Sparse data inpainting for the recovery of Galactic-binary gravitational wave signals from gapped data

Abstract

The forthcoming space-based gravitational wave observatory LISA will open a new window for the measurement of galactic binaries, which will deliver unprecedented information about these systems. However, the detection of galactic binary gravitational wave signals is challenged by the presence of gaps in the data. Whether being planned or not, gapped data dramatically reduce our ability to detect faint signals and the risk of misdetection soars. Inspired by advances in signal processing, we introduce a non-parametric inpainting algorithm based on the sparse representation of the galactic binary signal in the Fourier domain. In contrast to traditional inpainting approaches, noise statistics are known theoretically on ungapped measurements only. This calls for the joint recovery of both the ungapped noise and the galactic binary signal. We thoroughly show that sparse inpainting yields an accurate estimation of the gravitational imprint of the galactic binaries. Additionally, we highlight that the proposed algorithm produces a statistically consistent ungapped noise estimate. We further evaluate the performances of the proposed inpainting methods to recover the gravitational wave signal on a simple example involving verification galactic binaries recently proposed in LISA data challenges.

Effect of data gaps on the detectability and parameter estimation of massive black hole binaries with LISA

“…Nevertheless, our work here does not focus on studying the optimal gap treatment, but rather on making a baseline assessment of the impact of data gaps on the detectability of MBHB signals with LISA. Alternative methods for gap treatment can be found in [30] and in [105]. We then assess the impact of data gaps of different lengths by adopting different measures, such as the SNR loss.…”

Searching for Mini Extreme Mass Ratio Inspirals with Gravitational-Wave Detectors

“…The durations of these signals, and the presence of non-stationary noise and gaps in the data, imply computational challenges for traditional matched-filtering algorithms that search for binary neutron star mergers and sub-solar mass and stellar-mass binary black hole mergers [37][38][39]. Furthermore, mini-EMRIs could last in the detector band for timescales compatible with those expected from continuous waves from isolated neutron stars [40,41], boson clouds around black holes [23,[42][43][44], and quasi-monochromatic signals arising from dark matter interactions with GW detectors [45][46][47][48][49].…”