Machine Learning for High Energy Physics, on and off the Lattice

Session

Session 7

30 Sept 2021, 10:00

Aula Renzo Leonardi (ECT* - Trento)

15. Feature extraction of machine learning and phase transition point of Ising model

Shotaro Shiba Funai (Okinawa Institute of Science and Technology)

30/09/2021, 10:00

16. Deep learning and holographic QCD

Koji Hashimoto (Kyoto University, Physics Department)

30/09/2021, 10:50

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...