Date : February 14, 2023, 14:00 CET

Koji Hashimoto (Kyoto University)


Machine learning the bulk in AdS/CFT


Bulk reconstruction in AdS/CFT correspondence is a key idea revealing the
mechanism of it, and various methods were proposed to solve the inverse
problem. We use deep learning and identify the neural network as the
emergent geometry, to reconstruct the bulk. The lattice QCD data such
as chiral condensate, hadron spectra or Wilson loop is used as input data
to reconstruct the emergent geometry of the bulk. The requirement that the
bulk geometry is a consistent solution of an Einstein-dilaton system determines
the bulk dilaton potential backwards, to complete the reconstruction program.
We demonstrate the determination of the bulk system from QCD
lattice/experiment data.