Nguyen describes the advancement with LEFTfield as comparable to transitioning from DESI to its successor, a leap that typically takes 10 to 20 years.

Nguyen, now a research fellow at the Kavli Institute in Tokyo, believes LEFTfield will significantly aid in understanding dark energy, dark matter, and general relativity.

A University of Michigan-led initiative is advancing cosmology by optimizing the analysis of cosmic maps that depict galaxy distribution and clustering.

The research introduces LEFTfield, a new computational framework that allows scientists to extract more information from galaxy maps than traditional methods.

LEFTfield processes cosmic map data as 3D grids, preserving detailed information about galaxy distribution and density, unlike standard methods that compress data and risk losing crucial insights.

Nguyen's team demonstrated that LEFTfield could improve the measurement of the cosmological parameter sigma-8 by a factor of 3.5 to 5.2, significantly enhancing the understanding of the universe's clumpiness.

This approach allows for a better understanding of non-Gaussian features of the universe, which are influenced by dark energy and dark matter.

Current tools like the Dark Energy Spectroscopic Instrument (DESI) are being utilized to explore dark energy and dark matter, but there is a pressing need for more advanced instruments to gain deeper insights.

He emphasizes the importance of maximizing data utility as larger telescopes generate more information that could be partially lost.

However, challenges remain in integrating LEFTfield with specific instruments and addressing noise-related issues before full implementation.

The findings from this research were published in Physical Review Letters and earned the 2024 Buchalter Cosmology Prize.

Cosmic maps are essential for investigating dark energy, dark matter, and other cosmic phenomena, which are critical to understanding the universe's structure.