REVIEW 5 cited by
Extraction of gravitational wave signals from LISA data in the presence of artifacts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Extraction of gravitational wave signals from LISA data in the presence of artifacts
read the original abstract
The Laser Interferometer Space Antenna (LISA) mission is being developed by ESA with NASA participation. As it has recently passed the Mission Adoption milestone, models of the instruments and noise performance are becoming more detailed, and likewise prototype data analyses must as well. Assumptions such as Gaussianity, stationarity, and data continuity are unrealistic, and must be replaced with physically motivated data simulations, and data analysis methods adapted to accommodate such likely imperfections. To this end, the LISA Data Challenges have produced datasets featuring time-varying and unequal constellation armlength, and measurement artifacts including data interruptions and instrumental transients. In this work, we assess the impact of these data artifacts on the inference of Galactic Binary and Massive Black Hole properties. Our analysis shows that the treatment of noise transients and gaps is necessary for effective parameter estimation, as they substantially corrupt the analysis if unmitigated. We find that straightforward mitigation techniques can significantly if imperfectly suppress artifacts. For the Galactic Binaries, mitigation of glitches was essentially total, while mitigations of the data gaps increased parameter uncertainty by approximately 10%. For the Massive Black Hole binaries the particularly pernicious glitches resulted in a 30% uncertainty increase after mitigations, while the data gaps can increase parameter uncertainty by up to several times. Critically, this underlines the importance of early detection of transient gravitational waves to ensure they are protected from planned data interruptions.
Forward citations
Cited by 5 Pith papers
-
First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals
Moderately mitigated glitch streams induce negligible to minor biases (0.04–0.6σ) in EMRI parameters while weakly mitigated streams with higher-SNR events can reach ~1σ biases, making EMRI inference more robust than f...
-
Neural posterior estimation of Galactic Binary signals for the LISA mission
Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
-
Inpainting over the cracks: challenges of applying pre-merger searches for massive black hole binaries to realistic LISA datasets
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
-
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noi...
-
Handling Data Gaps for the Next Generation of Gravitational-Wave Observatories
Presents an efficient time-frequency Bayesian gap-filling technique that avoids costly matrix operations and integrates into LISA Global Fit, demonstrated on simulated LISA data.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.