Browsing by Author "Taylor, Russ"

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    Detecting the magnetic cosmic web through deep radio polarization imaging
    (2017) Burnham-King, Lauren S; Van Der Heyden, Kurt; Taylor, Russ
    The polarisation of radio emission is one of the most powerful probes of magnetic fields in the cosmos. Faraday rotation of polarized radiation provides one of the methods to observe magnetic fields. Measuring the rotation of the polarisation angle of radiation from an extragalactic source over a broad radio bandwidth allows us to infer the properties of the magnetic fields that the radiation passed through on the path to the observer. In the last few decades, the presence of structure in the matter distribution of the universe has been observed. It remains an open question whether there are magnetic fields associated with this large-scale structure. Large-scale universe simulations allow us to investigate the effect of extragalactic magnetic fields on the spatial distribution of Rotation Measure (RM) of radio sources that will be detected in deep radio images with MeerKAT. We constructed lightcones out to z = 1 from large-scale universe simulations as a base for our model and assemble a routine to trace large scale structures, attach magnetic fields to the structure and construct RM observations. The aim is to explore whether deep MeerKAT continuum observations will be able to detect magnetic fields associated with large-scale structure (the so-called magnetic cosmic web).
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    Machine Learning for Radio Frequency Interference Flagging
    (2021) Harrison, Kyle; Mishra, Amit; Taylor, Russ
    The field of radio frequency interference (RFI) flagging involves the identification of corrupted data within radio astronomy measurements. This work explores the application of supervised machine learning algorithms for RFI flagging, trained on real measurement data and simulated data with simulated RFI. The goal of this work is to investigate the prediction of RFI using specific machine learning algorithms; Naive Bayes Classifier, K-Nearest Neighbours Classifier, Random Forest Classifier, the U-Net convolution neural network and the Multilayer Perceptron. These algorithms are trained on real data, in which the ground truth includes inherent false positives, and simulated data where the ground truth positions of RFI are absolute. This is done through the use of time/frequency spectrogram data, relating to radio astronomy measurements, using the magnitudes and phases of each available polarization. Predictions for unseen test data are compared between algorithms, different implementations of those algorithms and each dataset. A specific implementation for data pre-processing is designed and implemented, utilizing a two dimensional filtering technique for feature construction. The goal of this method is intended to implement a means of injecting a form of spatial information of nearby time/frequency samples for each sample in a spectrogram. The inclusion of this spacial information, which is relevant to broadband bursts and narrowband persistent RFI, is hypothesised to increase the level of information present in the processed dataset. The use of feature construction using filtering techniques, demonstrates a noticeable improvement in the machine learning methods where each sample is treated individually during training and inference.