Galaxy populations from Deep ISO Surveys

I discuss some of the main extra-galactic field surveys which have been undertaken by the Infrared Space Observatory (ISO). I review the findings from the source counts analysies and then examine some of the more recent detailed investigations into the explicit nature of the populations that make up these source counts.Comment: 8 pages including 6 figures in 7 ps file

Statistical constraints on the IR galaxy number counts and cosmic IR background from the Spitzer GOODS survey

We perform fluctuation analyses on the data from the Spitzer GOODS survey (epoch one) in the Hubble Deep Field North (HDF-N). We fit a parameterised power-law number count model of the form dN/dS = N_o S^{-\delta} to data from each of the four Spitzer IRAC bands, using Markov Chain Monte Carlo (MCMC) sampling to explore the posterior probability distribution in each case. We obtain best-fit reduced chi-squared values of (3.43 0.86 1.14 1.13) in the four IRAC bands. From this analysis we determine the likely differential faint source counts down to $10^{-8} Jy$, over two orders of magnitude in flux fainter than has been previously determined. From these constrained number count models, we estimate a lower bound on the contribution to the Infra-Red (IR) background light arising from faint galaxies. We estimate the total integrated background IR light in the Spitzer GOODS HDF-N field due to faint sources. By adding the estimates of integrated light given by Fazio et al (2004), we calculate the total integrated background light in the four IRAC bands. We compare our 3.6 micron results with previous background estimates in similar bands and conclude that, subject to our assumptions about the noise characteristics, our analyses are able to account for the vast majority of the 3.6 micron background. Our analyses are sensitive to a number of potential systematic effects; we discuss our assumptions with regards to noise characteristics, flux calibration and flat-fielding artifacts.Comment: 10 pages; 29 figures (Figure added); correction made to flux scale of Fazio points in Figure

A new approach to multiwavelength associations of astronomical sources

One of the biggest problems faced by current and next-generation astronomical surveys is trying to produce large numbers of accurate cross-identifications across a range of wavelength regimes with varying data quality and positional uncertainty. Until recently, simple spatial 'nearest neighbour' associations have been sufficient for most applications. However as advances in instrumentation allow more sensitive images to be made, the rapid increase in the source density has meant that source confusion across multiple wavelengths is a serious problem. The field of far-IR and sub-mm astronomy has been particularly hampered by such problems. The poor angular resolution of current sub-mm and far-IR instruments is such that in a lot of cases, there are multiple plausible counterparts for each source at other wavelengths. Here we present a new automated method of producing associations between sources at different wavelengths using a combination of spatial and spectral energy distribution information set in a Bayesian framework. Testing of the technique is performed on both simulated catalogues of sources from GaLICS and real data from multiwavelength observations of the Subaru-XMM Deep Field. It is found that a single figure of merit, the Bayes factor, can be effectively used to describe the confidence in the match. Further applications of this technique to future Herschel data sets are discusse

Bayesian methods of astronomical source extraction

We present two new source extraction methods, based on Bayesian model selection and using the Bayesian Information Criterion (BIC). The first is a source detection filter, able to simultaneously detect point sources and estimate the image background. The second is an advanced photometry technique, which measures the flux, position (to sub-pixel accuracy), local background and point spread function. We apply the source detection filter to simulated Herschel-SPIRE data and show the filter's ability to both detect point sources and also simultaneously estimate the image background. We use the photometry method to analyse a simple simulated image containing a source of unknown flux, position and point spread function; we not only accurately measure these parameters, but also determine their uncertainties (using Markov-Chain Monte Carlo sampling). The method also characterises the nature of the source (distinguishing between a point source and extended source). We demonstrate the effect of including additional prior knowledge. Prior knowledge of the point spread function increase the precision of the flux measurement, while prior knowledge of the background has onlya small impact. In the presence of higher noise levels, we show that prior positional knowledge (such as might arise from a strong detection in another waveband) allows us to accurately measure the source flux even when the source is too faint to be detected directly. These methods are incorporated in SUSSEXtractor, the source extraction pipeline for the forthcoming Akari FIS far-infrared all-sky survey. They are also implemented in a stand-alone, beta-version public tool that can be obtained at http://astronomy.sussex.ac.uk/$\sim$rss23/sourceMiner\_v0.1.2.0.tar.gzComment: Accepted for publication by ApJ (this version compiled used emulateapj.cls

Principal Component Analysis and Radiative Transfer modelling of Spitzer IRS Spectra of Ultra Luminous Infrared Galaxies

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterise the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilise the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. We further investigate the principal components (PCs) of ULIRGs derived in Wang et al.(2011). We quantitatively show that five PCs is optimal for describing the IRS spectra. These five components (PC1-PC5) and the mean spectrum provide a template basis set that reproduces spectra of all z<0.35 ULIRGs within the noise. For comparison, the spectra are also modelled with a combination of radiative transfer models of both starbursts and the dusty torus surrounding active galactic nuclei. The five PCs typically provide better fits than the models. We argue that the radiative transfer models require a colder dust component and have difficulty in modelling strong PAH features. Aided by the models we also interpret the physical processes that the principal components represent. The third principal component is shown to indicate the nature of the dominant power source, while PC1 is related to the inclination of the AGN torus. Finally, we use the 5 PCs to define a new classification scheme using 5D Gaussian mixtures modelling and trained on widely used optical classifications. The five PCs, average spectra for the four classifications and the code to classify objects are made available at: http://www.phys.susx.ac.uk/~pdh21/PCA/Comment: 11 pages, 12 figures, accepted for publication in MNRA