Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30
Showing votes from 2015-09-08 11:30 to 2015-09-11 12:30 | Next meeting is Tuesday Jul 7th, 10:30 am.
Small-scale dark matter structure within the Milky Way is expected to affect pulsar timing. The change in gravitational potential induced by a dark matter halo passing near the line of sight to a pulsar would produce a varying delay in the light travel time of photons from the pulsar. Individual transits produce an effect that would either be too rare or too weak to be detected in 30-year pulsar observations. However, a population of dark matter subhalos would be expected to produce a detectable effect on the measured properties of pulsars if the subhalos constitute a significant fraction of the total halo mass. The effect is to increase the dispersion of measured period derivatives across the pulsar population. By statistical analysis of the ATNF pulsar catalogue, we place an upper limit on this dispersion of $\log \sigma_{\dot{P}} \leq -17.05$. We use this to place strong upper limits on the number density of ultracompact minihalos within the Milky Way. These limits are completely independent of the particle nature of dark matter.
Ultracompact Minihalos (UCMHs) have been proposed as a type of dark matter sub-structure seeded by large-amplitude primordial perturbations and topological defects. UCMHs are expected to survive to the present era, allowing constraints to be placed on their cosmic abundance using observations within our own Galaxy. Constraints on their number density can be linked to conditions in the early universe that impact structure formation, such as increased primordial power on small scales, generic weak non-Gaussianity, and the presence of cosmic strings. We use new constraints on the abundance of UCMHs from pulsar timing to place generalised limits on the parameters of each of these cosmological scenarios. At some scales, the limits are the strongest to date, exceeding those from dark matter annihilation. Our new limits have the added advantage of being independent of the particle nature of dark matter, as they are based only on gravitational effects.
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.