Vector Dark Matter Detection using the Quantum Jump of Atoms

The hidden sector U(1) vector bosons created from inflationary fluctuations can be a substantial fraction of dark matter if their mass is around $10^{-5}$eV. The creation mechanism makes the vector bosons' energy spectral density $\rho_{cdm}/\Delta E$ very high. Therefore, the dark electric dipole transition rate in atoms is boosted if the energy gap between atomic states equals the mass of the vector bosons. By using the Zeeman effect, the energy gap between the 2S state and the 2P state in hydrogen atoms or hydrogen like ions can be tuned. The $2S$ state can be populated with electrons due to its relatively long life, which is about $1/7$s. When the energy gap between the semi-ground $2S$ state and the 2P state matches the mass of the cosmic vector bosons, induced transitions occur and the 2P state subsequently decays into the 1S state. The $2P\to1S$ decay emitted Lyman-$\alpha$ photons can then be registered. The choices of target atoms depend on the experimental facilities and the mass ranges of the vector bosons. Because the mass of the vector boson is connected to the inflation scale, the proposed experiment may provide a probe to inflation.Comment: 5 pages, 3 figures; references added; matches version published in PL

J/ψJ/\psi Production by Magnetic Excitation of ηc\eta_c

We compute the probability of $J/\psi$ production from the interaction between $\eta_c$ and the strong magnetic field generated in relativistic heavy ion collisions. The computation is first carried out in the heavy quark effective model, in which the M1 radiative transition is considered. Then we investigate the transition in the framework of non-relativistic heavy hadron chiral perturbation theory and show that the polarization of $J/\psi$ produced by this process is parallel to the direction of magnetic field and thus perpendicular to the reaction plane. The transition probability obtained in both approaches is of order $2\times 10^{-4}$.Comment: 7 pages, minor modification, references adde

From Query to Usable Code: An Analysis of Stack Overflow Code Snippets

Enriched by natural language texts, Stack Overflow code snippets are an invaluable code-centric knowledge base of small units of source code. Besides being useful for software developers, these annotated snippets can potentially serve as the basis for automated tools that provide working code solutions to specific natural language queries. With the goal of developing automated tools with the Stack Overflow snippets and surrounding text, this paper investigates the following questions: (1) How usable are the Stack Overflow code snippets? and (2) When using text search engines for matching on the natural language questions and answers around the snippets, what percentage of the top results contain usable code snippets? A total of 3M code snippets are analyzed across four languages: C\#, Java, JavaScript, and Python. Python and JavaScript proved to be the languages for which the most code snippets are usable. Conversely, Java and C\# proved to be the languages with the lowest usability rate. Further qualitative analysis on usable Python snippets shows the characteristics of the answers that solve the original question. Finally, we use Google search to investigate the alignment of usability and the natural language annotations around code snippets, and explore how to make snippets in Stack Overflow an adequate base for future automatic program generation.Comment: 13th IEEE/ACM International Conference on Mining Software Repositories, 11 page