Hintze Centre for Astrophysical Surveys

Abstract:

Using the Dark Energy Survey 5-yr sample, we determine the properties of type Ia supernova (SN Ia) host galaxies across a wide multiwavelength range – from the optical to far-infrared – including data from the Herschel and Spitzer space telescopes. We categorize the SNe Ia into three distinct groups according to the distribution of their host galaxies on the star formation rate (SFR) – stellar mass () plane. Each region comprises host galaxies at distinct stages in their evolutionary pathways: Region 1 – low-mass hosts; Region 2 – high-mass, star-forming hosts and Region 3 – high-mass, passive hosts. We find SNe Ia in host galaxies located in Region 1 have the steepest slope (quantified by ) between their colours and luminosities, with . This differs at the significance level to SNe Ia in Region 3, which have the shallowest colour–luminosity slope with . After correcting SNe Ia in each subsample by their respective , events in Region 3 (high-mass, passive hosts) are mag () brighter, post-standardization. We conclude that future cosmological analyses should apply standardization relations to SNe Ia based upon the region in which the SN host galaxy lies in the SFR– plane. Alternatively, cosmological analyses should restrict the SN Ia sample to events whose host galaxies occupy a single region of this plane.

Abstract:

Precessing relativistic jets launched by compact objects are rarely directly measured, and present an invaluable opportunity to better understand many features of astrophysical jets. In this Letter we present MeerKAT radio observations of the neutron star X-ray binary system (NSXB) Circinus X-1 (Cir X-1). We observe a curved S-shaped morphology on scales in the radio emission around Cir X-1. We identify flux density and position changes in the S-shaped emission on year time-scales, robustly showing its association with relativistic jets. The jets of Cir X-1 are still propagating with mildly relativistic velocities from the core, the first time such large scale jets have been seen from a NSXB. The position angle of the jet axis is observed to vary on year time-scales, over an extreme range of at least . The morphology and position angle changes of the jet are best explained by a smoothly changing launch direction, verifying suggestions from previous literature, and indicating that precession of the jets is occurring. Steady precession of the jet is one interpretation of the data, and if occurring, we constrain the precession period and half-opening angle to yr and , respectively, indicating precession in a different parameter space to similar known objects such as SS 433.

Abstract:

We present the Virtual Research Assistant (VRA) of the ATLAS sky survey, which performs preliminary eyeballing on our clean transient data stream. The VRA uses histogram-based gradient-boosted decision tree classifiers trained on real data to score incoming alerts on two axes: “Real” and “Galactic.” The alerts are then ranked using a geometric distance such that the most “real” and “extragalactic” receive high scores; the scores are updated when new lightcurve data is obtained on subsequent visits. To assess the quality of the training we use the recall at rank K, which is more informative to our science goal than general metrics (e.g., accuracy, F1-scores). We also establish benchmarks for our metric based on the pre-VRA eyeballing strategy, to ensure our models provide notable improvements before being added to the ATLAS pipeline. Then, policies are defined on the ranked list to select the most promising alerts for humans to eyeball and to automatically remove bogus alerts. In production the VRA method has resulted in a reduction in eyeballing workload by 85% with a loss of follow-up opportunity <0.08%. It also allows us to automatically trigger follow-up observations with the Lesedi telescope, paving the way toward automated methods that will be required in the era of LSST. Finally, this is a demonstration that feature-based methods remain extremely relevant in our field, being trainable on only a few thousand samples and highly interpretable; they also offer a direct way to inject expertise into models through feature engineering.