Bartlett, Chiarenza, Doeser, Leclercq
Prideaux-Ghee, Leclercq, Lavaux, Heavens, Jasche
Leclercq, Heavens
Leclercq, Faure, Lavaux, Wandelt, Jaffe, Heavens, Percival, Noûs
Lavaux, Jasche, Leclercq
Leclercq, Enzi, Jasche, Heavens
Leclercq
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.
Please check the full version of my CV, my list of publications and my list of communications.
3/3 How it works:
1️⃣ Infer the initial power spectrum using SELFI,
2️⃣ Use it to expose and diagnose misspecifications (galaxy bias, masks, redshift errors, etc.) before parameter inference,
3️⃣ Recycle simulations for efficient cosmological parameter estimation.
Tristan Hoellinger & Florent Leclercq, Diagnosing systematic effects using the inferred initial power spectrum, arxiv.org/abs/2412.04443.
Developed in @AquilaScience at @astroIAP.
2/3 The next generation of galaxy surveys (DESI, Euclid @ec_euclid, LSST @VRubinObs) will revolutionise #cosmology—but only if we tackle *systematic uncertainties*. Our new method builds on SELFI (arxiv.org/abs/1902.10149) & Implicit Likelihood Inference of Bayesian hierarchical models (arxiv.org/abs/2209.11057) to detect and avoid hidden model biases.
1/3 Diagnosing systematic effects in galaxy surveys before inferring cosmology just got easier (arxiv.org/abs/2412.04443). Using the inferred initial power spectrum, we uncover and correct model misspecifications that could bias (Ωm, σ8) by over 2σ. Our 2-step approach makes it possible to spot model errors before they mislead you. #cosmology #datascience
5/5 Tested on particle-mesh cosmological simulations, COCA reduces errors and requires fewer force evaluations than COLA, providing accurate density and velocity fields. COCA outperforms direct emulation in accuracy and robustness, even out of the range of training data.
Deaglan J. Bartlett, Marco Chiarenza, Ludvig Doeser & Florent Leclercq, COmoving Computer Acceleration (COCA): N-body simulations in an emulated frame of reference. Developed in @AquilaScience at @astroIAP.
In this post, we discuss a new algorithm to simulate the Universe on a variety of hardware architectures.
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
Likelihood-free inference provides a framework for performing Bayesian inference in cosmology, by replacing likelihood calculations with data model evaluations.
25-04-2019
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.
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.