Bayesian optimisation for likelihood-free cosmological inference
The phase-space structure of nearby dark matter as constrained by the SDSS
Comparing cosmic web classifiers using information theory
Cosmic web-type classification using decision theory
Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey
Dark matter voids in the SDSS galaxy survey
Past and present cosmic structure in the SDSS DR7 main sample
My name is Florent Leclercq. I am a Research Fellow at the Imperial Centre for Inference and Cosmology. I work in the fields of theoretical and observational cosmology, focusing in particular on the analysis of galaxy survey data. I have been a member of the Aquila Consortium since it was created, in 2016.
My current research interests are related to the study of the cosmological large-scale structure using statistical data analysis tools. I am particularly interested in the initial conditions from which the large-scale structure originates, its formation history and the description of the cosmic web.
Bayesian large-scale structure inference and cosmic web analysis
Inférence bayésienne et analyse des grandes structures de l'UniversMy PhD
Simbelmynë is a publicly-available simulator to generate synthetic galaxy survey data. A more detailed description of the code can be found on its homepage, hosted on this website.Public data and software
The BORG SDSS data release
This website hosts the BORG SDSS data release, a set of data products that follow a chrono-cosmographic analysis of the three-dimensional large-scale structure of the nearby Universe.Public data and software
Charting the unseen skyNovember 2017
Cosmologists from the U.K., France and Germany have come up with new maps of how dark matter moves throughout the universe.Continue reading