In this paper, we demonstrate a new method for fitting galaxy profiles which makes use of the full multi-wavelength data provided by modern large optical-near-infrared imaging surveys. We present a new version of GALAPAGOS, which utilises a recently-developed multiwavelength version of GALFIT, and enables the automated measurement of wavelengthdependent Sérsic profile parameters for very large samples of galaxies. Our new technique is extensively tested to assess the reliability of both pieces of software, GALFIT and GALAPAGOS on both real ugrizY JHK imaging data from the GAMA survey and simulated data made to the same specifications. We find that fitting galaxy light profiles with multi-wavelength data increases the stability and accuracy of the measured parameters, and hence produces more complete and meaningful multi-wavelength photometry than has been available previously. The improvement is particularly significant for magnitudes in low S/N bands and for structural parameters like half-light radius r e and Sérsic index n for which a prior is used by constraining these parameters to a polynomial as a function of wavelength. This allows the fitting routines to push the magnitude of galaxies for which sensible values can be derived to fainter limits. The technique utilises a smooth transition of galaxy parameters with wavelength, creating more physically meaningful transitions than single-band fitting and allows accurate interpolation between passbands, perfect for derivation of rest-frame values.

The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful when probability model assumptions are satisfied. We have implemented a recursive Bayesian algorithm for reconstructing hand movement from neurons in the motor cortex. The algorithm uses a recently developed numerical method known as "particle filtering" and follows the same general strategy as that used by Brown et al. to reconstruct the path of a foraging rat from hippocampal place cells. We investigated the method in a numerical simulation study in which neural firing rate was assumed to be positive, but otherwise a linear function of movement velocity, and preferred directions were not uniformly distributed. In terms of mean-squared error, the approach was approximately 10 times more efficient than the PV algorithm and 5 times more efficient than OLE. Thus use of recursive Bayesian decoding can achieve the accuracy of the PV algorithm (or OLE) with approximately 10 times (or 5 times) fewer neurons. The method was also used to reconstruct hand movement in an ellipse-drawing task from 258 cells in the ventral premotor cortex. Recursive Bayesian decoding was again more efficient than the PV and OLE methods, by factors of roughly seven and three, respectively.

We investigate the relationship between colour and structure within galaxies using a large, volume-limited sample of bright, low-redshift galaxies with optical-near-infrared imaging from the GAMA survey. We fit single-component, wavelength-dependent, elliptical Sérsic models to all passbands simultaneously, using software developed by the MegaMorph project. Dividing our sample by n and colour, the recovered wavelength variations in effective radius (R e ) and Sérsic index (n) reveal the internal structure, and hence formation history, of different types of galaxies. All these trends depend on n; some have an additional dependence on galaxy colour. Late-type galaxies (n r < 2.5) show a dramatic increase in Sérsic index with wavelength. This might be a result of their two-component (bulge-disk) nature, though stellar population gradients within each component and dust attenuation are likely to play a role. All galaxies show a substantial decrease in R e with wavelength. This is strongest for early-types (n r > 2.5), even though they maintain constant n with wavelength, revealing that ellipticals are a superimposition of different stellar populations associated with multiple collapse and merging events. Processes leading to structures with larger R e must be associated with lower metallicity or younger stellar populations. This appears to rule out the formation of young cores through dissipative gas accretion as an important mechanism in the recent lives of luminous elliptical galaxies.

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Bulge-disc decomposition is a valuable tool for understanding galaxies. However, achieving robust measurements of component properties is difficult, even with high quality imaging, and it becomes even more so with the imaging typical of large surveys.In this paper we consider the advantages of a new, multi-band approach to galaxy fitting. We perform automated bulge-disc decompositions for 163 nearby galaxies, by simultaneously fitting multiple images taken in five photometric filters. We show that we are able to recover structural measurements that agree well with various other works, and confirm a number of key results. We additionally use our results to illustrate the link between total Sérsic index and bulge-disc structure, and demonstrate that the visually classification of lenticular galaxies is strongly dependent on the inclination of their disc component.By simulating the same set of galaxies as they would appear if observed at a range of redshifts, we are able to study the behaviour of bulge-disc decompositions as data quality diminishes. We examine how our multi-band fits perform, and compare to the results of more conventional, single-band methods. Multi-band fitting improves the measurement of all parameters, but particularly the bulge-to total flux ratio and component colours. We therefore encourage the use of this approach with future surveys.