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Register a new userPrincipal Component Analysis of the Time- and Position-Dependent Point Spread Function of the Advanced Camera for Surveys
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We describe the time- and position-dependent point spread function (PSF) variation of the Wide Field Channel (WFC) of the Advanced Camera for Surveys (ACS) with the principal component analysis (PCA) technique. The time-dependent change is caused by the temporal variation of the $HST$ focus whereas the position-dependent PSF variation in ACS/WFC at a given focus is mainly the result of changes in aberrations and charge diffusion across the detector, which appear as position-dependent changes in elongation of the astigmatic core and blurring of the PSF, respectively. Using >400 archival images of star cluster fields, we construct a ACS PSF library covering diverse environments of the $HST$ observations (e.g., focus values). We find that interpolation of a small number ($sim20$) of principal components or ``eigen-PSFs per exposure can robustly reproduce the observed variation of the ellipticity and size of the PSF. Our primary interest in this investigation is the application of this PSF library to precision weak-lensing analyses, where accurate knowledge of the instruments PSF is crucial. However, the high-fidelity of the model judged from the nice agreement with observed PSFs suggests that the model is potentially also useful in other applications such as crowded field stellar photometry, galaxy profile fitting, AGN studies, etc., which similarly demand a fair knowledge of the PSFs at objects locations. Our PSF models, applicable to any WFC image rectified with the Lanczos3 kernel, are publicly available.
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(abridged) We examine the spatial and temporal stability of the HST ACS Wide Field Camera (WFC) point spread function (PSF) using the two square degree COSMOS survey. We show that stochastic aliasing of the PSF necessarily occurs during `drizzling. This aliasing is maximal if the output pixel scale is equal to the input pixel scale of 0.05. We show that this source of PSF variation can be significantly reduced by choosing a Gaussian drizzle kernel and by setting the output pixel size to 0.03. We show that the PSF is temporally unstable, most likely due to thermal fluctuations in the telescopes focus. We find that the primary manifestation of this thermal drift in COSMOS images is an overall slow periodic focus change. Using a modified version of TinyTim, we create undistorted stars in a 30x30 grid across the ACS WFC CCDs. These PSF models are created for telescope focus values in the range -10 microns to +5 microns, thus spanning the allowed range of telescope focus values. We then use the approximately ten well measured stars in each COSMOS field to pick the best-fit focus value for each field. The TinyTim model stars are then used to perform PSF corrections for weak lensing allowing systematics due to incorrectly modeled PSFs to be greatly reduced. We have made the software for PSF modeling using our modified version of TinyTim available to the astronomical community. We show the effects of Charge Transfer Efficiency (CTE) degradation, which distorts the object in the readout direction, mimicking a weak lensing signal. We derive a parametric correction for the effect of CTE on the shapes of objects in the COSMOS field as a function of observation date, position within the ACS WFC field, and object flux.
We study degeneracies between cosmological parameters and measurement errors from cosmic shear surveys using a principal component analysis of the Fisher matrix. We simulate realistic survey topologies with non-uniform sky coverage, and quantify the effect of survey geometry, depth and noise from intrinsic galaxy ellipticities on the parameter errors. This analysis allows us to optimise the survey geometry. Using the shear two-point correlation functions and the aperture mass dispersion, we study various degeneracy directions in a multi-dimensional parameter space spanned by Omega_m, Omega_Lambda, sigma_8, the shape parameter Gamma, the spectral index n_s, along with parameters that specify the distribution of source galaxies. If only three parameters are to be obtained from weak lensing data, a single principal component is dominant and contains all information about the main parameter degeneracies and their errors. The variance of the dominant principal component of the Fisher matrix shows a minimum for survey strategies which have small cosmic variance and measure the shear correlation up to several degrees [abridged].
We present an empirical correction of sky coordinates of X-ray photons obtained with the XIS aboard the Suzaku satellite to improve the source position accuracy and restore the point-spread function (PSF). The XIS images are known to have an uncertainty in position of up to 1 arcmin, and to show considerable degradations of the PSF. These problems are caused by a drifting of the satellite attitude due to thermal distortion of the side panel 7, where the attitude control system is mounted. We found that the position error averaged over a pointing observation can be largely reduced by using the relation between the deviation of the source position in the DETX direction and the ecliptic latitude of the pointing target. We parameterized the wobbling of the source position synchronized with the satellite orbital period with temperatures of onboard radiators and elapsed time since the night-day transition of the spacecraft. We developed software, aeattcor, to correct the image drift using these parameters, and applied it to 27 point-source images. We show that the radius of the 90% error circle of the source position was reduced to 19 arcsec and the PSF was sharpened. These improvements have enhanced the scientific capability of the Suzaku XIS.
Model-independent analysis (MIA) methods are generally useful for analysing complex systems in which relationships between the observables are non-trivial and noise is present. Principle Component Analysis (PCA) is one of MIA methods allowing to isolate components in the input data graded to their contribution to the variability of the data. In this publication we show how the PCA can be applied to digitised signals obtained from a cavity beam position monitor (CBPM) system on the example of a 3-cavity test system installed at the Accelerator Test Facility 2 (ATF2) at KEK in Japan. We demonstrate that the PCA based method can be used to extract beam position information, and matches conventional techniques in terms of performance, while requiring considerably less settings and data for calibration.
The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its variations, and propose a general ridge regression model to extract features from both row and column directions. To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model. R2DPCA generates a weighting vector with utilizing the label information, and maximizes a relaxed criterion with applying an optimal algorithm to get the essential features. The R2DPCA-based approaches for face recognition and image reconstruction are also proposed and the selected principle components are weighted to enhance the role of main components. Numerical experiments on well-known standard databases indicate that R2DPCA has high generalization ability and can achieve a higher recognition rate than the state-of-the-art methods, including in the deep learning methods such as CNNs, DBNs, and DNNs.
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