Deep learning-based statistical noise reduction for multidimensional spectral data

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.Comment: 8 pages, 8 figure

Quantum electron liquid and its possible phase transition

Purely quantum electron systems exhibit intriguing correlated electronic phases by virtue of quantum fluctuations in addition to electron-electron interactions. To realize such quantum electron systems, a key ingredient is dense electrons decoupled from other degrees of freedom. Here, we report the discovery of a pure quantum electron liquid, which spreads up to ~ 3 {\AA} in the vacuum on the surface of electride crystal. An extremely high electron density and its weak hybridisation with buried atomic orbitals evidence the quantum and pure nature of electrons, that exhibit a polarized liquid phase as demonstrated by our spin-dependent measurement. Further, upon enhancing the electron correlation strength, the dynamics of quantum electrons changes to that of non-Fermi liquid along with an anomalous band deformation, suggestive of a transition to a hexatic liquid crystal phase. Our findings cultivate the frontier of quantum electron systems, and serve as a platform for exploring correlated electronic phases in a pure fashion.Comment: 29 pages, 4 figures, 10 extended data figure

Strain-controlled evolution of electronic structure indicating topological phase transition in the quasi-one-dimensional superconductor TaSe3

© 2022 American Physical Society.We report the signature of a strain-controlled topological phase transition in the electronic structure of a quasi-one-dimensional superconductor TaSe3. Using angle-resolved photoemission spectroscopy and first-principles calculation, TaSe3 is identified to be in a weak topological insulator phase which has topologically nontrivial surface states only at the allowed planes. Under uniaxial tensile strain, a Dirac point and the topological surface state emerge on the originally forbidden (101¯) plane, which demonstrates the transition to a strong topological insulator phase. Our results accomplish the experimental realization of possible topological insulating phases in TaSe3 and highlight the possibility of coupling the superconductivity with two distinct topological insulating phases in a controllable manner.11Nsciescopu