Revisiting the Unknown Unknowns in Transient Astrophysics with the SKA Telescopes

Introduction to the show: ident: Astrophysics. The day in astrophysics: observations, models and what the data forces.

Vera: I'm Vera.

Jocelyn: And I'm Jocelyn, here with Subrahmanyan.

Vera: We're looking at the paper: "Revisiting the Unknown Unknowns in Transient Astrophysics with the SKA Telescopes".

Jocelyn: Here's the gist of the paper. The paper revisits how to explore new areas in transient astrophysics using the SKA telescopes by focusing on "unknown unknowns" that have been missed despite previous explorations The Concept of Unknown Unknowns The exploration of the unknown is an extremely important concept in realising the potential of any new instrument While this Chapter considers transients and environments, the ethos of these previous Chapters should be considered and embraced broadly and can be applied more broadly across SKAO science We should utilise approaches such as machine learning in allowing us to address the complexity that arises from the expanded parameter space and increased data rates and candidate sources The importance of commensal observing for the discovery of “unknown unknowns” in particular, cannot be overstated Commensal searches offer two key advantages: the ability to access large amounts of observing time (essential for discovering rare events), and the probing of regions of parameter space that would not be chosen for dedicated observations Expanding the Parameter Space Radio astronomers now probe an expansive transient parameter space, defined by time, frequency, spatial scale, and all senses of electric field polarisation Time is a striking feature of the radio transient zoo with an enormous range of temporal scales spanning over 17 orders of magnitude Progress rests on three methodological pillars: high time resolution to resolve intrinsic structure, improving sensitivity for transients with durations seconds to minutes to capture longer period sources, and long-term monitoring to chart long-term variability Frequency is a second, critical axis of their characterisation where wide frequency coverage boosts instantaneous sensitivity and reveals unusual spectral windows in both known and newly discovered sources Polarisation yields valuable diagnostics of the magnetoionic environment surrounding the transient’s source and along the line of sight New Transient Populations Unveiled The SKA precursors and pathfinders have unveiled several new classes of sources that warrant further investigation These include: 1.

Vera: The long-period radio pulsars, whose detection has expanded the known parameter space of neutron star radio emission dramatically 2.

Jocelyn: Fast Radio Bursts (FRBs), for which the SKA telescopes will push the discovery potential into reaching higher time resolution as well as sensitive image plane searches 3.

Vera: The long-period radio transients, which were established through new “fast imaging” searches and may have multiple progenitors 4.

Title and authors: Vera: This paper focuses on how to use these concepts—the unknown unknowns—to explore new areas in transient astrophysics using the SKA telescopes. It’s about expanding our search beyond what we already know.

Jocelyn: The authors are Alex Andersson, Manisha Caleb, Joseph Callingham, Adam Deller, Dougal Dobie, Natasha Hurley-Walker, Benito Marcote, Kaustubh Rajwade, Antonia Rowlinson, Benjamin Stappers and Matteo Trudu. They bring a lot of different expertise to this problem.

Subrahmanyan: The paper essentially argues that we need to apply the spirit of "unknown unknowns" broadly across all SKAO science. It’s not just about transients; it’s about how we approach complexity in the expanded parameter space and increased data rates.

Vera: They stress using machine learning as a core tool here, not just for cleaning up data after the fact. They suggest ML can help us deal with that massive increase in complexity from all those new dimensions we're looking at.

Jocelyn: That brings up the idea of expanding the parameter space itself, which includes time, frequency, spatial scale, and all kinds of electric field polarization. It’s a lot to track simultaneously.

Subrahmanyan: The paper points out that we have already seen value in these approaches with discoveries like long period radio pulsars and fast radio bursts. Now they are pushing the limits further into new directions across all those domains.

The paper's summary: Vera: They summarize that our progress over the last decade, with things like FRBs and long period pulsars, has shown us how these approaches work in practice. But now, with SKA, they want to push that exploration even further into entirely new areas.

Jocelyn: The summary highlights the importance of high time resolution to see intrinsic structure, improving sensitivity for those seconds-to-minutes sources, and long-term monitoring for charting variability over long times.

Subrahmanyan: They also talk about frequency as a second critical axis, where wide coverage boosts instantaneous sensitivity and lets us find unusual spectral windows in both known sources and new ones. Polarization is also mentioned as a tool to diagnose the magnetoionic environment around these transients.

Vera: The paper then lists several new transient populations that have been unveiled by SKA precursors, including long-period radio pulsars, fast radio bursts, and long-period radio transients from new "fast imaging" searches.

Jocelyn: They also mention sources in polarization like new pulsars and stellar emission. It’s a broad sweep of what the SKA is already revealing about different types of objects across all these dimensions.

Subrahmanyan: The core message is that this chapter reinforces the search for unknown unknowns, urging us to be open to anomalies in the data that could reveal entirely new classes of astrophysical sources.

The paper's improvements: Vera: So what are the suggested improvements? They really push for using machine learning in a way that is a core part of the scientific workflow, not just a post-processing convenience.

Jocelyn: Specifically, they mention unsupervised machine learning techniques because they don't need training data, making them computationally less demanding and suitable for immediate use with SKAO data streams.

Subrahmanyan: They also suggest using AI to enhance search strategies by implementing novel search methods. We can improve on the current approach by using unsupervised machine learning for anomaly detection, which is good for finding those elusive radio transients.

Vera: Another improvement they suggest is using machine learning pipelines to reduce the volume of candidates through real-time processing, similar to what we see in things like CRAFT or MeerTRAP backends. This leads to automated SKAO transient alerts.

Jocelyn: And they also suggest designing the AI systems to use DM-t plane searches for fast transients. That should significantly reduce the number of candidates that need clustering and sifting compared to standard thresholding techniques, which is a big computational win.

Conclusion: Vera: So, wrapping up this discussion on "Revisiting the Unknown Unknowns in Transient Astrophysics with the SKA Telescopes," it really boils down to the vital need to use ML and commensal observing to push our discovery space further.

Jocelyn: The authors reiterate that these tools are essential not just for finding known sources, but also for pursuing those anomalies in the data that might reveal entirely new wonders. It’s about making SKA a true discovery instrument across all classes of sources.

Subrahmanyan: From my side, this paper emphasizes how crucial it is to have the SKA telescopes and its analysis software work together as one discovery instrument for both known and unknown astrophysical sources. We need that synergy to see what else is out there.

Vera: It’s a call to keep our eyes open, use those new tools like AI, and continue looking for what we don't yet have a model for. It gives us a clear direction for future searches in this massive data environment.

Jocelyn: I think the real implication is that the search strategy needs to be flexible enough to handle the sheer volume and complexity SKA will bring, allowing us to catch those unexpected signals before they disappear.

Subrahmanyan: We need that flexibility because without it, we risk only finding things that fit our existing models of what a transient should look like. This paper pushes us toward genuinely new science driven by data-driven exploration.