2,572 research outputs found
ANNz: estimating photometric redshifts using artificial neural networks
We introduce ANNz, a freely available software package for photometric
redshift estimation using Artificial Neural Networks. ANNz learns the relation
between photometry and redshift from an appropriate training set of galaxies
for which the redshift is already known. Where a large and representative
training set is available ANNz is a highly competitive tool when compared with
traditional template-fitting methods.
The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release
1, and for this particular data set the r.m.s. redshift error in the range 0 <
z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or
which are brighter than the photometric set for which redshifts are required)
are simulated and the impact on the photometric redshift accuracy assessed.Comment: 6 pages, 6 figures. Replaced to match version accepted by PASP (minor
changes to original submission). The ANNz package may be obtained from
http://www.ast.cam.ac.uk/~aa
Faint Blue Galaxies as a Probe of the X-ray Background at High Redshift
We present a formalism describing the physical content of cross-correlation
functions between a diffuse background and a population of discrete sources.
The formalism is used to interpret cross-correlation signals between the
unresolved X-ray background and a galaxy population resolved to high redshift
in another spectral band. Specifically, we apply it to the so-called faint blue
galaxy population and constrain their X-ray emissivity and clustering
properties. A model is presented which satisfies the recently measured
constraints on all 3 correlation functions (galaxy/galaxy,
background/background and galaxy/background). This model predicts that faint
galaxies in the magnitude range B=18-23 (cvering redshifts z \lsim 0.5) make
up of the X-ray background in the 0.5-2 keV band. At the mean
redshift of the galaxy sample, , the comoving volume emissivity is
ergs sMpc . When extrapolated
to fainter magnitudes, the faint blue galaxy population can account for most of
the residual background at soft energy. We show how the measurement of the
angular and zero-lag cross-correlation functions between increasingly faint
galaxies and the X-ray background can allow us to map the X-ray emissivity as a
function of redshift.Comment: uuencoded compressed postscript, without figures. The preprint is
available with figures at http://www.ast.cam.ac.uk/preprint/PrePrint.htm
Variance and Skewness in the FIRST survey
We investigate the large-scale clustering of radio sources in the FIRST
1.4-GHz survey by analysing the distribution function (counts in cells). We
select a reliable sample from the the FIRST catalogue, paying particular
attention to the problem of how to define single radio sources from the
multiple components listed. We also consider the incompleteness of the
catalogue. We estimate the angular two-point correlation function ,
the variance , and skewness of the distribution for the
various sub-samples chosen on different criteria. Both and
show power-law behaviour with an amplitude corresponding a spatial correlation
length of Mpc. We detect significant skewness in the
distribution, the first such detection in radio surveys. This skewness is found
to be related to the variance through , with
, consistent with the non-linear gravitational growth of
perturbations from primordial Gaussian initial conditions. We show that the
amplitude of variance and skewness are consistent with realistic models of
galaxy clustering.Comment: 13 pages, 21 inline figures, to appear in MNRA
The X-ray Cluster Dipole
We estimate the dipole of the whole sky X-ray flux-limited sample of
Abell/ACO clusters (XBACs) and compare it to the optical Abell/ACO cluster
dipole. The X-ray cluster dipole is well aligned () with the
CMB dipole, while it follows closely the radial profile of its optical cluster
counterpart although its amplitude is per cent lower. In view of
the fact that the the XBACs sample is not affected by the volume incompleteness
and the projection effects that are known to exist at some level in the optical
parent Abell/ACO cluster catalogue, our present results confirm the previous
optical cluster dipole analysis that there are significant contributions to the
Local Group motion from large distances (Mpc). In order to
assess the expected contribution to the X-ray cluster dipole from a purely
X-ray selected sample we compare the dipoles of the XBACs and the Brightest
Cluster Sample (Ebeling et al. 1997a) in their overlap region. The resulting
dipoles are in mutual good aggreement with an indication that the XBACs sample
slightly underestimates the full X-ray dipole (by per cent) while the
Virgo cluster contributes about 10 - 15 per cent to the overall X-ray cluster
dipole. Using linear perturbation theory to relate the X-ray cluster dipole to
the Local group peculiar velocity we estimate the density parameter to be
.Comment: 16 pages, latex, + 4 ps figures, submitted to Ap
AUTOMATED MORPHOLOGICAL CLASSIFICATION OF APM GALAXIES BY SUPERVISED ARTIFICIAL NEURAL NETWORKS
We train Artificial Neural Networks to classify galaxies based solely on the
morphology of the galaxy images as they appear on blue survey plates. The
images are reduced and morphological features such as bulge size and the number
of arms are extracted, all in a fully automated manner. The galaxy sample was
first classified by 6 independent experts. We use several definitions for the
mean type of each galaxy, based on those classifications. We then train and
test the network on these features. We find that the rms error of the network
classifications, as compared with the mean types of the expert classifications,
is 1.8 Revised Hubble Types. This is comparable to the overall rms dispersion
between the experts. This result is robust and almost completely independent of
the network architecture used.Comment: The full paper contains 25 pages, and includes 22 figures. It is
available at ftp://ftp.ast.cam.ac.uk/pub/hn/apm2.ps . The table in the
appendix is available on request from [email protected]. Mon. Not. R. Astr.
