Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques

A general problem in quantum mechanics is the reconstruction of eigenstate wave functions from measured data. In the case of molecular aggregates, information about excitonic eigenstates is vitally important to understand their optical and transport properties. Here we show that from spatially resolved near field spectra it is possible to reconstruct the underlying delocalized aggregate eigenfunctions. Although this high-dimensional nonlinear problem defies standard numerical or analytical approaches, we have found that it can be solved using a convolutional neural network. For both one-dimensional and two-dimensional aggregates we find that the reconstruction is robust to various types of disorder and noise

Associated production of the top-pions and single top at hadron colliders

In the context of topcolor assisted technicolor(TC2) models, we study the production of the top-pions $\pi_{t}^{0,\pm}$ with single top quark via the processes $p\bar{p} \to t\pi_{t}^{0}+X$ and $p\bar{p} \to t\pi_{t}^{\pm}+X$, and discuss the possibility of detecting these new particles at Tevatron and LHC. We find that it is very difficult to observe the signals of these particles via these processes at Tevatron, while the neutral and charged top-pions $\pi_{t}^{0}$ and $\pi_{t}^{\pm}$ can be detecting via considering the same sign top pair $tt\bar{c}$ event and the $tt\bar{b}$ (or $t\bar{t}b$) event at LHC, respectively.Comment: latex files,14 pages, 7 figures. Accepted for publication in Phys. Rev.