5,159 research outputs found
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
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