701,370 research outputs found
Measurement and Calibration of Noise Bias in Weak Lensing Galaxy Shape Estimation
Weak gravitational lensing has the potential to constrain cosmological
parameters to high precision. However, as shown by the Shear TEsting Programmes
(STEP) and GRavitational lEnsing Accuracy Testing (GREAT) Challenges, measuring
galaxy shears is a nontrivial task: various methods introduce different
systematic biases which have to be accounted for. We investigate how pixel
noise on the image affects the bias on shear estimates from a
Maximum-Likelihood forward model-fitting approach using a sum of co-elliptical
S\'{e}rsic profiles, in complement to the theoretical approach of an an
associated paper. We evaluate the bias using a simple but realistic galaxy
model and find that the effects of noise alone can cause biases of order 1-10%
on measured shears, which is significant for current and future lensing
surveys. We evaluate a simulation-based calibration method to create a bias
model as a function of galaxy properties and observing conditions. This model
is then used to correct the simulated measurements. We demonstrate that this
method can effectively reduce noise bias so that shear measurement reaches the
level of accuracy required for estimating cosmic shear in upcoming lensing
surveys.Comment: 12 pages, 4 figures, submitted to MNRA
S\'{e}rsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction
Cosmic shear is a powerful probe of cosmological parameters, but its
potential can be fully utilised only if galaxy shapes are measured with great
accuracy. Two major effects have been identified which are likely to account
for most of the bias for maximum likelihood methods in recent shear measurement
challenges. Model bias occurs when the true galaxy shape is not well
represented by the fitted model. Noise bias occurs due to the non-linear
relationship between image pixels and galaxy shape. In this paper we
investigate the potential interplay between these two effects when an imperfect
model is used in the presence of high noise. We present analytical expressions
for this bias, which depends on the residual difference between the model and
real data. They can lead to biases not accounted for in previous calibration
schemes. By measuring the model bias, noise bias and their interaction, we
provide a complete statistical framework for measuring galaxy shapes with model
fitting methods from GRavitational lEnsing Accuracy Testing (GREAT) like
images. We demonstrate the noise and model interaction bias using a simple toy
model, which indicates that this effect can potentially be significant. Using
real galaxy images from the Cosmological Evolution Survey (COSMOS) we quantify
the strength of the model bias, noise bias and their interaction. We find that
the interaction term is often a similar size to the model bias term, and is
smaller than the requirements of the current and shortly upcoming galaxy
surveys.Comment: 11 pages, 3 figure
Noise Suppression of Computed Tomography (CT) Images Using Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
In this study, an in-house residual encoder-decoder convolutional neural network (RED-CNN)-based algorithm was composed and trained using images of cylindrical polymethyl-methacrylate (PMMA) phantom with a diameter of 26 cm at different simulated noise levels. The model was tested on 21 × 26 cm elliptical PMMA computed tomography (CT) phantom images with simulated noise to evaluate its denoising capability using signal to noise ratio (SNR), comparative peak signal-to-noise ratio (cPSNR), structural similarity (SSIM) index, modulation transfer function frequencies (MTF 10 %) and noise power spectra (NPS) values as parameters. Evaluation of a possible decrease of image quality was also performed by testing the model using homogenous water phantom and wire phantom images acquired using different mAs values. Results show that the model was able to consistently increase SNR, cPSNR, SSIM values, and decrease the integral noise power spectra (NPS). However, the noise level on either training or testing data affects the model’s final denoising performance. The lower noise level on testing data images tends to result in over-smoothed images, as indicated by the shift of the NPS curves. In contrast, higher simulated noise level tends to result in less satisfactory denoising performance, as indicated by lower SNR, cPSNR, and SSIM values. Meanwhile, the higher noise level on training data images tends to produce denoised images with reduced sharpness, as indicated by the decrease of the MTF 10 % values. Further studies are required to better understand the character of RED-CNN for CT noise suppression regarding the optimum parameters for best results
Multiple Testing and Variable Selection along Least Angle Regression's path
In this article, we investigate multiple testing and variable selection using
Least Angle Regression (LARS) algorithm in high dimensions under the Gaussian
noise assumption. LARS is known to produce a piecewise affine solutions path
with change points referred to as knots of the LARS path. The cornerstone of
the present work is the expression in closed form of the exact joint law of
K-uplets of knots conditional on the variables selected by LARS, namely the
so-called post-selection joint law of the LARS knots. Numerical experiments
demonstrate the perfect fit of our finding.
Our main contributions are three fold. First, we build testing procedures on
variables entering the model along the LARS path in the general design case
when the noise level can be unknown. This testing procedures are referred to as
the Generalized t-Spacing tests (GtSt) and we prove that they have exact
non-asymptotic level (i.e., Type I error is exactly controlled). In that way,
we extend a work from (Taylor et al., 2014) where the Spacing test works for
consecutive knots and known variance. Second, we introduce a new exact multiple
false negatives test after model selection in the general design case when the
noise level can be unknown. We prove that this testing procedure has exact
non-asymptotic level for general design and unknown noise level. Last, we give
an exact control of the false discovery rate (FDR) under orthogonal design
assumption. Monte-Carlo simulations and a real data experiment are provided to
illustrate our results in this case. Of independent interest, we introduce an
equivalent formulation of LARS algorithm based on a recursive function.Comment: 62 pages; new: FDR control and power comparison between Knockoff,
FCD, Slope and our proposed method; new: the introduction has been revised
and now present a synthetic presentation of the main results. We believe that
this introduction brings new insists compared to previous version
Transient excitation and data processing techniques employing the fast fourier transform for aeroelastic testing
The development of testing techniques useful in airplane ground resonance testing, wind tunnel aeroelastic model testing, and airplane flight flutter testing is presented. Included is the consideration of impulsive excitation, steady-state sinusoidal excitation, and random and pseudorandom excitation. Reasons for the selection of fast sine sweeps for transient excitation are given. The use of the fast fourier transform dynamic analyzer (HP-5451B) is presented, together with a curve fitting data process in the Laplace domain to experimentally evaluate values of generalized mass, model frequencies, dampings, and mode shapes. The effects of poor signal to noise ratios due to turbulence creating data variance are discussed. Data manipulation techniques used to overcome variance problems are also included. The experience is described that was gained by using these techniques since the early stages of the SST program. Data measured during 747 flight flutter tests, and SST, YC-14, and 727 empennage flutter model tests are included
Improved method for SNR prediction in machine-learning-based test
This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach, the dynamic parameters can be predicted by using the signature results. However, it can only estimate the SNR accurately within a certain range. In order to overcome this limitation, an improved method based on work is applied in this work. It is validated on the Labview model of a 12-bit 80 Ms/s pipelined ADC with a pulse- wave input signal of 3 LSB noise and 7-bit nonlinear rising and falling edges
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