Soc., in pres
The Dipole Anisotropy of the First All-Sky X-ray Cluster Sample
We combine the recently published CIZA galaxy cluster catalogue with the
XBACs cluster sample to produce the first all-sky catalogue of X-ray clusters
in order to examine the origins of the Local Group's peculiar velocity without
the use of reconstruction methods to fill the traditional Zone of Avoidance.
The advantages of this approach are (i) X-ray emitting clusters tend to trace
the deepest potential wells and therefore have the greatest effect on the
dynamics of the Local Group and (ii) our all-sky sample provides data for
nearly a quarter of the sky that is largely incomplete in optical cluster
catalogues. We find that the direction of the Local Group's peculiar velocity
is well aligned with the CMB as early as the Great Attractor region 40 h^-1 Mpc
away, but that the amplitude of its dipole motion is largely set between 140
and 160 h^-1 Mpc. Unlike previous studies using galaxy samples, we find that
without Virgo included, roughly ~70% of our dipole signal comes from mass
concentrations at large distances (>60 h^-1 Mpc) and does not flatten,
indicating isotropy in the cluster distribution, until at least 160 h^-1 Mpc.
We also present a detailed discussion of our dipole profile, linking observed
features to the structures and superclusters that produce them. We find that
most of the dipole signal can be attributed to the Shapley supercluster
centered at about 150 h^-1 Mpc and a handful of very massive individual
clusters, some of which are newly discovered and lie well in the Zone of
Avoidance.Comment: 15 Pages, 9 Figures. Accepted by Ap
Measuring the Mach number of the Universe via the Sunyaev-Zeldovich effect
We introduce a new statistic to measure more accurately the cosmic sound
speed of clusters of galaxies at different redshifts. This statistic is
evaluated by cross-correlating cosmic microwave background (CMB) fluctuations
caused by the Sunyaev-Zel'dovich effect from observed clusters of galaxies with
their redshifts. When clusters are distributed in redshift bins of narrow
width, one could measure the mean squared cluster peculiar velocity with an
error \sigma_{C_S^2}\lsim (300{\rm km/s})^2. This can be done around z>0.3 with
clusters of flux above 200 mJy which will be detected by PLANCK, coupled with
high resolution microwave images to eliminate the cosmological part of the CMB
fluctuations. The latter can be achieved with observations by the planned ALMA
array or the NSF South Pole telescope and other surveys. By measuring the
cosmic sound speed and the bulk flow in, e.g., 4 spheres of ~ 100h^{-1}Mpc at
z=0.3, we could have a direct measurement of the matter density
0.21<\Omega_m<0.47 at 95 % confidence level.Comment: Ap.J.Letters, submitte
Combining cosmological datasets: hyperparameters and Bayesian evidence
A method is presented for performing joint analyses of cosmological datasets,
in which the weight assigned to each dataset is determined directly by it own
statistical properties. The weights are considered in a Bayesian context as a
set of hyperparameters, which are then marginalised over in order to recover
the posterior distribution as a function only of the cosmological parameters of
interest. In the case of a Gaussian likelihood function, this marginalisation
may be performed analytically. Calculation of the Bayesian evidence for the
data, with and without the introduction of hyperparameters, enables a direct
determination of whether the data warrant the introduction of weights into the
analysis; this generalises the standard likelihood ratio approach to model
comparison. The method is illustrated by application to the classic toy problem
of fitting a straight line to a set of data. A cosmological illustration of the
technique is also presented, in which the latest measurements of the cosmic
microwave background power spectrum are used to infer constraints on
cosmological parameters.Comment: 12 pages, 6 figures, submitted to MNRA
Bayesian `Hyper-Parameters' Approach to Joint Estimation: The Hubble Constant from CMB Measurements
Recently several studies have jointly analysed data from different
cosmological probes with the motivation of estimating cosmological parameters.
Here we generalise this procedure to take into account the relative weights of
various probes. This is done by including in the joint \chi^2 function a set of
`Hyper-Parameters', which are dealt with using Bayesian considerations. The
resulting algorithm (in the case of uniform priors on the log of the
Hyper-Parameters) is very simple: instead of minimising \sum \chi_j^2 (where
\chi_j^2 is per data set j) we propose to minimise \sum N_j \ln (\chi_j^2)
(where N_j is the number of data points per data set j). We illustrate the
method by estimating the Hubble constant H_0 from different sets of recent CMB
experiments (including Saskatoon, Python V, MSAM1, TOCO and Boomerang).Comment: submitted to MNRAS, 6 pages, Latex, with 3 figures embedde
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