Phase transitions in a holographic s+p model with backreaction

In a previous paper (arXiv:1309.2204, JHEP 1311 (2013) 087), we present a holographic s+p superconductor model with a scalar triplet charged under an SU(2) gauge field in the bulk. We also study the competition and coexistence of the s-wave and p-wave orders in the probe limit. In this work we continue to study the model by considering the full back-reaction The model shows a rich phase structure and various condensate behaviors such as the "n-type" and "u-type" ones, which are also known as reentrant phase transitions in condensed matter physics. The phase transitions to the p-wave phase or s+p coexisting phase become first order in strong back-reaction cases. In these first order phase transitions, the free energy curve always forms a swallow tail shape, in which the unstable s+p solution can also play an important role. The phase diagrams of this model are given in terms of the dimension of the scalar order and the temperature in the cases of eight different values of the back reaction parameter, which show that the region for the s+p coexisting phase is enlarged with a small or medium back reaction parameter, but is reduced in the strong back-reaction cases.Comment: 15 pages(two-column), 9 figure

Evolution of High-Energy Particle Distribution in Mature Shell-Type Supernova Remnants

Multi-wavelength observations of mature supernova remnants (SNRs), especially with recent advances in gamma-ray astronomy, make it possible to constrain energy distribution of energetic particles within these remnants. In consideration of the SNR origin of Galactic cosmic rays and physics related to particle acceleration and radiative processes, we use a simple one-zone model to fit the nonthermal emission spectra of three shell-type SNRs located within 2 degrees on the sky: RX J1713.7-3946, CTB 37B, and CTB 37A. Although radio images of these three sources all show a shell (or half-shell) structure, their radio, X-ray, and gamma-ray spectra are quite different, offering an ideal case to explore evolution of energetic particle distribution in SNRs. Our spectral fitting shows that 1) the particle distribution becomes harder with aging of these SNRs, implying a continuous acceleration process, and the particle distributions of CTB 37A and CTB 37B in the GeV range are harder than the hardest distribution that can be produced at a shock via the linear diffusive shock particle acceleration process, so spatial transport may play a role; 2) the energy loss timescale of electrons at the high-energy cutoff due to synchrotron radiation appears to be always a bit (within a factor of a few) shorter than the age of the corresponding remnant, which also requires continuous particle acceleration; 3) double power-law distributions are needed to fit the spectra of CTB 37B and CTB 37A, which may be attributed to shock interaction with molecular clouds.Comment: Accepted for publication in The Astrophysical Journal, 11 pages, 3 figures, 1 tabl

Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of Procgen games and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.Comment: 23 pages, 16 figure

Tackling Visual Control via Multi-View Exploration Maximization

We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks. To the best of our knowledge, MEM is the first approach that combines multi-view representation learning and intrinsic reward-driven exploration in reinforcement learning (RL). More specifically, MEM first extracts the specific and shared information of multi-view observations to form high-quality features before performing RL on the learned features, enabling the agent to fully comprehend the environment and yield better actions. Furthermore, MEM transforms the multi-view features into intrinsic rewards based on entropy maximization to encourage exploration. As a result, MEM can significantly promote the sample-efficiency and generalization ability of the RL agent, facilitating solving real-world problems with high-dimensional observations and spare-reward space. We evaluate MEM on various tasks from DeepMind Control Suite and Procgen games. Extensive simulation results demonstrate that MEM can achieve superior performance and outperform the benchmarking schemes with simple architecture and higher efficiency.Comment: 21 pages, 9 figure