Modeling the Unknown: A New Tool for Radio Bursts

What is the source of radio transients? Astronomers still aren’t sure, but that’s not stopping them from modeling their observations of these mysterious flashes.

The Challenge of Modeling a Mystery

Imagine a radio astronomer in the first moments following an alert that their telescope just recorded something strange in the sky. Their computer informs them that for barely a millisecond, it spotted a mighty flash of radio waves (aptly called a fast radio burst), but before any humans knew something was happening, the flash had already faded. What was that?

In some fields of astronomy, the next steps would be obvious. The scientist would need to write a model that simulates the physics of some known system, then fiddle with the input parameters of that model until its outputs resemble their data. This radio astronomer is not so lucky, though. That’s because although there are many good ideas and many theorists actively working on it, scientists still do not know the source of fast radio bursts.

So, what is the radio astronomer to do? How can you fit a model and learn anything about what you just saw when you don’t know what caused it? That’s where the authors of a recent publication led by Emmanuel Fonseca, West Virginia University, come in.

A New Tool

Observed data of a fast radio burst (left), the best-fitting fitburst model (center), and the residuals to the fit (right). Click to enlarge. [Fonseca et al. 2024]

Fonseca and collaborators created a flexible model that is able to reproduce a wide range of different pulse shapes and sizes, then coded it all up as an open-source Python package called fitburst. Some of their input parameters, like the dispersion measure, correspond to physical quantities, and they include every bit of realistic physics that they can. Other input parameters, however, are just heuristics. Fitting all of the parameters in their model won’t tell you why certain frequencies remained dim while others flared, but it will tell you the relationship between frequency and peak brightness.

That’s a crucial intermediate step towards developing a more complete theory of fast radio bursts, since it allows scientists to classify the population of observed bursts even without a full understanding of their underlying cause. Already astronomers have noted that there seem to be at least a few distinct types of fast radio bursts, and with a tool like fitburst, they can begin to quantify the differences between these populations.

Careful and Complete Implementation

A fit to a different fast radio burst, which arrived at several staggered, frequency-dependent times. Click to enlarge. [Fonseca et al. 2024]

Fonseca and the team also derived analytic expressions for the derivatives of each of their input parameters, which unlocked a powerful family of model-fitting algorithms that rely on this extra information to find the best values. In a series of comparisons with real observations of fast radio bursts, they convincingly demonstrate both that these algorithms can find best-fitting solutions, and also that these solutions closely resemble the observed data.

Excitingly, the researchers also noted that the fitburst model is flexible enough to fit other types of pulses as well. Although designed primarily for fast radio bursts, it can also be used to analyze observations of pulsars and other radio transients. The team encourages all radio astronomers to take fitburst for a spin, and they themselves already list four distinct projects underway. The future of fitburst is bright, much like the mysterious flashes it models.


“Modeling the Morphology of Fast Radio Bursts and Radio Pulsars with fitburst,” E. Fonseca et al 2024 ApJS 271 49. doi:10.3847/1538-4365/ad27d6