Macroeconomics modelling on UK GDP growth by neural computing

This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed

MegaMorph: classifying galaxy morphology using multi-wavelength S\'ersic profile fits

Aims. This work investigates the potential of using the wavelength-dependence of galaxy structural parameters (S\'ersic index, n, and effective radius, Re) to separate galaxies into distinct types. Methods. A sample of nearby galaxies with reliable visual morphologies is considered, for which we measure structural parameters by fitting multi-wavelength single-S\'ersic models. Additionally, we use a set of artificially redshifted galaxies to test how these classifiers behave when the signal-to-noise decreases. Results. We show that the wavelength-dependence of n may be employed to separate visually-classified early- and late-type galaxies, in a manner similar to the use of colour and n. Furthermore, we find that the wavelength variation of n can recover galaxies that are misclassified by these other morphological proxies. Roughly half of the spiral galaxies that contaminate an early-type sample selected using (u-r) versus n can be correctly identified as late-types by N, the ratio of n measured in two different bands. Using a set of artificially-redshifted images, we show that this technique remains effective up to z ~ 0.1. N can therefore be used to achieve purer samples of early-types and more complete samples of late-types than using a colour-n cut alone. We also study the suitability of R, the ratio of Re in two different bands, as a morphological classifier, but find that the average sizes of both early- and late-type galaxies do not change substantially over optical wavelengths.Comment: 6 pages, 2 figures, 2 tables, Accepted for publication in A&

The rapid transition from star-formation to AGN dominated rest-frame UV light at z ~ 4

With the advent of deep optical-to-near-infrared extragalactic imaging on the degree scale, samples of high-redshift sources are being selected that contain both bright star-forming (SF) galaxies and faint active galactic nuclei (AGN). In this study we investigate the transition between SF and AGN-dominated systems at $z \simeq 4$ in the rest-frame UV. We find a rapid transition to AGN-dominated sources bright-ward of $M_{\rm UV} \simeq -23.2$. The effect is observed in the rest-frame UV morphology and size-luminosity relation, where extended clumpy systems become point-source dominated, and also in the available spectra for the sample. These results allow us to derive the rest-frame UV luminosity function for the SF and AGN-dominated sub-samples. We find the SF-dominated LF is best fit with a double-power law, with a lensed Schechter function being unable to explain the existence of extremely luminous SF galaxies at $M_{\rm UV} \simeq -23.5$. If we identify AGN-dominated sources according to a point-source morphology criterion we recover the relatively flat faint-end slope of the AGN LF determined in previous studies. If we instead separate the LF according to the current spectroscopic AGN fraction, we find a steeper faint-end slope of $\alpha = -1.83 \pm 0.11$. Using a simple model to predict the rest-frame AGN LF from the $z = 4$ galaxy LF we find that the increasing impact of host galaxy light on the measured morphology of faint AGN can explain our observations.Comment: 17 pages, 11 figures, 2 tables. Accepted to MNRA