Searching in 2-Dimensional mass space for final states with 2 invisible particles

A method to search for particles of unknown masses in final states with two invisible particles is presented. Searching for final states with missing energy is a challenging task usually performed in the tail of a missing energy related distribution. The search method proposed is based on a 2-Dimensional mass reconstruction of the final state with two invisible particles. Thus, a bump hunting is possible, allowing a stronger signal versus background discrimination. Parameters of the new theory can be extracted from the mass distributions, a valuable step towards understanding its true nature. The proof of principle is based on the existing SM top pairs in their dilepton final state. The method is applicable in many interesting searches at the LHC, including dark matter candidates or heavy top partners.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.