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Abstract:

We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array archive data observed at 4–8 GHz. The system is a triple consisting of a tight binary () of two M dwarfs of spectral types M4.5 and M6.5 and a wide T3 brown dwarf companion (16.4 arcsec). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H emission. We detect the unresolved binary at a peak flux density of at a signal-to-noise ratio (SNR) of and determine a radio luminosity of . The radio emission is quiescent, polarized at a mean circular polarization fraction % and exhibits a spectral index . We probe the binary using the Enhanced Multi-Element Remotely Linked Interferometer Network (e-MERLIN) with an angular resolution of mas at 5 GHz and detect a component at a peak flux density of Jy at a SNR . We propose a gyrosynchrotron origin for the radio emission and estimate a magnetic field strength G, an emitting region of size times the radius of the M4.5 primary and a plasma number density . The brown dwarf companion is not detected. Additionally, we have analysed observations of 2MASS J04183483+213127, a chromospherically active L5 brown dwarf which is also not detected and can only place flux density upper limits at Jy and Jy for Stokes I and V, respectively.

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.