Foreground separation of interferometric 21cm cosmological observations using convolutional neural networks

Abstract

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is an array of radio telescopes being deployed at the Square Kilometre Array in South Africa, which will generate terabytes of interferometric data daily. This paper investigates the use of convolutional autoencoders to separate cosmological signal from the foreground contaminations in the context of 21cm observations.

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A key point concerning the identi cation of the foreground is its spectrally smooth nature as a frequency function which, in principle, allows it to be separated from the target 21cm signal. The network used in this work is an autoencoder with an underlying convolutional neural network (CNN) architecture. The convolutional layers serve to convolve the input images, e ectively preserving spatial dependence whilst the hidden layers of the autoencoder act as an e ective feature detector, in this case identifying the signal data from the foreground contaminants. The network was evaluated using the loss and accuracy of predictions.

The visibility timestreams (input data) of HIRAX observations were simulated using Draco (a pipeline for the analysis and simulation of drift scan radio data), along with the following python packages: Driftscan, Cora, Caput. Simulated or synthetic data plays an integral role in the interpretation of observations. Due to the varying properties and calibrations a telescope may have it becomes challenging to keep track of all the anomalies that arise, which adds further constraints on the task of identifying and separating the target signal from the foreground.

(a) Prediction (b) Signal test data Fig. 1: Graphical results of a single channel, over frequencies ranging from 500 -700MHz over three days (72 hours), show a signi cant subtraction of contaminants. The predicted outcome (a) is compared against the signal test data (b).

Foregrounds are several orders of magnitude more intense than the 21cm signal and are highly correlated hence we track the progress of the model by plotting the predicted outcomes, the signal-only data and the original (contaminated) inputs - resulting in graphs of frequency (MHz) against time(hrs). The colour-bars generated aids in calculating the di erence in magnitudes of what is observed from the output vs. the signal-only data and is an estimate of how well the model performed. From the results obtained during this experiment, the network is able to recover some of the signal and has managed to subtract a signi cant amount of the foreground contaminants.

We conclude that deep learning techniques have the potential to perform well in this domain.

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