Electrophilic dark matter with dark photon: from DAMPE to direct detection

The electron-positron excess reported by the DAMPE collaboration recently may be explained by an electrophilic dark matter (DM). A standard model singlet fermion may play the role of such a DM when it is stablized by some symmetries, such as a dark $U(1)_X^{}$ gauge symmetry, and dominantly annihilates into the electron-positron pairs through the exchange of a scalar mediator. The model, with appropriate Yukawa couplings, can well interpret the DAMPE excess. Naively one expects that in this type of models the DM-nucleon cross section should be small since there is no tree-level DM-quark interactions. We however find that at one-loop level, a testable DM-nucleon cross section can be induced for providing ways to test the electrophilic model. We also find that a $U(1)$ kinetic mixing can generate a sizable DM-nucleon cross section although the $U(1)_X^{}$ dark photon only has a negligible contribution to the DM annihilation. Depending on the signs of the mixing parameter, the dark photon can enhance/reduce the one-loop induced DM-nucleon cross section.Comment: 4 pages, typos are corrected, references are added as well as more discussions on direct detectio

Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations

Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions. In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality. We show that RQRCP algorithms can be as reliable as QRCP with failure probabilities exponentially decaying in oversampling size. We also analyze efficiency differences among different RQRCP algorithms. More importantly, we develop distributed memory implementations of RQRCP that are significantly better than QRCP implementations in ScaLAPACK. As a further development, we introduce the concept of and develop algorithms for computing spectrum-revealing QR factorizations for low-rank matrix approximations, and demonstrate their effectiveness against leading low-rank approximation methods in both theoretical and numerical reliability and efficiency.Comment: 11 pages, 14 figures, accepted by 2017 IEEE 24th International Conference on High Performance Computing (HiPC), awarded the best paper priz