The Impact of Realistic Data Artifacts on Gravitational Wave Signal Extraction in LISA Data
Mitigating the impact of data gaps and instrumental glitches is crucial for accurate gravitational wave signal extraction in realistic LISA data, particularly for low signal-to-noise ratio sources.
The Impact of Tidal Forces and Mass Transfer on the Double White Dwarf Population Observable by LISA
Tidal forces and mass transfer significantly impact the observed gravitational wave signals from double white dwarf binaries, necessitating flexible data analysis tools for LISA to accurately characterize this population and extract astrophysical insights.
LISA 은하계 전경에 대한 템플릿 기반 비등방성 분석
본 논문에서는 LISA 데이터에서 은하계 전경의 비등방성을 정확하게 특성화하고 이를 LISA의 다른 과학적 목표를 위한 노이즈 소스로서 효과적으로 처리하기 위한 새로운 베이지안 분석 프레임워크를 제시합니다.
Bayesian Analysis of the LISA Galactic Foreground Using Astrophyiscally-Motivated Anisotropic Templates
Accurately characterizing the anisotropic Galactic foreground produced by unresolved white dwarf binaries in the Milky Way is crucial for LISA's success, and leveraging astrophysically-motivated templates in a Bayesian framework offers a computationally efficient and accurate method for achieving this.
Prototype Pipeline for Recovering Stochastic Gravitational Wave Backgrounds in LISA Global Fit Residuals
Accurately identifying stochastic gravitational wave backgrounds (SGWBs) in LISA data requires careful consideration of non-stationarity and non-gaussianity in global fit residuals, particularly from the galactic foreground.
為 LISA 快速推斷大質量黑洞雙星調整新穎框架
本文介紹了 LISA-RIFT，這是一種經過修改的 RIFT 參數估計演算法，用於快速準確地推斷大質量黑洞雙星 (MBHB) 訊號，並探討了其在模擬 LISA 資料集上的應用。
Rapid Inference of Massive Black Hole Binaries for LISA Using an Adapted RIFT Framework
This paper introduces LISA-RIFT, an adapted version of the RIFT parameter estimation algorithm, designed for rapid and accurate inference of massive black hole binary (MBHB) parameters from LISA data, demonstrating its efficiency and accuracy on both simulated and LDC datasets.
評估不等量噪音和前景建模對 LISA 重建 SGWB 的影響
在模擬的 LISA 數據分析中，適度的不均勻噪音對宇宙學 SGWB 重建的影響很小，但不準確的前景建模會顯著降低參數估計的準確性，突出了準確建模和減去天體物理學前景以表徵可能的宇宙學成分的重要性。
LISAを用いたSGWB再構成におけるノイズの不均一性と前景モデリングの影響評価
LISAを用いた宇宙論的重力波背景放射（SGWB）の再構成において、ノイズの不均一性と前景モデリングの不正確さが検出精度に与える影響を調査した結果、ノイズのばらつきは軽微な影響にととどまる一方、前景モデリングの精度が低い場合は宇宙論的信号の再構成が著しく劣化することが明らかになった。
Assessing the Impact of Unequal Noise Levels and Uncertain Astrophisical Foreground Modeling on Cosmological Stochastic Gravitational Wave Background Reconstruction with LISA
Accurately modeling and subtracting astrophysical foregrounds is crucial for LISA to successfully detect and characterize cosmological stochastic gravitational wave backgrounds, as uncertainties in these foregrounds can significantly impact signal reconstruction, even more so than moderate variations in noise levels across LISA arms.
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