Chiral field theory of 0−+0^{-+} glueball

A chiral field theory of $0^{-+}$ glueball is presented. By adding a $0^{-+}$ glueball field to a successful Lagrangian of chiral field theory of pseudoscalar, vector, and axial-vector mesons, the Lagrangian of this theory is constructed. The couplings between the pseodoscalar glueball field and mesons are via U(1) anomaly revealed. Qualitative study of the physical processes of the $0^{-+}$ glueball of $m=1.405\textrm{GeV}$ is presented. The theoretical predictions can be used to identify the $0^{-+}$ glueball.Comment: 29 page

Static de-Sitter Black Holes Abhor Charged Scalar Hair

We prove a no charged scalar hair theorem for static black holes in de-Sitter spacetime in the region between the event horizon and the cosmological horizon. The proof does not depend on the assumption of spherical symmetry. It allows for general non-minimal coupling functions of the scalar field to gravity and electromagnetic fields, and for higher curvature term corrections to Einstein gravity. The extension to other asympitotic spacetimes is applicable by requiring appropriate boundary conditions. Our result excludes the possibility for spontaneous scalarization of charged scalar around static charged de-Sitter black holes.Comment: 8 pages, 0 figure

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

Energy dependence of pion in-medium effects on \pi^-/\pi^+ ratio in heavy-ion collisions

Within the framework of a thermal model with its parameters fitted to the results from an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model, we study the pion in-medium effect on the charged-pion ratio in heavy-ion collisions at various energies. We find that due to the cancellation between the effects from pion-nucleon s-wave and p-wave interactions in nuclear medium, the \pi^-/\pi^+ ratio generally decreases after including the pion in-medium effect. The effect is larger at lower collision energies as a result of narrower pion spectral functions at lower temperatures.Comment: 4 pages, 4 figures, 1 table, minor modifications, version to appear in Physical Review