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Accueil > Thèses, Stages, Formation et Enseignement > Propositions de thèses 2026 > First joint Bayesian field-level inference of density and velocity fields using DESI galaxies and ZTF SN Ia
First joint Bayesian field-level inference of density and velocity fields using DESI galaxies and ZTF SN Ia
par Tristan Beau - 10 novembre
Titre : First joint Bayesian field-level inference of density and velocity fields using DESI galaxies and ZTF SN Ia
Directrice/directeur de thèse : Pauline Zarrouk
Groupe d’accueil : Cosmology
Collaboration : DESI
Description :
DESI (Dark Energy Spectroscopic Instrument) is the first next-generation galaxy survey dedicated to tracking dark energy through galaxy clustering, with data collection beginning in 2021 and scheduled to conclude at the end of 2028. The collaboration has already published several series of new results, highlighting a preference for dynamic dark energy with a significance ranging from 3σ when DESI results are combined with those from the CMB (Planck, ACT) to 4.2σ when Type Ia supernovae (SN Ia) data are included (DESI Collaboration 2025, DESI DR2 Results II). DESI has also demonstrated, through measurements of structure growth, that general relativity remains valid on cosmic scales (DESI Collaboration 2024 V, VII), with the LPNHE DESI team playing a leading role.
However, the standard analyses used to obtain these results rely on information compression using two-point statistical tools, which do not exploit the non-Gaussian and non-linear information contained in today’s density field. To extract all the information from galaxy surveys, a Bayesian inference must be performed directly at the field level, and the most advanced algorithm for this method is BORG (Jasche & Wandelt 2013 ; Jasche et al. 2015, Jasche & Lavaux 2019).
Moreover, to obtain the best constraints on dark energy and gravity in the recent Universe (z < 0.1), dominated by dark energy, DESI galaxy clustering data can be complemented with measurements of galaxy peculiar velocities via the DESI Peculiar Velocity Survey or through residuals in the Hubble diagram of ZTF SN Ia.
The goal of this thesis is to develop field-level inference using DESI and ZTF data to reconstruct the density and velocity fields jointly and constrain cosmology, particularly gravity and the nature of dark energy. To achieve this, the PhD student will first become familiar with the BORG algorithm and the work already carried out by the LPNHE team on ZTF SN Ia and DESI data alone. The student will then extend this work to DESI Peculiar Velocity data and compare the results between the two surveys. Work on accounting for selection effects, as well as astrophysical and instrumental effects that could bias measurements, will be conducted and tested using simulated data before applying the method to real data. Finally, the core of the thesis will involve developing, testing, and validating the joint inference of the density and velocity fields from DESI and ZTF SN Ia data to obtain the best constraints on the nature of dark energy and gravity in the local Universe (z < 0.1).
Lieu(x) de travail : LPNHE
Déplacements éventuels : Etats-Unis, Stockholm
Stage proposé avant la thèse : Oui
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