23,143 research outputs found
Bayesian modelling of clusters of galaxies from multi-frequency pointed Sunyaev--Zel'dovich observations
We present a Bayesian approach to modelling galaxy clusters using
multi-frequency pointed observations from telescopes that exploit the
Sunyaev--Zel'dovich effect. We use the recently developed MultiNest technique
(Feroz, Hobson & Bridges, 2008) to explore the high-dimensional parameter
spaces and also to calculate the Bayesian evidence. This permits robust
parameter estimation as well as model comparison. Tests on simulated Arcminute
Microkelvin Imager observations of a cluster, in the presence of primary CMB
signal, radio point sources (detected as well as an unresolved background) and
receiver noise, show that our algorithm is able to analyse jointly the data
from six frequency channels, sample the posterior space of the model and
calculate the Bayesian evidence very efficiently on a single processor. We also
illustrate the robustness of our detection process by applying it to a field
with radio sources and primordial CMB but no cluster, and show that indeed no
cluster is identified. The extension of our methodology to the detection and
modelling of multiple clusters in multi-frequency SZ survey data will be
described in a future work.Comment: 12 pages, 7 figures, submitted to MNRA
Super-sample covariance approximations and partial sky coverage
Super-sample covariance (SSC) is the dominant source of statistical error on
large scale structure (LSS) observables for both current and future galaxy
surveys. In this work, we concentrate on the SSC of cluster counts, also known
as sample variance, which is particularly useful for the self-calibration of
the cluster observable-mass relation; our approach can similarly be applied to
other observables, such as galaxy clustering and lensing shear. We first
examined the accuracy of two analytical approximations proposed in the
literature for the flat sky limit, finding that they are accurate at the 15%
and 30-35% level, respectively, for covariances of counts in the same redshift
bin. We then developed a harmonic expansion formalism that allows for the
prediction of SSC in an arbitrary survey mask geometry, such as large sky areas
of current and future surveys. We show analytically and numerically that this
formalism recovers the full sky and flat sky limits present in the literature.
We then present an efficient numerical implementation of the formalism, which
allows fast and easy runs of covariance predictions when the survey mask is
modified. We applied our method to a mask that is broadly similar to the Dark
Energy Survey footprint, finding a non-negligible negative cross-z covariance,
i.e. redshift bins are anti-correlated. We also examined the case of data
removal from holes due to, for example bright stars, quality cuts, or
systematic removals, and find that this does not have noticeable effects on the
structure of the SSC matrix, only rescaling its amplitude by the effective
survey area. These advances enable analytical covariances of LSS observables to
be computed for current and future galaxy surveys, which cover large areas of
the sky where the flat sky approximation fails.Comment: 14 pages, 10 figures. Updated to match version published in Astronomy
& Astrophysic
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
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