Browsing by Author "Lochner, Michelle"

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    Open Access
    Bayesian inference for radio observations
    (Oxford University Press, 25) Lochner, Michelle; Natarajan, Iniyan; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
    New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software MEQTREES to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for ‘super-resolution’ on scales much smaller than the synthesized beam.
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    Open Access
    Classification of multiwavelength transients with machine learning
    (2019) Sooknunan, Kimeel; Lochner, Michelle; Bassett, Bruce
    With the advent of powerful telescopes such as the Square Kilometre Array (SKA), its precursor MeerKAT and the Large Synoptic Survey Telescope (LSST), we are entering a golden era of multiwavelength transient astronomy. The large MeerKAT science project ThunderKAT may dramatically increase the detected number of radio transients. Currently radio transient datasets are still very small, allowing spectroscopic classification of all objects of interest. As the event rate increases, follow-up resources must be prioritised by making use of early classification of the radio data. Machine learning algorithms have proven themselves invaluable in the context of optical astronomy, however it has yet to be applied to radio transients. In the burgeoning era of multimessenger astronomy, incorporating data from different telescopes such as MeerLICHT, Fermi, LSST and the gravitational wave observatory LIGO could significantly improve classification of events. Here we present MALT (Machine Learning for Transients): a general machine learning pipeline for multiwavelength transient classification. In order to make use of most machine learning algorithms, "features" must be extracted from complex and often high dimensional datasets. In our approach, we first interpolate the data onto a uniform grid using Gaussian processes, we then perform a wavelet decomposition and finally reduce the dimensionality using principal component analysis. We then classify the light curves with the popular machine learning algorithm random forests. For the first time, we apply machine learning to the classification of radio transients. Unfortunately publicly available radio transient data is scarce and our dataset consists of just 87 light curves, with several classes only consisting of a single example. However machine learning is often applied to such small datasets by making use of data augmentation. We develop a novel data augmentation technique based on Gaussian processes, able to generate new data statistically consistent with the original. As the dataset is currently small, three studies were done on the effect of the training set. The classifier was trained on a non-representative training set, achieving an overall accuracy of 77.8% over all 11 classes with the known 87 lightcurves with just eight hours of observations. The expected increase in performance, as more training data are acquired, is shown by training the classifier on a simulated representative training set, achieving an average accuracy of 95.8% across all 11 classes. Finally, the effectiveness of including multiwavelength data for general transient classification is demonstrated. First the classifier is trained on wavelet features and a contextual feature, achieving an average accuracy of 72.9%. The classifier was then trained on wavelet features and a contextual feature, together with a single optical flux feature. This addition improves the overall accuracy to 94.7%. This work provides a general approach for multiwavelength transient classification and shows that machine learning can be highly effective at classifying the influx of radio transients anticipated with MeerKAT and other radio telescopes.
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    Open Access
    FireFly: A Bayesian Approach to Source Finding in Astronomical Data
    (2019) Moloko, Oarabile Hope; Lochner, Michelle; Bassett, Bruce
    Efficient and rigorous source finding techniques are needed for the upcoming large data sets from telescopes like MeerKAT, LSST and the SKA. Most of the current source-finding algorithms lack full statistical rigor. Typically these algorithms use some form of thresholding to find sources, which leads to contamination and missed sources. Ideally we would like to use all the available information when performing source detection, including any prior knowledge we may have. Bayesian statistics is the obvious approach as it allows precise statistical interrogations of the data and the inclusion of all available information. In this thesis, we implement nested sampling and Monte Carlo Markov Chain (MCMC) techniques to develop a new Bayesian source finding technique called FireFly. FireFly employs a technique of switching ‘on’ and ‘off’ sources during sampling to deal with the fact that we don’t know how many true sources are present. It therefore tackles one of the critical questions in source finding, which is estimating the number of real sources in the image. We compare FireFly against a Bayesian evidence-based search method and show on simulated astronomical images that FireFly outperforms the evidence-based approach. We further investigate two implementations of FireFly: the first with nested sampling and the second with MCMC. Our results show that MCMC FireFly has better computational scaling than the nested sampling version FireFly but the nested sampling version of FireFly appears to perform somewhat better than MCMC FireFly. Future work should examine how best to quantify FireFly performance and extend the formalism developed here to deal with multiwavelength data.