Dharmesh Mistry

Cataclysmic variables (CV) encompass a diverse array of accreting white dwarf binary systems. Each class of CV represents a snapshot along an evolutionary journey, one with the potential to trigger a type Ia supernova event. The study of CVs offers valuable insights into binary evolution and accretion physics, with the rarest examples potentially providing the deepest insights. However, the escalating number of detected transients, coupled with our limited capacity to investigate them all, poses challenges in identifying such rarities. Machine learning (ML) plays a pivotal role in addressing this issue by facilitating the categorization of each detected transient into its respective transient class. Leveraging these techniques, we have developed a two-stage pipeline tailored to the Zwicky Transient Facility transient alert stream. The first stage is alerts filter aimed at removing non-CVs, while the latter is an ML classifier produced using Extreme Gradient Boosting, achieving a macro average area under the curve score of 0.92 for distinguishing between CV classes. By utilizing the generative topographic mapping algorithm with classifier posterior probabilities as input, we obtain representations indicating that CV evolutionary factors play a role in classifier performance, while the associated feature maps present a potent tool for identifying the features deemed most relevant for distinguishing between classes. Implementation of the pipeline in 2023 June yielded 51 intriguing candidates that are yet to be reported as CVs or classified with further granularity. Our classifier represents a significant step in the discovery and classification of different CV classes, a domain of research still in its infancy.

Wide-field time domain facilities detect transient events in large numbers through difference imaging. For example, Zwicky Transient Facility produces alerts for hundreds of thousands of transient events per night, a rate set to be dwarfed by the upcoming Vera C. Rubin Observatory. The automation provided by machine learning (ML) is therefore necessary to classify these events and select the most interesting sources for follow-up observations. Cataclysmic variables (CVs) are a transient class that are numerous, bright, and nearby, providing excellent laboratories for the study of accretion and binary evolution. Here we focus on our use of ML to identify CVs from photometric data of transient sources published by the Gaia Science Alerts (GSA) program – a large, easily accessible resource, not fully explored with ML. Use of light-curve feature extraction techniques and source metadata from the Gaia survey resulted in a random forest model capable of distinguishing CVs from supernovae, active galactic nuclei, and young stellar objects with a 92 per cent precision score and an 85 per cent hit rate. Of 13 280 sources within GSA without an assigned transient classification our model predicts the CV class for ∼2800. Spectroscopic observations are underway to classify a statistically significant sample of these targets to validate the performance of the model. This work puts us on a path towards the classification of rare CV subtypes from future wide-field surveys such as the Legacy Survey of Space and Time.

Between 2015 September and 2016 January, we obtained 18 spectra of the 2015 classical outburst of AG Pegasi—a symbiotic star consisting of a white dwarf (WD), red giant, and surrounding nebula. Modelling the flux contributions of these components reveals that nebular emission, from the reprocessing of high energy WD photons, dominates the 3200–6300 Å range. Nebular emission rises and falls in line with changes seen in the WD, whose properties have been derived using Hβ and He ii (4686 A) line flux, and emission measure calculations. WD parameters follow changes seen in visual band light curves. During the second peak of the outburst, WD temperatures reach 166,000 ± 6000 K, with a luminosity and radius of 14,000 ± 2000 L _⊙ and 0.149 ± 0.014 R _⊙ respectively. These features are consistent with an expansion of the WD pseudo-photosphere due to an accretion rate exceeding that required for stable hydrogen burning.

In 2015 June, the symbiotic binary star AG Pegasi underwent a period of outburst. SkyCam T, mounted onboard the 2 m Liverpool Telescope, collected over 650 unfiltered images of this event, and several thousand images from before and after. This note presents Sloan r′ band light curves obtained from these images. In addition, a color–magnitude diagram (CMD) using AAVSO data is included to infer parameters of the system’s white dwarf (WD). Features in the light curve allow one to classify this outburst as that of classical (or Z And) type. The CMD suggests the temperature and luminosity of the WD increase concurrently to produce the light curve maxima. Light curves either side of the event show wave-like oscillations owing to the orbital period of AG Pegasi’s components, indicating quiescence.