion Polymerization of Styrene In the Aqueous Phase at 50° & Low Monomer Concentration

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