Teaching Machines to Find Fast Radio Bursts

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Title: Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach
Authors: Yunfan Gerry Zhang, Vishal Gajjar, Griffin Foster, Andrew Siemion, James Cordes, Casey Law, Yu Wang
First Author’s Institution: McGill University of California Berkeley
Status: Submitted to ApJ

Today’s astrobite combines two independently fascinating topics — machine learning and fast radio bursts (FRBs) — for a very interesting result. The field of machine learning is moving at an unprecedented pace with fascinating new results. FRBs have entirely unknown origins, and experiments to detect more of them are gearing up. So let’s jump right into it and take a look at how the authors of today’s astrobite got a machine to identify fast radio bursts.

Convolutional Neural Networks

Let’s begin by introducing the technique and machinery the authors employed for finding these signals. The field of machine learning is exceptionally hot right now, and with new understanding being introduced almost daily into the best machine-learning algorithms, the diffusion into nearby fields is accelerating. This is of course no exception for astronomy (radio or otherwise), where datasets grow to be extraordinarily large and intractable for classical algorithms. Enter the Convolutional Neural Network (CNN): the go-to machine-learning algorithm for understanding and prediction on data with spatial features (aka images). How does one of these fancy algorithms work? A basic starting point would be that of a traditional neural network, but I’ll leave that explanation to someone else. A generic neural network can take in few or many inputs, but the inputs don’t necessarily have to be spatially related to each other; CNNs, however, are well suited for images. (Note: you can also have one-dimensional or three-dimensional CNNs). These images have features that, when combined, are important for identifying what is in the image. In Figure 1, for example, the dog has features such as floppy ears, or a large mouth with a tongue protruding. A CNN learns some or all of these features from a provided training dataset with a known ground truth; in Figure 1, for instance, the prediction can be labeled dog, cat, lion, or bird. These features are learned at varying spatial scales as the input images are successively convolved over, and the prediction is compared to its known label, with any corrections propagated backwards to update those features. This latter step is the training part — which you might notice is the same process as a non-convolutional neural network. Thus armed with this blazingly fast classifier, we can move forward to understanding what we’ll be predicting on.

Fast Radio Bursts

We’ve covered FRBs on astrobites in the past (1, 2, 3), and with each new post we seem to be getting closer and closer to finding the origin of these mysterious radio signals. FRBs are radio-bright millisecond bursts seen in time–frequency radio telescope data. These bursts have unique features that set them apart from other radio signals and will be important for understanding how the authors developed an FRB training dataset for the predictions in their paper. These features consist of a dispersion measure (DM), time of arrival (TOA), amplitude, and pulse width (there are more, but I’ll highlight these as being the most important characteristics). The DM is one of the more interesting features of an FRB, as this is what indicates that FRBs are cosmological. The DM is measured from the dispersion of the signal in time and frequency as it traveled through an ionized medium — in this case, the intergalactic medium. This is that curved trait seen in Figure 2, which delays the signal to later times when moving to lower frequencies. TOA is when the signal arrived in the observations, amplitude is the flux density of the signal, and pulse width is the width at 10% of the maximum amplitude.

Using all of these characteristics to define a training dataset, the authors simulated many different types of FRBs, all with their own unique values. This is important because having a large, robust training dataset means you’re more likely to have a neural network capable of robust predictions.

Putting the CNN to Work

We now have all the components: a convolutional neural network, a robust training dataset, and a monumental amount of Green Bank Telescope (GBT) data. The authors seek to probe archival data of the now pretty well known FRB 121102, which has a history of being a repeating FRB. This means that FRB 121102 is an amazing resource for understanding FRBs because we can take many measurements.

Using several hours of GBT archival data, the authors set the CNN to work predicting whether there are additional FRB pulses from FRB 121102 that may have gone overlooked due to the signal being weak or just plain being missed due to the amount of data. They successfully find 72 additional pulses from FRB 121102! And interestingly enough, more than half of these newly discovered pulses happened within the first half-hour of this dataset. This brings the total tally, including the older signals, to 93 FRB pulses.

The additional detection and measurement of these pulses is certainly important. Like we’ve stated in our past astrobites, the origin of these bursts is almost completely speculative and we need to build up as many measurements as we can to either rule out or constrain the potential cosmological sources. Having a repeating FRB with which we can start to collect measurements, like the distributions seen in Figure 3, is fantastic for understanding the FRB’s environment affecting these parameters. Hopefully with the continued development of these CNNs and other machine-learning techniques, we’ll see an explosion of FRB detections.

About the author, Joshua Kerrigan:

I’m a 5th year PhD student at Brown University studying the early universe through the 21cm neutral hydrogen emission. I do this by using radio interferometer arrays such as the Precision Array for Probing the Epoch of Reionization (PAPER) and the Hydrogen Epoch of Reionization Array (HERA).