Radio Monitoring of Classical Novae using the ASKAP Variable and Slow Transients Survey

We present a search for radio emission from classical novae at 887.5 MHz using data from the Australian SKA Pathfinder Variable And Slow Transient (VAST) survey. We cross-matched 43 optically discovered classical novae that erupted between 2021 September and 2025 November 2 within the 1200 deg Galactic survey footprint, and found three which show significant radio emission: V6598 Sgr, V1716 Sco, and V1723 Sco. To analyse their radio light curves, we use both thermal free-free and non-thermal synchrotron emission models. We fit the data using the Markov chain Monte Carlo (MCMC) method to constrain parameters, including the ejected mass and ejecta velocities for the thermal models, and mass-loss rate, explosion energy, wind velocity, and filling factor for the non-thermal model. All three novae show evidence of non-thermal synchrotron emission as the dominant emission mechanism at this frequency. We use a broken power law to describe the radial density structure of a non-uniform circumbinary material, which provides a better fit than a standard wind density profile. This strong early-time synchrotron emission is strong evidence of shock-driven particle acceleration, which may be related to detections of gamma-rays from all three novae as well. In contrast to earlier studies that used multi-frequency data to distinguish between emission models, our analysis is based on single-frequency radio light curves, which can still provide useful constraints on the dominant emission mechanism when interpreted with physically motivated models.

Improvements to AI systems:

1. Physics-informed model selection for single-frequency time-series data

• Improvement: Train an AI system to automatically distinguish between thermal free-free and non-thermal synchrotron emission mechanisms using only single-frequency light curves, by embedding the MCMC-fitted broken power-law density profile and shock-driven particle acceleration priors from this paper.

• What it can do: Given a radio light curve from any transient (e.g., novae, supernovae), the AI can classify the dominant emission mechanism and estimate physical parameters (ejected mass, ejecta velocity, mass-loss rate, explosion energy, wind velocity, filling factor) without needing multi-frequency observations, reducing telescope time and enabling real-time classification.

2. Automated detection of non-uniform circumbinary density structures

• Improvement: Incorporate the broken power-law radial density model (instead of a standard wind profile) into an AI-based fitting engine, using the paper’s finding that this provides better fits for shock-driven emission.

• What it can do: The AI can automatically identify whether a transient’s environment has a non-uniform density profile (e.g., from binary interactions or prior eruptions) by comparing Bayesian evidence between models, and then output the break radius and power-law indices—useful for predicting future outbursts or gamma-ray associations.

3. Cross-matching and prioritization of transient candidates

• Improvement: Build an AI pipeline that replicates the paper’s cross-matching logic (optical nova catalog vs. radio survey footprint) and uses the three detected novae as training examples to learn which optical properties (e.g., eruption date, location, brightness) correlate with detectable radio synchrotron emission.

• What it can do: The AI can rank newly discovered optical novae by predicted radio detectability, allowing telescopes to prioritize follow-up observations and catch early-time shock-driven emission before it fades—maximizing scientific yield from limited observing time.

4. Joint inference of gamma-ray and radio emission from shock acceleration

• Improvement: Use the paper’s correlation between synchrotron radio and gamma-ray detections to train a multi-wavelength AI model that predicts gamma-ray flux from radio light curve shape (or vice versa), leveraging the shared shock-driven particle acceleration physics.

• What it can do: Given only a radio light curve, the AI can forecast whether gamma-ray emission is likely and estimate its timing and intensity, enabling coordinated multi-messenger observations and testing of particle acceleration models across wavelengths.

5. Uncertainty-aware parameter estimation with sparse data

• Improvement: Implement a neural network that mimics the MCMC posterior distributions from this paper, but trained to handle very sparse (e.g., 3–5 data points) single-frequency light curves, using the paper’s three novae as ground truth.

• What it can do: The AI can provide reliable posterior estimates (with credible intervals) for ejected mass and explosion energy even when only a few radio measurements exist, which is critical for early-stage transients where data is scarce—improving rapid response and theoretical model validation.

We present a search for radio emission from classical novae at 887.5 MHz using data from the Australian SKA Pathfinder Variable And Slow Transient (VAST) survey. We cross-matched 43 optically discovered classical novae that erupted between 2021 September and 2025 November within the 1200-square-degree Galactic survey footprint, and found three which show significant radio emission: V6598 Sgr, V1716 Sco, and V1723 Sco. To analyse their radio light curves, we use both thermal free-free and non-thermal synchrotron emission models. We fit the data using the Markov chain Monte Carlo (MCMC) method to constrain parameters, including the ejected mass and ejecta velocities for the thermal models, and mass-loss rate, explosion energy, wind velocity, and filling factor for the non-thermal model. All three novae show evidence of non-thermal synchrotron emission as the dominant emission mechanism at this frequency. We use a broken power law to describe the radial density structure of a non-uniform circumbinary material, which provides a better fit than a standard wind density profile. This strong early-time synchrotron emission is strong evidence of shock-driven particle acceleration, which may be related to detections of gamma-rays from all three novae as well. In contrast to earlier studies that used multi-frequency data to distinguish between emission models, our analysis is based on single-frequency radio light curves, which can still provide useful constraints on the dominant emission mechanism when interpreted with physically motivated models