The Square Kilometre Array (SKA)

Abstract:

Aims. The orientation-based unification scheme of radio-loud active galactic nuclei (AGNs) asserts that radio galaxies and quasars are essentially the same type of object, but viewed from different angles. To test this unification model, we compared the environments of radio galaxies and quasars, which would reveal similar properties when an accurate model is utilized. Methods. Using the second data release of the LOFAR Two-metre Sky Survey (LoTSS DR2), we constructed a sample of 26 577 radio galaxies and 2028 quasars at 0.08 < z < 0.4. For radio galaxies with optical spectra, we further classified them as 3631 low-excitation radio galaxies (LERGs) and 1143 high-excitation radio galaxies (HERGs). We crossmatched these samples with two galaxy cluster catalogs from the Sloan Digital Sky Survey (SDSS). Results. We find that 17.1 ± 0.2% of the radio galaxies and 4.1 ± 0.4% of the quasars are associated with galaxy clusters. Luminous quasars are very rare in clusters, while 18.7 ± 0.7% LERGs and 15.2 ± 1.1% HERGs reside in clusters. We also note that in radio galaxies, both HERGs and LERGs tend to reside in the centers of clusters, while quasars do not show a strong preference for their positions in clusters. Conclusions. This study shows that local quasars and radio galaxies exist in different environments, challenging the orientation-based unification model. This means that factors other than orientation may play an important role in distinguishing radio galaxies from quasars. The future WEAVE-LOFAR survey will offer high-quality spectroscopic data for a large number of radio sources and allow for a more comprehensive exploration of the environments of radio galaxies and quasars.

Abstract:

<jats:title>ABSTRACT</jats:title> <jats:p>In this work, we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters (‘feature sets’) as applied to two anomaly detection techniques in the astronomaly package and analyse our performance by comparison with citizen science labels on the same data set. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best-performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other data sets, and as a real-time transient detection system for upcoming large surveys.</jats:p>