Pulsars, transients and relativistic astrophysics

Abstract:

Abstract Black hole (BH) superradiance can provide strong constraints on the properties of ultralight bosons (ULBs). While most of the previous work has focused on the theoretical predictions, here we investigate the most suitable statistical framework to constrain ULB masses and self-interactions using BH spin measurements. We argue that a Bayesian approach based on a simple timescales analysis provides a clear statistical interpretation, deals with limitations regarding the reproducibility of existing BH analyses, incorporates the full information from BH data, and allows us to include additional nuisance parameters or to perform hierarchical modelling with BH populations in the future. We demonstrate the feasibility of our approach using mass and spin posterior samples for the X-ray binary BH M33 X-7 and, for the first time in this context, the supermassive BH IRAS 09149-6206. We explain the differences to existing ULB constraints in the literature and illustrate the effects of various assumptions about the superradiance process (equilibrium regime vs cloud collapse, higher occupation levels). As a result, our procedure yields the most statistically rigorous ULB constraints available in the literature, with important implications for the QCD axion and axion-like particles. We encourage all groups analysing BH data to publish likelihood functions or posterior samples as supplementary material to facilitate this type of analysis, and for theory developments to compress their findings to effective timescale modifications. https://github.com/sebhoof/bhsr

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.