Imaging sub-milliarcsecond stellar features with intensity interferometry using air Cherenkov telescope arrays

Recent proposals have been advanced to apply imaging air Cherenkov telescope arrays to stellar intensity interferometry (SII). Of particular interest is the possibility of model-independent image recovery afforded by the good (u, v)-plane coverage of these arrays, as well as recent developments in phase retrieval techniques. The capabilities of these instruments used as SII receivers have already been explored for simple stellar objects, and here the focus is on reconstructing stellar images with non-uniform radiance distributions. We find that hot stars (T > 6000 K) containing hot and/or cool localized regions (T \sim 500 K) as small as \sim 0.1 mas can be imaged at short wavelengths ({\lambda} = 400 nm).Comment: Accepted for publication in MNRAS. 6 pages, 10 figure

Towards optical intensity interferometry for high angular resolution stellar astrophysics

Most neighboring stars are still detected as point sources and are beyond the angular resolution reach of current observatories. Methods to improve our understanding of stars at high angular resolution are investigated. Air Cherenkov telescopes (ACTs), primarily used for Gamma-ray astronomy, enable us to increase our understanding of the circumstellar environment of a particular system. When used as optical intensity interferometers, future ACT arrays will allow us to detect stars as extended objects and image their surfaces at high angular resolution. Optical stellar intensity interferometry (SII) with ACT arrays, composed of nearly 100 telescopes, will provide means to measure fundamental stellar parameters and also open the possibility of model-independent imaging. A data analysis algorithm is developed and permits the reconstruction of high angular resolution images from simulated SII data. The capabilities and limitations of future ACT arrays used for high angular resolution imaging are investigated via Monte-Carlo simulations. Simple stellar objects as well as stellar surfaces with localized hot or cool regions can be accurately imaged. Finally, experimental efforts to measure intensity correlations are expounded. The functionality of analog and digital correlators is demonstrated. Intensity correlations have been measured for a simulated star emitting pseudo-thermal light, resulting in angular diameter measurements. The StarBase observatory, consisting of a pair of 3 m telescopes separated by 23 m, is described.Comment: PhD dissertatio

Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces

Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms

We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions--the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L^2(R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L^2(R^2), we then discuss a methodology for constructing 2D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1D counterpart, we relate the real and imaginary components of these complex wavelets using a multi-dimensional extension of the HT--the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient FFT-based filterbank algorithm for implementing the associated complex wavelet transform.Comment: 36 pages, 8 figure