19,220 research outputs found
A Bayesian approach to discrete object detection in astronomical datasets
A Bayesian approach is presented for detecting and characterising the signal
from discrete objects embedded in a diffuse background. The approach centres
around the evaluation of the posterior distribution for the parameters of the
discrete objects, given the observed data, and defines the
theoretically-optimal procedure for parametrised object detection. Two
alternative strategies are investigated: the simultaneous detection of all the
discrete objects in the dataset, and the iterative detection of objects. In
both cases, the parameter space characterising the object(s) is explored using
Markov-Chain Monte-Carlo sampling. For the iterative detection of objects,
another approach is to locate the global maximum of the posterior at each
iteration using a simulated annealing downhill simplex algorithm. The
techniques are applied to a two-dimensional toy problem consisting of Gaussian
objects embedded in uncorrelated pixel noise. A cosmological illustration of
the iterative approach is also presented, in which the thermal and kinetic
Sunyaev-Zel'dovich effects from clusters of galaxies are detected in microwave
maps dominated by emission from primordial cosmic microwave background
anisotropies.Comment: 20 pages, 12 figures, accepted by MNRAS; contains some additional
material in response to referee's comment
Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima
The optimization of three problems with high dimensionality and many local minima are investigated
under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO
package, and QNSTOP, a new algorithm developed at Indiana University
Improved decision support for engine-in-the-loop experimental design optimization
Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms.
After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio.
In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions.
As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design.
The decision support methodology is combined with standard optimization and search methods – namely gradient descent and simulated annealing – in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experi- mentation
Catching Super Massive Black Hole Binaries Without a Net
The gravitational wave signals from coalescing Supermassive Black Hole
Binaries are prime targets for the Laser Interferometer Space Antenna (LISA).
With optimal data processing techniques, the LISA observatory should be able to
detect black hole mergers anywhere in the Universe. The challenge is to find
ways to dig the signals out of a combination of instrument noise and the large
foreground from stellar mass binaries in our own galaxy. The standard procedure
of matched filtering against a grid of templates can be computationally
prohibitive, especially when the black holes are spinning or the mass ratio is
large. Here we develop an alternative approach based on Metropolis-Hastings
sampling and simulated annealing that is orders of magnitude cheaper than a
grid search. We demonstrate our approach on simulated LISA data streams that
contain the signals from binary systems of Schwarzschild Black Holes, embedded
in instrument noise and a foreground containing 26 million galactic binaries.
The search algorithm is able to accurately recover the 9 parameters that
describe the black hole binary without first having to remove any of the bright
foreground sources, even when the black hole system has low signal-to-noise.Comment: 4 pages, 3 figures, Refined search algorithm, added low SNR exampl
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and
probabilistic programming rely heavily on sampling from generally intractable
probability distributions. There is increasing interest in the potential
advantages of using quantum computing technologies as sampling engines to speed
up these tasks or to make them more effective. However, some pressing
challenges in state-of-the-art quantum annealers have to be overcome before we
can assess their actual performance. The sparse connectivity, resulting from
the local interaction between quantum bits in physical hardware
implementations, is considered the most severe limitation to the quality of
constructing powerful generative unsupervised machine-learning models. Here we
use embedding techniques to add redundancy to data sets, allowing us to
increase the modeling capacity of quantum annealers. We illustrate our findings
by training hardware-embedded graphical models on a binarized data set of
handwritten digits and two synthetic data sets in experiments with up to 940
quantum bits. Our model can be trained in quantum hardware without full
knowledge of the effective parameters specifying the corresponding quantum
Gibbs-like distribution; therefore, this approach avoids the need to infer the
effective temperature at each iteration, speeding up learning; it also
mitigates the effect of noise in the control parameters, making it robust to
deviations from the reference Gibbs distribution. Our approach demonstrates the
feasibility of using quantum annealers for implementing generative models, and
it provides a suitable framework for benchmarking these quantum technologies on
machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys.
Rev.
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