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What if human analysis, combined with machine learning, could advance the study of the universe? The U.S. Department of Energy awarded Fermilab scientist Brian Nord a $2.5 million Early Career Research Award to explore that possibility. Nord has envisioned a new hybrid data-analysis method to undertake the project. It integrates the strengths of artificial intelligence and interpretations of statistics in ways that could potentially advance the studies of cosmology.

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Fermilab scientist Kirsty Duffy responds to some of the many wonderful questions viewers have left in the comments. Plus, a guest visit from fellow Fermilab science communicator Don Lincoln.

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The Dark Energy Survey collaboration has created the largest ever maps of the distribution and shapes of galaxies, tracing both ordinary and dark matter in the universe out to a distance of over 7 billion light years. The analysis, which includes the first three years of data from the survey, is consistent with predictions from the current best model of the universe, the standard cosmological model. Nevertheless, there remain hints from DES and other experiments that matter in the current universe is a few percent less clumpy than predicted.

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DESI will capture and study the light from tens of millions of galaxies and other distant objects to better understand our universe and the properties of dark energy. The formal start of DESI’s five-year survey follows a four-month trial run of its custom instrumentation that captured 4-million spectra of galaxies — more than the combined output of all previous spectroscopic surveys. Fermilab has contributed multiple components to the international collaboration led by Berkeley Lab.