Jianan Liu, Avi Vajpeyi, Renate Meyer, Jeung Eun Lee, Patricio Maturana-Russel (2026). Variational Bayesian Inference for the Spectral Structure of LISA Noise. arXiv e-prints.
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Nazeela Aimen, Patricio Maturana-Russel, Avi Vajpeyi, Nelson Christensen, Renate Meyer (2026). Bayesian P-spline recovery of stochastic gravitational-wave backgrounds in LISA. arXiv e-prints.
Preprint
Yixuan Liu, Renate Meyer, Nelson Christensen, Jeung Eun Lee, Jianan Liu, Patricio Maturana-Russel, Avi Vajpeyi (2026). Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors. arXiv e-prints.
Preprint
Avi Vajpeyi, Renate Meyer, Patricio Maturana-Russel, Jianan Liu (2026). Multivariate Bayesian P-spline estimation of spectral density matrices, with application to LISA TDI noise. arXiv e-prints.
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Avi Vajpeyi, Giorgio Mentasti, Quentin Baghi, Ollie Burke, Lorenzo Speri (2026). An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis. arXiv e-prints.
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Amin Boumerdassi, Matthew C. Edwards, Avi Vajpeyi, Ollie Burke (2026). First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals. Physical Review D.
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El Mehdi Zahraoui, Patricio Maturana-Russel, Avi Vajpeyi, Willem van Straten, Renate Meyer, Sergei Gulyaev (2026). Enhancing evidence estimation through informed probability density approximation. Physical Review D.
Preprint DOI
Nazeela Aimen, Patricio Maturana-Russel, Avi Vajpeyi, Nelson Christensen, Renate Meyer (2026). Bayesian power spectral density estimation for LISA noise based on penalized splines with a parametric boost. Physical Review D.
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Tousif Islam, Avi Vajpeyi, Feroz H. Shaik, Carl-Johan Haster, Vijay Varma, Scott E. Field, Jacob Lange, Richard O’Shaughnessy, Rory Smith (2025). Analysis of GWTC-3 with fully precessing numerical relativity surrogate models. Physical Review D.
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Jianan Liu, Avi Vajpeyi, Renate Meyer, Kamiel Janssens, Jeung Eun Lee, Patricio Maturana-Russel, Nelson Christensen, Yixuan Liu (2025). Variational inference for correlated gravitational wave detector network noise. Physical Review D.
URL DOI
Daniel Williams, John Veitch, Maria Luisa Chiofalo, Patricia Schmidt, Rhiannon P. Udall, Avi Vajpeyi, Charlie Hoy (2023). Asimov: A framework for coordinating parameter estimation workflows. Journal of Open Source Software.
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Avi Vajpeyi, Rory Smith, Eric Thrane (2023). Deep Follow-up for Gravitational-wave Inference: A Case Study with GW151226. The Astrophysical Journal.
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Avi Vajpeyi, Rory Smith, Eric Thrane, Gregory Ashton, Thomas Alford, Sierra Garza, Maximiliano Isi, Jonah Kanner, T J Massinger, Liting Xiao (2022). A follow-up on intermediate-mass black hole candidates in the second LIGO–Virgo observing run with the Bayes Coherence Ratio. Monthly Notices of the Royal Astronomical Society.
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B McKernan, K E S Ford, T Callister, W M Farr, R O’Shaughnessy, R Smith, E Thrane, A Vajpeyi (2022). LIGO–Virgo correlations between mass ratio and effective inspiral spin: testing the active galactic nuclei channel. Monthly Notices of the Royal Astronomical Society.
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Avi Vajpeyi, Eric Thrane, Rory Smith, Barry McKernan, K. E. Saavik Ford (2022). Measuring the Properties of Active Galactic Nuclei Disks with Gravitational Waves. The Astrophysical Journal.
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Daniel Williams, Duncan Macleod, Avi Vajpeyi, James Clark (2021). transientlunatic/asimov: v0.3.2. Zenodo.
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Juan Calderón Bustillo, Nicolas Sanchis-Gual, Alejandro Torres-Forné, José A. Font, Avi Vajpeyi, Rory Smith, Carlos Herdeiro, Eugen Radu, Samson H. W. Leong (2021). GW190521 as a Merger of Proca Stars: A Potential New Vector Boson of 8.7 × 10⁻¹³ eV. Physical Review Letters.
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Rory J. E. Smith, Gregory Ashton, Avi Vajpeyi, Colm Talbot (2020). Massively parallel Bayesian inference for transient gravitational-wave astronomy. Monthly Notices of the Royal Astronomical Society.
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Maximiliano Isi, Rory Smith, Salvatore Vitale, T. J. Massinger, Jonah Kanner, Avi Vajpeyi (2018). Enhancing confidence in the detection of gravitational waves from compact binaries using signal coherence. Physical Review D.
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