APS : Leveraging time-dependent instrumental noise for the LISA stochastic gravitational wave background analysis

Alvey, James; Domcke, Valerie; Bhardwaj, Uddipta; Pieroni, Mauro; Weniger, Christoph

doi:10.1103/PhysRevD.111.102006

Article
Report number	arXiv:2408.00832 ; CERN-TH-2024-127
Title	Leveraging time-dependent instrumental noise for the LISA stochastic gravitational wave background analysis
Related title	Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis
Author(s)	Alvey, James (U. Amsterdam, GRAPPA) ; Bhardwaj, Uddipta (U. Amsterdam, GRAPPA ; U. Amsterdam, IHEF) ; Domcke, Valerie (CERN) ; Pieroni, Mauro (CERN) ; Weniger, Christoph (U. Amsterdam, GRAPPA)
Publication	2025-05-15
Imprint	2024-08-01
Number of pages	15
Note	12 pages, 5 figures. saqqara available at https://github.com/peregrine-gw/saqqara, GW_response available at https://github.com/Mauropieroni/GW_response - v2: Version accepted by PRD
In:	Phys. Rev. D 111 (2025) 102006
DOI	10.1103/PhysRevD.111.102006 (publication)
Subject category	hep-ph ; Particle Physics - Phenomenology ; astro-ph.IM ; Astrophysics and Astronomy ; astro-ph.CO ; Astrophysics and Astronomy ; gr-qc ; General Relativity and Cosmology
Abstract	Variations in the instrumental noise of the Laser Interferometer Space Antenna (LISA) over time are expected as a result of e.g. scheduled satellite operations or unscheduled glitches. We demonstrate that these fluctuations can be leveraged to improve the sensitivity to stochastic gravitational wave backgrounds (SGWBs) compared to the stationary noise scenario. This requires optimal use of data segments with downward noise fluctuations, and thus a data analysis pipeline capable of analysing and combining shorter time segments of mission data. We propose that simulation based inference is well suited for this challenge. In an approximate, but state-of-the-art, modeling setup, we show by comparison with Fisher Information Matrix estimates that the optimal information gain can be achieved in practice.
Copyright/License	publication: © 2025-2026 American Physical Society preprint: (License: CC BY 4.0)

$\emph{Left:} FIM forecasts for the two-parameter signal model. The pink curves in the corner plot and the inset indicate the result for non-varying noise parameters. The black curves and the black histograms highlight the results for time-dependent noise parameters, sampled according to Sec.~\ref{sec:modeling}. The fiducial parameters taken here are given by: $\alpha = -11$, $\gamma = 0$, $A = 3$, $P = 15$, with $N_s = 100$ data segments, $f_\mathrm{min} = 3 \times 10^{-5} \, \mathrm{Hz}$, $f_\mathrm{max} = 5 \times 10^{-1}\,\mathrm{Hz}$, and $T_s = 11.5 \, \mathrm{days}$. \emph{Right:} Demonstration that in the case where the noise parameters are genuinely stationary across the dataset, we should expect the same sensitivity on the signal parameters $\alpha$ and $\gamma$ if we analyze the dataset assuming only one pair of noise parameters $(A = 3, P = 15)$, or allow them to vary across each segment $(A_\tau = 3, P_\tau = 15)$ for $\tau = 1...N_s$. Naturally, the constraints on the parameters covering the full dataset (blue curves/contours) ($A, P$) are tighter than the constraints on the parameters in any one segment $(A_\tau, P_\tau)$ (pink curves/contours). In the upper right panel, we show that nonetheless, the constraints and correlations on the signal parameters are identical to the left-hand scenario.$

$MCMC vs. SBI Comparison. Comparison between MCMC (black contours and histogram) and SBI (orange contours) at the single segment level, evaluated on the same noise realization. The MCMC analysis uses the Whittle likelihood, evaluated on the full frequency grid. The blue lines indicate the injection values for the signal parameters $\alpha$ (amplitude) and $\gamma$ (slope).$ Show more plots

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Record created 2024-08-06, last modified 2025-05-28

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