28,803 research outputs found
Filtering techniques for the detection of Sunyaev-Zel'dovich clusters in multifrequency CMB maps
The problem of detecting Sunyaev-Zel'dovich (SZ) clusters in multifrequency
CMB observations is investigated using a number of filtering techniques. A
multifilter approach is introduced, which optimizes the detection of SZ
clusters on microwave maps. An alternative method is also investigated, in
which maps at different frequencies are combined in an optimal manner so that
existing filtering techniques can be applied to the single combined map. The SZ
profiles are approximated by the circularly-symmetric template , with and , where the core radius and the overall amplitude of the effect
are not fixed a priori, but are determined from the data. The background
emission is modelled by a homogeneous and isotropic random field, characterized
by a cross-power spectrum with . The
filtering methods are illustrated by application to simulated Planck
observations of a patch of sky in 10 frequency
channels. Our simulations suggest that the Planck instrument should detect
SZ clusters in 2/3 of the sky. Moreover, we find the catalogue
to be complete for fluxes mJy at 300 GHz.Comment: 12 pages, 7 figures; Corrected figures. Submitted to MNRA
Wavefront sensing of atmospheric phase distortions at the Palomar 200-in. telescope and implications for adaptive optics
Major efforts in astronomical instrumentation are now being made to apply the techniques of adaptive optics to the correction of phase distortions induced by the turbulent atmosphere and by quasi-static aberrations in telescopes themselves. Despite decades of study, the problem of atmospheric turbulence is still only partially understood. We have obtained video-rate (30 Hz) imaging of stellar clusters and of single-star phase distortions over the pupil of the 200" Hale telescope on Palomar Mountain. These data show complex temporal and spatial behavior, with multiple components arising at a number of scale heights in the atmosphere; we hope to quantify this behavior to ensure the feasibility of adaptive optics at the Observatory. We have implemented different wavefront sensing techniques to measure aperture phase in wavefronts from single stars, including the classical Foucault test, which measures the local gradient of phase, and the recently-devised curvature sensing technique, which measures the second derivative of pupil phase and has formed the real-time wavefront sensor for some very productive astronomical adaptive optics. Our data, though not fast enough to capture all details of atmospheric phase fluctuations, provide important information regarding the capabilities that must be met by the adaptive optics system now being built for the 200" telescope by a team at the Jet Propulsion Lab. We describe our data acquisition techniques, initial results from efforts to characterize the properties of the turbulent atmosphere at Palomar Mountain, and future plans to extract additional quantitative parameters of use for adaptive optics performance predictions
The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution
We present here a new method, MMF, for automatically segmenting cosmic
structure into its basic components: clusters, filaments, and walls.
Importantly, the segmentation is scale independent, so all structures are
identified without prejudice as to their size or shape. The method is ideally
suited for extracting catalogues of clusters, walls, and filaments from samples
of galaxies in redshift surveys or from particles in cosmological N-body
simulations: it makes no prior assumptions about the scale or shape of the
structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For
Full Resolution Version see:
http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd
Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement
The goal of many early visual filtering processes is to remove noise while at the same time sharpening contrast. An historical succession of approaches to this problem, starting with the use of simple derivative and smoothing operators, and the subsequent realization of the relationship between scale-space and the isotropic dfffusion equation, has recently resulted in the development of "geometry-driven" dfffusion. Nonlinear and anisotropic diffusion methods, as well as image-driven nonlinear filtering, have provided improved performance relative to the older isotropic and linear diffusion techniques. These techniques, which either explicitly or implicitly make use of kernels whose shape and center are functions of local image structure are too computationally expensive for use in real-time vision applications. In this paper, we show that results which are largely equivalent to those obtained from geometry-driven diffusion can be achieved by a process which is conceptually separated info two very different functions. The first involves the construction of a vector~field of "offsets", defined on a subset of the original image, at which to apply a filter. The offsets are used to displace