Speaker
Description
Modern cosmology faces a data problem. Progress is no longer limited by the volume of observations, but by the ability to process, interpret, and control biases in massive datasets. As surveys push measurements of large-scale structure to percent-level precision, tensions with the standard cosmological model have emerged, many of which may reflect systematic effects rather than new physics. CLONES (Constrained LOcal & Nesting Environment Simulations) introduces digital twins of the Local Universe designed to reproduce the observed cosmic environment and explicitly control environmental and observational systematics. By combining large-scale simulations, advanced statistical inference, machine learning, and high-performance computing, CLONES turns cosmological datasets into bias-controlled laboratories. This framework enables bias-aware inference of dark matter, dark energy, and structure formation parameters, demonstrating how interdisciplinary data-driven approaches are essential for robust cosmology in the data-intensive era.