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Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,General Relativity and Quantum Cosmology, gr-qc
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..
