New Methods for Offline GstLAL Analyses

TL;DR

This paper introduces new computational and analytical methods for offline GstLAL gravitational wave searches, significantly improving sensitivity, reliability, and efficiency during the O4 observing run.

Contribution

The paper presents novel techniques for data reuse, combined searches, likelihood adjustments, and background estimation that enhance GstLAL's offline analysis capabilities.

Findings

50%-100% increase in sensitivity for high-mass sources

More reliable significance estimation of gravitational wave candidates

Reduced computational resources and improved reusability of analysis results

Abstract

In this work, we present new methods implemented in the GstLAL offline gravitational wave search. These include a technique to reuse the matched filtering data products from a GstLAL online analysis, which hugely reduces the time and computational resources required to obtain offline results; a technique to combine these results with a separate search for heavier black hole mergers, enabling detections from a larger set of gravitational wave sources; changes to the likelihood ratio which increases the sensitivity of the analysis; and two separate changes to the background estimation, allowing more precise significance estimation of gravitational wave candidates. Some of these methods increase the sensitivity of the analysis, whereas others correct previous mis-estimations of sensitivity by eliminating false positives. These methods have been adopted for GstLAL's offline results during the fourth observing run of LIGO, Virgo, and KAGRA (O4). To test these new methods, we perform an offline analysis over one chunk of O3 data, lasting from May 12 19:36:42 UTC 2019 to May 21 14:45:08 UTC 2019, and compare it with previous GstLAL results over the same period of time. We show that cumulatively these methods afford around a 50% - 100% increase in sensitivity in the highest mass space, while simultaneously increasing the reliability of results, and making them more reusable and computationally cheaper.