Speaker
Description
Constructing template banks for matched filtering gravitational wave searches requires placing waveforms densely across a curved parameter space, where distances are defined by a position dependent metric. Evaluating these distances is computationally expensive, and the non-uniform curvature of the space makes uniform template placement suboptimal.
We propose a neural network approach that learns an approximate isometric embedding of the physical parameter space. In the learned coordinates, Euclidean distances closely approximate true metric distances, reducing template placement to a simple sphere-covering problem in flat space. The network is trained using geometric objectives derived from the induced metric, without relying on density estimation or likelihood-based training.
Applied to a three dimensional gravitational wave parameter space, the learned embedding achieves ∼98% injection recovery, demonstrating that geometry aware coordinate learning is a promising direction for efficient template bank construction.