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 for MBHBs.
Baghiet al.(LDC Working Group), The LISA Data Challenges, (2022), arXiv:2204.12142 [gr-qc]
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
Numerical ringdown waveforms for black holes in Dehnen dark matter profiles are generated and analyzed for detectability and parameter inference using second-generation TDI in space-based detectors such as LISA, Taiji, and TianQin.
citing papers explorer
-
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 for MBHBs.
-
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.
-
Ringdown Signatures of Dehnen Dark Matter Halos: Fluid Modes and Detectability with Space-Based Detectors
Numerical ringdown waveforms for black holes in Dehnen dark matter profiles are generated and analyzed for detectability and parameter inference using second-generation TDI in space-based detectors such as LISA, Taiji, and TianQin.