6,296 research outputs found
Probing dynamical spacetimes with gravitational waves
This decade will see the first direct detections of gravitational waves by
observatories such as Advanced LIGO and Virgo. Among the prime sources are
coalescences of binary neutron stars and black holes, which are ideal probes of
dynamical spacetime. This will herald a new era in the empirical study of
gravitation. For the first time, we will have access to the genuinely
strong-field dynamics, where low-energy imprints of quantum gravity may well
show up. In addition, we will be able to search for effects which might only
make their presence known at large distance scales, such as the ones that
gravitational waves must traverse in going from source to observer. Finally,
coalescing binaries can be used as cosmic distance markers, to study the
large-scale structure and evolution of the Universe.
With the advanced detector era fast approaching, concrete data analysis
algorithms are being developed to look for deviations from general relativity
in signals from coalescing binaries, taking into account the noisy detector
output as well as the expectation that most sources will be near the threshold
of detectability. Similarly, several practical methods have been proposed to
use them for cosmology. We explain the state of the art, including the
obstacles that still need to be overcome in order to make optimal use of the
signals that will be detected. Although the emphasis will be on
second-generation observatories, we will also discuss some of the science that
could be done with future third-generation ground-based facilities such as
Einstein Telescope, as well as with space-based detectors.Comment: 38 pages, 9 figures. Book chapter for the Springer Handbook of
Spacetime (Springer Verlag, to appear in 2013
On the Complexity and Approximation of Binary Evidence in Lifted Inference
Lifted inference algorithms exploit symmetries in probabilistic models to
speed up inference. They show impressive performance when calculating
unconditional probabilities in relational models, but often resort to
non-lifted inference when computing conditional probabilities. The reason is
that conditioning on evidence breaks many of the model's symmetries, which can
preempt standard lifting techniques. Recent theoretical results show, for
example, that conditioning on evidence which corresponds to binary relations is
#P-hard, suggesting that no lifting is to be expected in the worst case. In
this paper, we balance this negative result by identifying the Boolean rank of
the evidence as a key parameter for characterizing the complexity of
conditioning in lifted inference. In particular, we show that conditioning on
binary evidence with bounded Boolean rank is efficient. This opens up the
possibility of approximating evidence by a low-rank Boolean matrix
factorization, which we investigate both theoretically and empirically.Comment: To appear in Advances in Neural Information Processing Systems 26
(NIPS), Lake Tahoe, USA, December 201
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