Our missionWe are an international collaboration of researchers interested in developing and applying cutting-edge statistical inference techniques to study the content and properties of our Universe. We embrace the latest innovations in information theory and artificial intelligence to optimally extract physical information from data and use derived results to facilitate new discoveries.
Our latest results
Field-level inference on galaxy intrinsic alignment
Elliptical galaxies tend to align with the large scale structures for two reasons: through intrinsic deformations and tilting during their formation or gravitational weak lensing. Here, we constrain the intrinsic alignment for luminous red giants in the SDSS3-BOSS sample, using 3D tidal fields constrained with forward modeling of SDSS3-BOSS data. We have found 4σ evidence of intrinsic alignment at 20 Mpc/h.
Constraining dark matter annihilation and decay in large-scale structures
The identification of dark matter is a crucial task of modern physics. We present a full-sky, field-level search for dark matter annihilation and decay in the large-scale structure of the nearby universe, exploiting more information than conventional analyses targetting specific objects. We find no evidence for such effects, placing new constraints on the rates of dark matter interactions.
Bayesian Inference of Initial Conditions from Non-Linear Cosmic Structures using Field-Level Emulators
Leveraging a highly accurate physics model essential for next-generation cosmological analysis typically involves significant computational demands. We here propose a solution by integrating a machine learning-based emulator into the BORG algorithm for effective sampling of cosmic initial conditions from non-linear cosmic structures.