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AI “Shallow" and “Deep" Learning of the Dark Universe

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Abstract: The Cosmological Constant (Lambda) + Cold Dark Matter model has survived many observational tests, subject to some ‘tensions’ in the Hubble Constant and the ‘clumpiness’ parameter. When shall we stop? Could the impressive data sets be used to address entirely new questions? In that spirit, we review recent results for cosmological parameters and mass mapping as well as unexpected outcomes from the Dark Energy Survey and other experiments. Some of the analyses use AI “Shallow” and “Deep” Learning approaches, with implications for the new surveys (e.g. DESI , Rubin-LSST and Euclid). We also discuss training of the next generation of scientists, with the example of UCL ’s Centre for Doctoral Training in Data Intensive Science.

This talk is part of the Institute of Astronomy Colloquia series.

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