An investigation into the Multiple Optimised Parameter Estimation and Data compression algorithm

We investigate the use of the Multiple Optimised Parameter Estimation and Data compression algorithm (MOPED) for data compression and faster evaluation of likelihood functions. Since MOPED only guarantees maintaining the Fisher matrix of the likelihood at a chosen point, multimodal and some degenerate distributions will present a problem. We present examples of scenarios in which MOPED does faithfully represent the true likelihood but also cases in which it does not. Through these examples, we aim to define a set of criteria for which MOPED will accurately represent the likelihood and hence may be used to obtain a significant reduction in the time needed to calculate it. These criteria may involve the evaluation of the full likelihood function for comparison.Comment: 5 pages, 8 figures; corrections and additions to match version published in MNRAS Letters; added reference to published versio

SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

We present the first public release of our generic neural network training algorithm, called SkyNet. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SkyNet uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimisation using a regularised variant of Newton's method, where the level of regularisation is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimise using standard backpropagation techniques. SkyNet employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SkyNet are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SkyNet software, which is implemented in standard ANSI C and fully parallelised using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.Comment: 19 pages, 21 figures, 7 tables; this version is re-submission to MNRAS in response to referee comments; software available at http://www.mrao.cam.ac.uk/software/skynet

Electrochemical energy storage subsystems study, volume 1

The effects on life cycle costs (LCC) of major design and performance technology parameters for multi kW LEO and GEO energy storage subsystems using NiCd and NiH2 batteries and fuel cell/electrolysis cell devices were examined. Design, performance and LCC dynamic models are developed based on mission and system/subsystem requirements and existing or derived physical and cost data relationships. The models define baseline designs and costs. The major design and performance parameters are each varied to determine their influence on LCC around the baseline values

Laser cooling of a nanomechanical resonator mode to its quantum ground state

We show that it is possible to cool a nanomechanical resonator mode to its ground state. The proposed technique is based on resonant laser excitation of a phonon sideband of an embedded quantum dot. The strength of the sideband coupling is determined directly by the difference between the electron-phonon couplings of the initial and final states of the quantum dot optical transition. Possible applications of the technique we describe include generation of non-classical states of mechanical motion.Comment: 5 pages, 3 figures, revtex

Analysis of a Hubble Space Telescope Search for Red Dwarfs: Limits on Baryonic Matter in the Galactic Halo

We re-examine a deep {\it Hubble Space Telescope} pencil-beam search for red dwarfs, stars just massive enough to burn Hydrogen. The authors of this search (Bahcall, Flynn, Gould \& Kirhakos 1994) found that red dwarfs make up less than 6\% of the galactic halo. First, we extrapolate this result to include brown dwarfs, stars not quite massive enough to burn hydrogen; we assume a $1/{\cal M}$ mass function. Then the total mass of red dwarfs and brown dwarfs is $\leq$18\% of the halo. This result is consistent with microlensing results assuming a popular halo model. However, using new stellar models and parallax observations of low mass, low metallicity stars, we obtain much tighter bounds on low mass stars. We find the halo red dwarf density to be $<1\%$ of the halo, while our best estimate of this value is 0.14-0.37\%. Thus our estimate of the halo mass density of red dwarfs drops to 16-40 times less than the reported result of Bahcall et al (1994). For a $1/{\cal M}$ mass function, this suggests a total density of red dwarfs and brown dwarfs of $\sim$0.25-0.67\% of the halo, \ie , (0.9-2.5)\times 10^9\msun out to 50 kpc. Such a low result would conflict with microlensing estimates by the \macho\ group (Alcock \etal 1995a,b).Comment: 13 pages, 2 figures. Figure one only available via fax or snail-mail To be published in ApJL. fig. 2 now available in postscript. Some minor changes in dealing with disk forground. Some cosmetic changes. Updated reference

Direct Detection of Giant Close-In Planets Around the Source Stars of Caustic-Crossing Microlensing Events

We propose a direct method to detect close-in giant planets orbiting stars in the Galactic bulge. This method uses caustic-crossing binary microlensing events discovered by survey teams monitoring the bulge to measure light from a planet orbiting the source star. When the planet crosses the caustic, it is more magnified than the source star; its light is magnified by two orders of magnitude for Jupiter size planets. If the planet is a giant close to the star, it may be bright enough to make a significant deviation in the light curve of the star. Detection of this deviation requires intensive monitoring of the microlensing light curve using a 10-meter class telescope for a few hours after the caustic. This is the only method yet proposed to directly detect close-in planets around stars outside the solar neighborhood.Comment: 4 pages, 2 figures. Submitted to ApJ Letter