Inpainting over the cracks: challenges of applying pre-merger searches for massive black hole binaries to realistic LISA datasets

TL;DR

This paper compares zero-latency filtering and inpainting techniques for premerger detection of massive black hole binaries in LISA data, demonstrating successful identification even with data gaps and overlapping signals.

Contribution

First application of inpainting for premerger detection in LISA data, showing robustness to data gaps and overlapping signals.

Findings

Inpainting method detects signals with 3-day data gaps.

Both methods identify signals at least half a day before merger.

Removing identified signals helps detect quieter overlapping signals.

Abstract

A key science target of the Large Interferometer Space Antenna (LISA) is to carry out multi-messenger observations of massive black hole binaries, observing the merger simultaneously in gravitational waves and with electromagnetic observatories. Identifying that a merger is happening and providing an updating estimate of the sky location in the hours, days and weeks before the merger is critical to enable electromagnetic observations of the merger event. In this work we demonstrate and compare two methods for premerger identification of massive black hole binaries; a zero-latency filter approach and, for the first time, an approach using an ``inpainting'' technique. We apply these methods to the LISA Data Challenge dataset 2a--Sangria-HM--and demonstrate the successful recovery of the 14 signals in the dataset that we expected to be identifiable at least half a day before merger. We show that the inpainting method can identify premerger signals even when gaps are present in the data, demonstrating the recovery of a signal even when 3 day-long data gaps are added to the 14 days preceding merger. Finally, we explore the challenge of overlapping signals, using a region of overlapping signals in the Sangria-HM dataset, all of which merge within a 10-day window, and show how removing signals that have been confidently identified from the data allows us to identify quieter signals in the same period.