Application of radial basis functions neutral networks in spectral functions

Understand

The reconstruction of spectral function from correlation function in Euclidean space is a challenging task.

• In this paper, we employ the Machine Learning techniques in terms of the radial basis functions networks to reconstruct the spectral function from a finite number of correlation data.
• To test our method, we first generate one type of correlation data using a mock spectral function by mixing several Breit-Wigner propagators.
• We found that compared with other traditional methods, TSVD, Tikhonov, and MEM, our approach gives a continuous and unified reconstruction for both positive definite and negative spectral function, which is especially useful for studying the QCD phase transition.