• Bayesian forward modelling of cosmic shear data; N. Porqueres, A. Heavens, D. Mortlock, G. Lavaux; MNRAS 502, 3035-3044; arXiv:2011.07722


  • Impacts of the physical data model on the forward inference of initial conditions from biased tracers; N. M. Nguyen, F. Schmidt, G. Lavaux, J. Jasche; arXiv:2011.06587
  • A hierarchical field-level inference approach to reconstruction from sparse Lyman-α forest data; N. Porqueres, O. Hahn, J. Jasche, G. Lavaux; A&A 642, A139; arxiV:2005.12928
  • Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration; F. Leclercq, B. Faure, G. Lavaux, B. D. Wandelt, A. H. Jaffe, A. F. Heavens, W. J. Percival; arXiv:2003.04925
  • Setting the Stage: Structures from Gaussian Random Fields; Till Sawala, Adrian Jenkins, Stuart McAlpine, Jens Jasche, Guilhem Lavaux, Peter H. Johansson, Carlos S. Frenk; arXiv:2003.04321
  • Bayesian delensing delight: sampling-based inference of the primordial CMB andgravitational lensing; M. Millea, E. Anderes, Benjamin D. Wandelt; arXiv:2002.00965


  • Velocity debiasing for Hubble constant measurements from standard sirens; S. Mukherjee, G. Lavaux, F. R. Bouchet, J. Jasche, B. D. Wandelt, S. M. Nissanke, F. Leclercq, K. Hotokezaka; arXiv:1909.08627
  • Neural physical engines for inferring the halo mass distribution function; T. Charnock, G. Lavaux, B. D. Wandelt, S. Sarma Boruah, J. Jasche, M. J. Hudson; arXiv:1909.06379
  • Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data; G. Lavaux, J. Jasche, F. Leclercq; arXiv:1909.06396
  • The Quijote simulations; Francisco Villaescusa-Navarro ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana MariaDelgado, Doogesh Kodi Ramanah, Tom Charnock, Elena Giusarma, Yin Li, Erwan Allys, Antoine Brochard, Chi-Ting Chiang, Siyu He, Alice Pisani, Andrej Obuljen, Yu Feng, EmanueleCastorina, Gabriella Contardo, Christina D. Kreisch, Andrina Nicola, Roman Scoccimarro, LiciaVerde, Matteo Viel, Shirley Ho, Stephane Mallat, Benjamin Wandelt, David N. Spergel; arXiv: 1909.05273
  • Cosmology Inference from Biased Tracers using the EFT-based Likelihood; F. Elsner, F. Schmidt, J. Jasche, G. Lavaux, M. Nguyen; arXiv:1906.07143
  • * Physical Bayesian modelling of the non-linear matter distribution: new insights into the Nearby Universe; J. Jasche & G. Lavaux; A&A, 2019; arXiv: 1806.11117
  • A rigorous EFT-based forward model for large-scale structure; Schmidt, Fabian; Elsner, Franz; Jasche, Jens; Nguyen, Nhat Minh; Lavaux, Guilhem; JCAP, 2019 ;arXiV: 1808.02002
  • The peculiar velocity field up to z~0.05 by forward-modeling Cosmicflows-3 data; R. Graziani, H. M. Courtois, G. Lavaux, Y. Hoffman, R. B. Tully, Y. Copin, D. Pomarède; MNRAS, 2019; arxiv: 1901.01818
  • Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys; Kodi Ramanah, Doogesh; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin D.; A&A, 2019; arXiV: 1808.07496
  • Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys; N. Porqueres, D. Kodi Ramanah, J. Jasche, G. Lavaux; A&A, 2019; arXiV: 1812.05113
  • Painting halos from 3D dark matter fields using Wasserstein mapping networks; D. Kodi Ramanah, T. Charnock, G. Lavaux; arXiv: 1903.10524



  • Wiener filter reloaded: fast signal reconstruction without preconditioning; D. Kodi Ramanah, G. Lavaux, B. Wandelt; MNRAS; 2017; arXiV 1702.08852
  • Bayesian power-spectrum inference with foreground and target contamination treatment; J. Jasche & G. Lavaux; A&A, 2017; arXiV 1706.08971
  • The phase-space structure of nearby dark matter as constrained by the SDSS; Leclercq, Florent; Jasche, Jens; Lavaux, Guilhem; Wandelt, Benjamin; Percival, Will; JCAP, 2017; arXiV 1601.00093