Random projections in gravitational-wave searches from compact binaries II: efficient reconstruction of the detection statistic

TL;DR

This paper introduces a block-wise randomized matrix factorization method for efficient low-rank approximation of large template matrices in gravitational wave searches, significantly reducing computational costs.

Contribution

It presents a novel block-wise randomized matrix factorization algorithm and an approximate reconstruction method for detection statistics, improving efficiency in large-scale gravitational wave data analysis.

Findings

The new scheme is more efficient within the GstLAL pipeline.

Approximate reconstruction reduces total computational cost.

Combining algorithms enables more efficient online searches.

Abstract

Low-latency gravitational wave search pipelines such as GstLAL take advantage of low-rank factorization of the template matrix via singular value decomposition (SVD). With unprecedented improvements in detector bandwidth and sensitivity in advanced-LIGO and Virgo detectors, one expects several orders of magnitude increase in the size of template banks. This poses a formidable computational challenge in factorizing huge template matrices. Previously, [in Kulkarni et al. [6]], we introduced the idea of random projection (RP)-based matrix factorization as a computationally viable alternative to SVD, applicable for large template banks. This follow-up paper demonstrates the application of a block-wise randomized matrix factorization (RMF) algorithm for computing low-rank factorizations at a preset average fractional loss of SNR. This new scheme is shown to be more efficient in the context of the LLOID framework of the GstLAL search pipeline. Further, it is well-known that for huge template banks, the total computational cost of the search is dominated by reconstructing the detection statistic compared to that of filtering the data. However, optimizing the reconstruction cost has not been addressed satisfactorily so far in the available literature. We show that it is possible to approximately reconstruct the time-series of the matched-filter detection statistic at a fraction of the total cost using the matching pursuit algorithm. Combining the two algorithms presented in this paper can handle online searches involving large template banks more efficiently. We have analyzed the total computational cost in detail and offer various tips for optimally applying the RMF scheme in different parts of the parameter space. The algorithms presented in this paper are designed in a suitable manner that can be efficiently implemented over a distributed computing architecture.