I work with field-level inferences of the cosmic large-scale structure to study the physics of the Dark Universe. In particular, my research focuses on field-level inferences of:
- Galaxy clustering, photometric redshifts
- Peculiar velocities
- Galaxy intrinsic alignment
- Supernova clustering
- Environments of galaxies and supernovae
Next-generation photometric surveys will deliver redshifts with an uncertainty corresponding to as much as 300 Mpc or more. Reconstructing the large-scale structure from highly uncertain galaxy locations results in structures that are distorted. Here, we developed a method that mitigates such distortions, while also constraining individual galaxy redshift probability density functions. Our framework provides Markov Chain Monte Carlo realizations of the primordial and present-day large-scale structure as constrained by galaxy clustering, accounting for survey- and data-related uncertainties. Owing to the structure formation model we employ, we reconstruct the structure formation history of the large-scale structure, as well as filaments. We provide constraints on the large-scale structure on scales much smaller than the original redshift uncertainty.
Galaxy intrinsic alignment
Galaxy shapes are not random, but align with the large-scale structure. This effect, known as intrinsic alignment, is linked to a multitude of cosmic phenomena. It provides constraints on galaxy formation and evolution, the initial conditions of the Universe, redshift-space distortions, as well as the gravitational wave background, among others. Constraints on intrinsic alignment further allow the decontamination of the weak lensing signal from intrinsic galaxy shape correlations which bias cosmological estimates. In Tsaprazi et al. 2021b, we find 4σ evidence of intrinsic alignment within a luminous red galaxy sample, by cross-correlating with three-dimensional tidal fields constrained with spectroscopic galaxy observations. Field-level approaches, like the one we present in our study, facilitate the modeling of nonlinear corrections to intrinsic alignment models and gravitational evolution, as well as joint inferences of intrinsic alignment and weak lensing.
You can find a summary of common notations and concepts on galaxy intrinsic alignment in Lamman et al. 2024.
Standard candle clustering
An endeavor that has been challenging cosmologists is to obtain an accurate picture of how the universe looks on large scales, as this picture contains invaluable information that can help us better understand how gravity works on the biggest scales and the origin of cosmic acceleration. To this end, galaxy surveys are employed and allow us to map the cosmic structure. However, at large distances, galaxies become too dim to detect, whereas supernovae are bright point sources that can be detected very far, and thus uncover the locations of galaxies that would not have been resolved otherwise. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time will detect tens of thousands of supernovae per year, a detection rate which would in principle allow us to exploit supernova explosions to map the cosmic structure at large distances. In Tsaprazi et al. 2021a we studied the large-scale environments of supernovae and found that supernovae can be used to map the high-redshift universe. A byproduct of our findings was that supernovae are biased tracers of density, in contrast with traditional clustering assumptions in supernova surveys.
Recent evidence suggests that the magnitude of the local bulk flow may be inconsistent with the ΛCDM cosmology. In Tsaprazi & Tsagas 2020 we performed a study of large-scale bulk flows in the context of General Relativity and Linear Perturbation Theory to probe the full kinematics of linear peculiar velocities by accounting for relative motion effects. In doing so, we found that the relativistic energy flux of a bulk flow can contribute to its measured amplitude. On these grounds, the reported tension in the local bulk flow and the ΛCDM prediction, could be accounted for by neglected relativistic effects.