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Placing signal templates (grid points) as efficiently as possible to cover amulti-dimensional parameter space is crucial in computing-intensivematched-filtering searches for gravitational waves, but also in similarsearches in other fields of astronomy. To generate efficient coverings ofarbitrary parameter spaces, stochastic template banks have been advocated,where templates are placed at random while rejecting those too close to others.However, in this simple scheme, for each new random point its distance to everytemplate in the existing bank is computed. This rapidly increasing number ofdistance computations can render the acceptance of new templatescomputationally prohibitive, particularly for wide parameter spaces or in largedimensions. This work presents a neighboring cell algorithm that candramatically improve the efficiency of constructing a stochastic template bank.By dividing the parameter space into sub-volumes (cells), for an arbitrarypoint an efficient hashing technique is exploited to obtain the index of itsenclosing cell along with the parameters of its neighboring templates. Henceonly distances to these neighboring templates in the bank are computed,massively lowering the overall computing cost, as demonstrated in simpleexamples. Furthermore, we propose a novel method based on this technique toincrease the fraction of covered parameter space solely by directed templateshifts, without adding any templates. As is demonstrated in examples, thismethod can be highly effective..

Efficient generation and optimization of stochastic template banks by a neighboring cell algorithm