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

TL;DR

This paper introduces a neighboring cell algorithm that significantly enhances the efficiency of constructing stochastic template banks for gravitational wave searches by reducing distance computations and proposes a method to improve coverage through directed template shifts.

Contribution

The work presents a novel neighboring cell algorithm for efficient template bank generation and a new method for increasing coverage via template shifts without adding templates.

Findings

The neighboring cell algorithm reduces computational cost in template bank construction.

The proposed method improves parameter space coverage through directed template shifts.

Demonstrated efficiency gains in simple example scenarios.

Abstract

Placing signal templates (grid points) as efficiently as possible to cover a multi-dimensional parameter space is crucial in computing-intensive matched-filtering searches for gravitational waves, but also in similar searches in other fields of astronomy. To generate efficient coverings of arbitrary 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 every template in the existing bank is computed. This rapidly increasing number of distance computations can render the acceptance of new templates computationally prohibitive, particularly for wide parameter spaces or in large dimensions. This work presents a neighboring cell algorithm that can dramatically improve the efficiency of constructing a stochastic template bank. By dividing the parameter space into sub-volumes (cells), for an arbitrary point an efficient hashing technique is exploited to obtain the index of its enclosing cell along with the parameters of its neighboring templates. Hence only distances to these neighboring templates in the bank are computed, massively lowering the overall computing cost, as demonstrated in simple examples. Furthermore, we propose a novel method based on this technique to increase the fraction of covered parameter space solely by directed template shifts, without adding any templates. As is demonstrated in examples, this method can be highly effective..