Dirichlet Methods for Bayesian Source Detection in Radio Astronomy Images

<p>The sheer volume of data to be produced by the next generation of radio telescopes—exabytes of data on hundreds of millions of objects—makes automated methods for the detection of astronomical objects ("sources") essential. Of particular importance are low surface brightness objects, which are not well found by current automated methods. This thesis explores Bayesian methods for source detection that use Dirichlet or multinomial models for pixel intensity distributions in discretised radio astronomy images. A novel image discretisation method that incorporates uncertainty about how the image should be discretised is developed. Latent Dirichlet allocation — a method originally developed for inferring latent topics in document collections — is used to estimate source and background distributions in radio astronomy images. A new Dirichlet-multinomial ratio, indicating how well a region conforms to a well-specified model of background versus a loosely-specified model of foreground, is derived. Finally, latent Dirichlet allocation and the Dirichlet-multinomial ratio are combined for source detection in astronomical images. The methods developed in this thesis perform source detection well in comparison to two widely-used source detection packages and, importantly, find dim sources not well found by other algorithms.</p>

Dirichlet Methods for Bayesian Source Detection in Radio Astronomy Images

<p>The sheer volume of data to be produced by the next generation of radio telescopes—exabytes of data on hundreds of millions of objects—makes automated methods for the detection of astronomical objects ("sources") essential. Of particular importance are low surface brightness objects, which are not well found by current automated methods. This thesis explores Bayesian methods for source detection that use Dirichlet or multinomial models for pixel intensity distributions in discretised radio astronomy images. A novel image discretisation method that incorporates uncertainty about how the image should be discretised is developed. Latent Dirichlet allocation — a method originally developed for inferring latent topics in document collections — is used to estimate source and background distributions in radio astronomy images. A new Dirichlet-multinomial ratio, indicating how well a region conforms to a well-specified model of background versus a loosely-specified model of foreground, is derived. Finally, latent Dirichlet allocation and the Dirichlet-multinomial ratio are combined for source detection in astronomical images. The methods developed in this thesis perform source detection well in comparison to two widely-used source detection packages and, importantly, find dim sources not well found by other algorithms.</p>

Disturbance Modelling for Minimum Variance Control in Adaptive Optics Systems Using Wavefront Sensor Sampled-Data

Modern large telescopes are built based on the effectiveness of adaptive optics systems in mitigating the detrimental effects of wavefront distortions on astronomical images. In astronomical adaptive optics systems, the main sources of wavefront distortions are atmospheric turbulence and mechanical vibrations that are induced by the wind or the instrumentation systems, such as fans and cooling pumps. The mitigation of wavefront distortions is typically attained via a control law that is based on an adequate and accurate model. In this paper, we develop a modelling technique based on continuous-time damped-oscillators and on the Whittle’s likelihood method to estimate the parameters of disturbance models from wavefront sensor time-domain sampled-data. On the other hand, when the model is not accurate, the performance of the minimum variance controller is affected. We show that our modelling and identification techniques not only allow for more accurate estimates, but also for better minimum variance control performance. We illustrate the benefits of our proposal via numerical simulations.

ABOUT DIGITAL PROCESSING OF ASTRONOMICAL IMAGES USING HISTOGRAM PROCESSING METHODS

Photos of extended objects are crucial for astronomers, as they contain enough detailed information about the celestial bodies that it is quite difficult to extract visually. Most of the information available for analyzing these objects begins with studying them with telescopes or satellites. Unfortunately, the quality of astronomical images is usually very poor compared to other real images, and this is due to the technical and physical features associated with the process of obtaining them. This increases the percentage of noise and makes it more difficult to directly use standard methods on the original image. Images taken from a satellite or telescope are almost always grayscale, but still contain some color information. However, an astronomical image can be obtained through a color filter. Different photodetectors also usually have different sensitivity to different colors (wavelengths). In our paper, we will present a method for processing astronomical images, using histogram processing, which can be successfully used to improve images, and post-processing.

Artificial neural network classification of asteroids in the M1:2 mean-motion resonance with Mars

ABSTRACT Artificial neural networks (ANNs) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work, we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85 per cent levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision, and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.

Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration

Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.