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gr-qcastro-ph.HEastro-ph.IMhep-thastro-ph.COastro-ph.GAhep-ph

On Valency

published · living versions
W_y39rsyrw·v1 · currentpublished
Noisy pipelines II: Non-parametric Noise Inference for LISA
with Charlie Hoy, Joseph Bayley, Laura K. Nuttall
1 version

Preprints & journals

21 papers in the corpus · 2019–2026
Untangling Extreme Mass Ratio Inspirals and Massive Black Hole Binaries in LISA Data2610.27642v1 · Rishav Agrawal, Cl'emence Allietta, Ollie Burke et al.2026 · 0 citationsarXivon Valency
Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA2610.28726v1 · Ollie Burke, Joseph Bayley, Charlie Hoy et al.2026 · 0 citationsarXiv
Mind the second gap: The impact of gaps on noise and signal parameter inference2609.31550v1 · Ollie Burke, Federico Pozzoli, Martina Muratore et al.2026 · 0 citationsarXiv
An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis2606.20269v2 · Avi Vajpeyi, Giorgio Mentasti, Quentin Baghi et al.2026 · 0 citationsarXiv
First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals2512.16322v2 · Amin Boumerdassi, Matthew C. Edwards, Avi Vajpeyi et al.2025 · 0 citationsPhys. Rev. D 113, 104044 (2026)
Systematic errors in fast relativistic waveforms for Extreme Mass Ratio Inspirals2509.08875v2 · Hassan Khalvati, Philip Lynch, Ollie Burke et al.2025 · 5 citationsPhys. Rev. D 113, 084042 (2026)
The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes2506.09470v3 · Christian E. A. Chapman-Bird, Lorenzo Speri, Zachary Nasipak et al.2025 · 35 citationsPhys.Rev.D 112 (2025) 10, 104023
Mind the gap: addressing data gaps and assessing noise mismodeling in LISA2502.17426v2 · Ollie Burke, Sylvain Marsat, Jonathan R. Gair et al.2025 · 16 citationsarXiv
A note on the conversion of orbital angles for extreme mass ratio inspirals2411.04955v2 · Philip Lynch, Ollie Burke2024 · 3 citationsClass. Quantum Grav. 42 2025 167001
Test for LISA foreground Gaussianity and stationarity: extreme mass-ratio inspirals2410.08862v2 · Manuel Piarulli, Riccardo Buscicchio, Federico Pozzoli et al.2024 · 8 citationsPhys. Rev. D 111, 103047 (2025)
Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge2406.07607v2 · Lorenzo Speri, Susanna Barsanti, Andrea Maselli et al.2024 · 17 citationsarXiv
Accuracy Requirements: Assessing the Importance of First Post-Adiabatic Terms for Small-Mass-Ratio Binaries2310.08927v3 · Ollie Burke, Gabriel Andres Piovano, Niels Warburton et al.2023 · 2 citationsarXiv
Calibrating approximate Bayesian credible intervals of gravitational-wave parameters2310.06321v2 · Ruiting Mao, Jeong Eun Lee, Ollie Burke et al.2023 · 2 citationsPhys. Rev. D 109, 083002 (2024)
Strongly-Lensed Extreme Mass-ratio Inspirals2307.06722v3 · Martina Toscani, Ollie Burke, Chang Liu et al.2023 · 10 citationsarXiv
Noisy neighbours: inference biases from overlapping gravitational-wave signals2104.01897v2 · Andrea Antonelli, Ollie Burke, Jonathan R. Gair2021 · 41 citationsarXiv
Constraining the spin parameter of near-extremal black holes using LISA2010.05932v2 · Ollie Burke, Jonathan R. Gair, Joan Sim'on et al.2020 · 26 citationsPhys. Rev. D 102, 124054 (2020)
Transition from Inspiral to Plunge: A Complete Near-Extremal Trajectory and Associated Waveform1909.12846v2 · Ollie Burke, Jonathan R. Gair, Joan Sim'on2019 · 33 citationsPhys. Rev. D 101, 064026 (2020)
Career total: 41 works. 21 are in this corpus.

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