Key research themes
1. How can machine learning and topological data analysis improve the extraction and modeling of the Cosmic Web's large-scale structure?
This research area focuses on developing sophisticated algorithms that can identify, extract, and probabilistically model the filamentary and web-like structures formed by galaxies and matter in the universe, known as the Cosmic Web. Traditional astrophysical statistics, such as correlation functions, often fail to capture the complexity of these large-scale arrangements. New computational methods aim to overcome challenges posed by high-dimensional data, noise, and hierarchical structures inherent in cosmological simulations and observations.
2. What role do multi-field dynamics and complex bias modeling play in advancing theoretical descriptions and observational constraints of large-scale structure formation?
This theme investigates the incorporation of multi-field scalar dark energy models and advanced biasing schemes in understanding the growth of cosmic structures. It covers both theoretical frameworks, such as multi-field dark energy models with curved field space trajectories and effective field theory (EFT) approaches to structure formation, and the precise measurement of bias parameters up to cubic order in Eulerian and Lagrangian frameworks. These developments address inadequacies in single-field or local bias models and refine parameter estimation for cosmological data sets.
3. How do observational cosmology techniques, including CMB measurements and 21 cm cosmology, interact with large-scale structure studies to constrain fundamental parameters like optical depth and the growth of structure?
This area explores the interplay between cosmic microwave background (CMB) analyses, large-scale structure observations, and novel probes such as the 21 cm hydrogen line to improve constraints on critical cosmological parameters, particularly the optical depth to reionization (τ) and amplitude of matter fluctuations (As). Combining these complementary datasets aims to overcome parameter degeneracies, refine the timeline of reionization, and enhance the precision of growth rate measurements, thereby advancing our understanding of the universe's evolution and underlying physics.