Cover art: issues in the metric-guided and metric-less placement of random and stochastic template banks

TL;DR

This paper investigates randomized template placement strategies for gravitational-wave searches, addressing theoretical and practical challenges, and proposing a triangulation-based approach as an alternative to metric-based methods.

Contribution

It introduces a triangulation-based method for template placement that replaces the need for a Riemannian metric, enhancing flexibility in curved parameter spaces.

Findings

Randomized placement improves template bank efficiency.

Triangulation-based geometry can replace metric calculations.

Algorithm performance varies with parameter-space boundaries.

Abstract

The efficient placement of signal templates in source-parameter space is a crucial requisite for exhaustive matched-filtering searches of modeled gravitational-wave sources. Unfortunately, the current placement algorithms based on regular parameter-space meshes are difficult to generalize beyond simple signal models with few parameters. Various authors have suggested that a general, flexible, yet efficient alternative can be found in randomized placement strategies such as random placement and stochastic placement, which enhances random placement by selectively rejecting templates that are too close to others. In this article we explore several theoretical and practical issues in randomized placement: the size and performance of the resulting template banks; the effects of parameter-space boundaries; the use of quasi-random (self avoiding) number sequences; most important, the implementation of these algorithms in curved signal manifolds with and without the use of a Riemannian signal metric, which may be difficult to obtain. Specifically, we show how the metric can be replaced with a discrete triangulation-based representation of local geometry. We argue that the broad class of randomized placement algorithms offers a promising answer to many search problems, but that the specific choice of a scheme and its implementation details will still need to be fine-tuned separately for each problem.