At small scales, the three-dimensional large-scale structure contains a wealth of cosmological information which cannot be trivially extracted due to the non-linear dynamical evolution of the density field. In order to optimally extract information from upcoming, but also existing, surveys, I am interested in the description of structure formation in this regime.
In 2013, I proposed a fast method to improve the performance of Lagrangian perturbation theory in the mildly non-linear regime, based on a remapping of the approximately-evolved density field, using information extracted from N-body simulations (Leclercq et al. 2013).
I am also the author of Simbelmynë [SEEM-bale-mü-nay], a flexible cosmological code for forward-modelling of the large-scale structure. Simbelmynë features an efficient implementation the temporal and spatial tCOLA (COmoving Lagrangian Acceleration, Tassev et al. 2013) and sCOLA (Tassev et al. 2015) schemes for very efficient dark matter simulations. It also includes a variety of tools to create synthetic observations of galaxies, and to analyse the dynamics of the cosmic web (phase-space field estimators, cosmic web classifiers).Generation of synthetic galaxy observations with Simbelmynë. The code first generates a dark matter density field (left panel) using a non-linear gravitational model. The galaxy density field (middle panel) takes into account galaxy bias and redshift-space distortions. From this, observations are simulated (right panel) using the survey geometry and a prescription for selection effects and missing observations.
As an end-to-end generative process for galaxy surveys data, the Simbelmynë model can be conveniently written in the form of a Bayesian Hierarchical Model, and it is particularly suitable for use with likelihood-free inference methods.