Improved performance of an array of Cherenkov telescopes by means of multi-objective evolutionary optimization

Multi-objective evolutionary algorithm is employed for improving the performance of an array of imaging atmospheric Cherenkov telescopes from their positions on the ground, looking for configurations beyond the telescope arrangement investigated by Monte Carlo simulations. For the case of arrays of few (up to 6) telescopes, we are able to uniquely associated the various optimization objectives to different geometry classes, whereas for large arrays a more varied landscape of array configurations are recovered which provide new insights for posterior detailed Monte-Carlo investigations. This is the first time that meat-heuristics are applied to optimization of Gamma-Ray Astronomy, opening new possibilities for telescope array designs, at a moment when the international community prepares to construct the global Cherenkov Telescope Array (CTA) observatory.