Accelerated implementations of the RIME for DDE calibration and source modelling
- Authors: Van Staden, Joshua
- Date: 2021
- Subjects: Radio astronomy , Radio inferometers , Radio inferometers -- Calibration , Radio astronomy -- Data processing , Radio inferometers -- Data processing , Radio inferometers -- Calibration -- Data processing
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/172422 , vital:42199
- Description: Second- and third-generation calibration methods filter out subtle effects in interferometer data, and therefore yield significantly higher dynamic ranges. The basis of these calibration techniques relies on building a model of the sky and corrupting it with models of the effects acting on the sources. The sensitivities of modern instruments call for more elaborate models to capture the level of detail that is required to achieve accurate calibration. This thesis implements two types of models to be used in for second- and third-generation calibration. The first model implemented is shapelets, which can be used to model radio source morphologies directly in uv space. The second model implemented is Zernike polynomials, which can be used to represent the primary beam of the antenna. We implement these models in the CODEX-AFRICANUS package and provide a set of unit tests for each model. Additionally, we compare our implementations against other methods of representing these objects and instrumental effects, namely NIFTY-GRIDDER against shapelets and a FITS-interpolation method against the Zernike polynomials. We find that to achieve sufficient accuracy, our implementation of the shapelet model has a higher runtime to that of the NIFTY-GRIDDER. However, the NIFTY-GRIDDER cannot simulate a component-based sky model while the shapelet model can. Additionally, the shapelet model is fully parametric, which allows for integration into a parameterised solver. We find that, while having a smaller memory footprint, our Zernike model has a greater computational complexity than that of the FITS-interpolated method. However, we find that the Zernike implementation has floating-point accuracy in its modelling, while the FITS-interpolated model loses some accuracy through the discretisation of the beam.
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Design patterns and software techniques for large-scale, open and reproducible data reduction
- Authors: Molenaar, Gijs Jan
- Date: 2021
- Subjects: Radio astronomy -- Data processing , Radio astronomy -- Data processing -- Software , Radio astronomy -- South Africa , ASTRODECONV2019 dataset , Radio telescopes -- South Africa , KERN (omputer software)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/172169 , vital:42172 , 10.21504/10962/172169
- Description: The preparation for the construction of the Square Kilometre Array, and the introduction of its operational precursors, such as LOFAR and MeerKAT, mark the beginning of an exciting era for astronomy. Impressive new data containing valuable science just waiting for discovery is already being generated, and these devices will produce far more data than has ever been collected before. However, with every new data instrument, the data rates grow to unprecedented quantities of data, requiring novel new data-processing tools. In addition, creating science grade data from the raw data still requires significant expert knowledge for processing this data. The software used is often developed by a scientist who lacks proper training in software development skills, resulting in the software not progressing beyond a prototype stage in quality. In the first chapter, we explore various organisational and technical approaches to address these issues by providing a historical overview of the development of radioastronomy pipelines since the inception of the field in the 1940s. In that, the steps required to create a radio image are investigated. We used the lessons-learned to identify patterns in the challenges experienced, and the solutions created to address these over the years. The second chapter describes the mathematical foundations that are essential for radio imaging. In the third chapter, we discuss the production of the KERN Linux distribution, which is a set of software packages containing most radio astronomy software currently in use. Considerable effort was put into making sure that the contained software installs appropriately, all items next to one other on the same system. Where required and possible, bugs and portability fixes were solved and reported with the upstream maintainers. The KERN project also has a website, and issue tracker, where users can report bugs and maintainers can coordinate the packaging effort and new releases. The software packages can be used inside Docker and Singularity containers, enabling the installation of these packages on a wide variety of platforms. In the fourth and fifth chapters, we discuss methods and frameworks for combining the available data reduction tools into recomposable pipelines and introduce the Kliko specification and software. This framework was created to enable end-user astronomers to chain and containerise operations of software in KERN packages. Next, we discuss the Common Workflow Language (CommonWL), a similar but more advanced and mature pipeline framework invented by bio-informatics scientists. CommonWL is supported by a wide range of tools already; among other schedulers, visualisers and editors. Consequently, when a pipeline is made with CommonWL, it can be deployed and manipulated with a wide range of tools. In the final chapter, we attempt something unconventional, applying a generative adversarial network based on deep learning techniques to perform the task of sky brightness reconstruction. Since deep learning methods often require a large number of training samples, we constructed a CommonWL simulation pipeline for creating dirty images and corresponding sky models. This simulated dataset has been made publicly available as the ASTRODECONV2019 dataset. It is shown that this method is useful to perform the restoration and matches the performance of a single clean cycle. In addition, we incorporated domain knowledge by adding the point spread function to the network and by utilising a custom loss function during training. Although it was not possible to improve the cleaning performance of commonly used existing tools, the computational time performance of the approach looks very promising. We suggest that a smaller scope should be the starting point for further studies and optimising of the training of the neural network could produce the desired results.
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Parametrised gains for direction-dependent calibration
- Authors: Russeeaeon, Cyndie
- Date: 2021
- Subjects: Radio astronomy , Radio inferometers , Radio inferometers -- Calibration
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/172400 , vital:42196
- Description: Calibration in radio interferometry describes the process of estimating and correcting for instrumental errors from data. Direction-Dependent (DD) calibration entails correcting for corruptions which vary across the sky. For small field of view observations, DD corruptions can be ignored but for wide fild observations, it is crucial to account for them. Traditional maximum likelihood calibration is not necessarily efficient in low signal-to-noise ratio (SNR) scenarios and this can lead to ovefitting. This can bias continuum subtraction and hence, restrict the spectral line studies. Since DD effects are expected to vary smoothly across the sky, the gains can be parametrised as a smooth function of the sky coordinates. Hence, we implement a solver where the atmosphere is modelled using a time-variant 2-dimensional phase screen with an arbitrary known frequency dependence. We assume arbitrary linear basis functions for the gains over the phase screen. The implemented solver is ptimised using the diagonal approximation of the Hessian as shown in previous studies. We present a few simulations to illustrate the performance of the solver.
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