Random projections in gravitational wave searches of compact binaries

Abstract

Random projection (RP) is a powerful dimension reduction technique widely used in the analysis of high dimensional data. We demonstrate how this technique can be used to improve the computational efficiency of gravitational wave searches from compact binaries of neutron stars or black holes. Improvements in low-frequency response and bandwidth due to detector hardware upgrades pose a data analysis challenge in the advanced LIGO era as they result in increased redundancy in template databases and longer templates due to the higher number of signal cycles in-band. The RP-based methods presented here address both these issues within the same broad framework. We first use RP for an efficient, singular value decomposition inspired template matrix factorization and develop a geometric intuition for why this approach works. We then use RP to calculate approximate time-domain match correlations in a lower dimensional vector space. For searches over parameters corresponding to non-spinning binaries with a neutron star and a black hole, a combination of the two methods can reduce the total on-line computational cost by an order of magnitude over a nominal baseline. This can, in turn, help free-up computational resources needed to go beyond current spin-aligned searches to more complex ones involving generically spinning waveforms.

Matches the published version in Physical Review D (Rapid Communication) Phys. Rev. D 99, 101503(R) (2019)

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Cited by in corpus (4)

• Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
• A geometric template bank for the detection of spinning low-mass compact binaries with moderate orbital eccentricity
• Hierarchical approach to matched filtering using a reduced basis
• Random projections in gravitational-wave searches from compact binaries II: efficient reconstruction of the detection statistic