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Addressing data gaps and assessing noise mismodeling in LISA

Ollie Burke ^1,2,3,4,*, Sylvain Marsat ^2,†, Jonathan R. Gair ^3,4, and Michael L. Katz ^3,5

¹School of Physics and Astronomy, University of Glasgow, University Avenue, Glasgow, G12 8QQ, United Kingdom
²Laboratoire des 2 Infinis-Toulouse (L2IT-IN2P3), Université de Toulouse, CNRS, UPS, F-31062 Toulouse Cedex 9, France
³Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, Potsdam-Golm 14476, Germany
⁴School of Mathematics, University of Edinburgh, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
⁵NASA Marshall Space Flight Center, Huntsville, Alabama 35811, USA

^*Contact author: ollie.burke@glasgow.ac.uk
^†Contact author: sylvain.marsat@l2it.in2p3.fr

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Articles Citing

Addressing data gaps and assessing noise mismodeling in LISA

Flexible Gravitational-Wave Parameter Estimation with Transformers

Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

Fast premerger detection of massive black-hole binaries in LISA based on time-frequency excess power

Quantifying the scientific potential of intermediate and extreme mass ratio inspirals with the Laser Interferometer Space Antenna

Global time-frequency search for stellar-mass binary black holes in LISA

Sequential simulation-based inference for extreme mass ratio inspirals

Handling data gaps for the next generation of gravitational-wave observatories

Systematic errors in fast relativistic waveforms for extreme mass ratio inspirals

First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals

Gravitational-wave parameter estimation to the Moon and back: Massive binaries and the case of GW231123

Impact of missing data on the construction of LISA time-delay-interferometry Michelson variables

Gotta light? Illuminating AGN discs with LISA EMRIs

Simulation-based Inference of Massive Black Hole Binaries using Sequential Neural Likelihood

Temporal context and noise conditioning in neural gravitational-wave inpainting: An architecture ablation study for sub-second strain gaps

Identifying Galactic Binary Systems of Neutron Stars and Black Holes with LISA

Accelerating the time-domain LISA response model with central finite differences and hybridization techniques

Accelerating LISA inference with Gaussian processes

Enhancing Taiji’s parameter estimation under nonstationarity: A time-frequency domain framework for Galactic binaries and instrumental noises

Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: A mock data challenge

Simulation-based population inference of LISA’s Galactic binaries: Bypassing the global fit

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