Field-Based Physical Inference From Peculiar Velocity Tracers
On the accuracy and precision of correlation functions and field-level inference in cosmology
Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration
Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data
Primordial power spectrum and cosmology from black-box galaxy surveys
Bayesian optimisation for likelihood-free cosmological inference
The phase-space structure of nearby dark matter as constrained by the SDSS
My name is Florent Leclercq. I am a research scientist (Chargé de recherche CNRS) at the Institut d'Astrophysique de Paris (IAP). I hold an interdisciplinary position at the interface between astrophysics (Institut national des sciences de l'Univers, INSU) and information science (Institut des sciences de l'information et de leurs interactions, INS2I). I work in the fields of numerical cosmology and artificial intelligence, 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. I am also a member of the Euclid Consortium, where I currently co-lead the "Additional Probes" work package of the Galaxy Clustering Science Working Group.
My current research interests are related to the study of the cosmological large-scale structure using statistical inference, machine learning, and high-performance computing tools. I am particularly interested constraining cosmology from the large-scale structure, in the initial conditions from which it originates, its formation history and the description of the cosmic web.
New master internship and PhD studentship opportunity: High-performance information extraction from cosmic web probes in the INFOCW project. See Supervision and mentoring.
Pages updated: pySELFI (v2.0) and Lotka-Volterra simulator.
Submitted paper: Higher-order statistics of the large-scale structure from photometric redshifts.
New paper: Simulation-based inference of Bayesian hierarchical models while checking for model misspecification (MaxEnt'22 proceedings).
Dealing with systematic effects: the issue of robustness to model misspecification
28-11-2023, Debating the potential of machine learning in astronomical surveys #2 conference, Institut d'Astrophysique de Paris, Paris, France
Evolution of cosmological simulations over the last 50 years
I recently scanned the literature for the purpose of following and plotting the number of particles used in \(N\)-body simulations over the last five decades.08-04-2020
Algorithms for likelihood-free cosmological data analysis
Likelihood-free inference provides a framework for performing Bayesian inference in cosmology, by replacing likelihood calculations with data model evaluations.25-04-2019
Public data and software
pySELFI is a publicly-available implementation of the Simulator Expansion for Likelihood-Free Inference algorithm, allowing primordial power spectrum inference from black-box galaxy surveys.
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.