filters away from boundaries to prevent edge blurring and destruction. The second is the (straightforward) application of the filter itself. The former function is a kind generalized image skeletonization; the latter is conventional image filtering. This formulation leads to results which are qualitatively similar to contemporary nonlinear diffusion methods, but at computation times that are roughly two orders of magnitude faster; allowing applications of this technique to real-time imaging. An additional advantage of this formulation is that it allows existing filter hardware and software implementations to be applied with no modification, since the offset step reduces to an image pixel permutation, or look-up table operation, after application of the filter
Optical Cluster-Finding with An Adaptive Matched-Filter Technique: Algorithm and Comparison with Simulations
We present a modified adaptive matched filter algorithm designed to identify
clusters of galaxies in wide-field imaging surveys such as the Sloan Digital
Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys
with spectroscopic coverage, multicolor photometric redshifts, no redshift
information at all, and any combination of these within one survey. It works
with high efficiency in multi-band imaging surveys where photometric redshifts
can be estimated with well-understood error distributions. Tests of the
algorithm on realistic mock SDSS catalogs suggest that the detected sample is
~85% complete and over 90% pure for clusters with masses above 1.0*10^{14}
h^{-1} M_solar and redshifts up to z=0.45. The errors of estimated cluster
redshifts from maximum likelihood method are shown to be small (typically less
that 0.01) over the whole redshift range with photometric redshift errors
typical of those found in the Sloan survey. Inside the spherical radius
corresponding to a galaxy overdensity of Delta=200, we find the derived cluster
richness Lambda_{200} a roughly linear indicator of its virial mass M_{200},
which well recovers the relation between total luminosity and cluster mass of
the input simulation.Comment: Accepted to ApJ. 13 pages, 9 figure
High spatial resolution and high contrast optical speckle imaging with FASTCAM at the ORM
In this paper, we present an original observational approach, which combines,
for the first time, traditional speckle imaging with image post-processing to
obtain in the optical domain diffraction-limited images with high contrast
(1e-5) within 0.5 to 2 arcseconds around a bright star. The post-processing
step is based on wavelet filtering an has analogy with edge enhancement and
high-pass filtering. Our I-band on-sky results with the 2.5-m Nordic Telescope
(NOT) and the lucky imaging instrument FASTCAM show that we are able to detect
L-type brown dwarf companions around a solar-type star with a contrast DI~12 at
2" and with no use of any coronographic capability, which greatly simplifies
the instrumental and hardware approach. This object has been detected from the
ground in J and H bands so far only with AO-assisted 8-10 m class telescopes
(Gemini, Keck), although more recently detected with small-class telescopes in
the K band. Discussing the advantage and disadvantage of the optical regime for
the detection of faint intrinsic fluxes close to bright stars, we develop some
perspectives for other fields, including the study of dense cores in globular
clusters. To the best of our knowledge this is the first time that high
contrast considerations are included in optical speckle imaging approach.Comment: Proceedings of SPIE conference - Ground-based and Airborne
Instrumentation for Astronomy III (Conference 7735), San Diego 201
Triplicity and Physical Characteristics of Asteroid (216) Kleopatra
To take full advantage of the September 2008 opposition passage of the M-type
asteroid (216) Kleopatra, we have used near-infrared adaptive optics (AO)
imaging with the W.M. Keck II telescope to capture unprecedented high
resolution images of this unusual asteroid. Our AO observations with the W.M.
Keck II telescope, combined with Spitzer/IRS spectroscopic observations and
past stellar occultations, confirm the value of its IRAS radiometric radius of
67.5 km as well as its dog-bone shape suggested by earlier radar observations.
Our Keck AO observations revealed the presence of two small satellites in orbit
about Kleopatra (see Marchis et al., 2008). Accurate measurements of the
satellite orbits over a full month enabled us to determine the total mass of
the system to be 4.64+/-0.02 10^18 Kg. This translates into a bulk density of
3.6 +/-0.4 g/cm3, which implies a macroscopic porosity for Kleopatra of ~
30-50%, typical of a rubble-pile asteroid. From these physical characteristics
we measured its specific angular momentum, very close to that of a spinning
equilibrium dumbbell.Comment: 35 pages, 3 Tables, 9 Figures. In press to Icaru
Regularity scalable image coding based on wavelet singularity detection
In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc
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