September 27, 2021 to October 1, 2021
ECT* - Trento
Europe/Rome timezone

Deep learning and holographic QCD

Sep 30, 2021, 10:50 AM
50m
Aula Renzo Leonardi (ECT* - Trento)

Aula Renzo Leonardi

ECT* - Trento

Strada delle Tabarelle, 286 38123 - Villazzano (TN) Italy

Speaker

Koji Hashimoto (Kyoto University, Physics Department)

Description

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

Presentation materials