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Register a new userQSO redshift estimates from optical, near-infrared and ultraviolet colours
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A simple estimate of the photometric redshift would prove invaluable to forthcoming continuum surveys on the next generation of large radio telescopes, as well as mitigating the existing bias towards the most optically bright sources. While there is a well known correlation between the near-infrared K-band magnitude and redshift for galaxies, we find the K-z relation to break down for samples dominated by quasi-stellar objects (QSOs). We hypothesise that this is due to the additional contribution to the near-infrared flux by the active galactic nucleus (AGN), and, as such, the K-band magnitude can only provide a lower limit to the redshift in the case of active galactic nuclei, which will dominate the radio surveys. From a large optical dataset, we find a tight relationship between the rest-frame (U-K)/(W2-FUV) colour ratio and spectroscopic redshift over a sample of 17,000 sources, spanning z ~ 0.1 - 5. Using the observed-frame ratios of (U K)/(W2-FUV) for redshifts of z > 1, (I-W2)/(W3-U) for 1 < z < 3 and (I-W2.5)/(W4-R) for z > 3, where W2.5 is the 8.0 micron magnitude and the appropriate redshift ranges are estimated from the W2 (4.5 micron) magnitude, we find this to be a robust photometric redshift estimator for quasars. We suggest that the rest-frame U-K colour traces the excess flux from the AGN over this wide range of redshifts, although the W2-FUV colour is required to break the degeneracy.
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Several methods exist to convert near-infrared (NIR) stellar observations into extinction maps. We present a new method based on NIR multiband observations. The method uses a discretised version of the distribution of intrinsic stellar colours. A number of variations of the basic method are tested, and the results are compared to NICER calculations. When photometric errors are large, the results are close to those of NICER method but some advantages can be seen when the distribution of intrinsic colours cannot be described well with a single covariance matrix. A priori information about relative column density variations at sub-beam scales can result in a significant increase in accuracy. The results may be further improved by considering the magnitude dependence of the intrinsic colours. Thus, the new methods are useful mostly when photometric errors are small, the distribution of intrinsic colours is well known, or one has prior knowledge of the small-scale structures.
We use empirical techniques to interpret the near-infrared colours of a sample of 5800 galaxies drawn from Sloan Digital Sky Survey (SDSS) main spectroscopic sample with YJHK photometry from the UK Infrared Deep Sky Survey (UKIDSS) data release one. We study correlations between near-IR colours measured within SDSS fibre and physical parameters derived from the spectra. These parameters include specific star formation rate, stellar age, metallicity and dust attenuation. All correlations are analyzed for samples of galaxies that are closely matched in redshift, in stellar mass and in concentration index. Whereas more strongly star-forming galaxies have bluer optical colours, the opposite is true at near-IR wavelengths -- galaxies with higher specific star formation rate have redder near-IR colours. This result agrees qualitatively with the predictions of models in which Thermally Pulsing Asymptotic Giant Branch (TP-AGB) stars dominate the H and K-band light of a galaxy following a burst of star formation. We also find a surprisingly strong correlation between the near-IR colours of star-forming galaxies and their dust attenuation as measured from the Balmer decrement. Unlike optical colours, however, near-IR colours exhibit very little dependence on galaxy inclination. This suggests that the correlation of near-IR colours with dust attenuation arises because TP-AGB stars are the main source of dust in the galaxy. Finally, we compare the near-IR colours of the galaxies in our sample to the predictions of three different stellar population models: the Bruzual & Charlot 2003 model, a preliminary version of a new model under development by Charlot & Bruzual, which includes a new prescription for AGB star evolution, and the Maraston 2005 model.
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2%) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.
Using the Horizon-AGN hydrodynamical simulation and self-organising maps (SOMs), we show how to compress the complex data structure of a cosmological simulation into a 2-d grid which is much easier to analyse. We first verify the tight correlation between the observed 0.3$!-!5mu$m broad-band colours of Horizon-AGN galaxies and their high-resolution spectra. The correlation is found to extend to physical properties such as redshift, stellar mass, and star formation rate (SFR). This direct mapping from colour to physical parameter space is shown to work also after including photometric uncertainties that mimic the COSMOS survey. We then label the SOM grid with a simulated calibration sample and estimate redshift and SFR for COSMOS-like galaxies up to $zsim3$. In comparison to state-of-the-art techniques based on synthetic templates, our method is comparable in performance but less biased at estimating redshifts, and significantly better at predicting SFRs. In particular our data-driven approach, in contrast to model libraries, intrinsically allows for the complexity of galaxy formation and can handle sample biases. We advocate that obtaining the calibration for this method should be one of the goals of next-generation galaxy surveys.
Template fits to observed galaxy fluxes allow calculation of K-corrections and
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