Patrick Alan Woudt

Wide-field optical transient surveys collectively discover ∼50 new transients per night, making rapid, multi-filter and/or spectroscopic follow-up of all but the most exceptional events unfeasible. Consequently, the (early) light-curve properties of most supernovae (SNe), particularly sub-luminous events, are often poorly characterised. Here, we showcase recovery of key parameters by combining publicly available data from multiple, contemporaneous surveys. We built a catalogue of 125 SNe by cross-matching the MeerLICHT (ML) transient survey with the Transient Name Server list of spectroscopically classified SNe detected between 2017 August and 2022 October. For a subset of 14 SNe with good multi-survey photometric coverage, we combined ML- q , ZTF- g , r , and ATLAS- c , o data to construct composite early-time light curves. We then applied an expanding fireball ( t ² ) model to constrain their explosion dates and rest-frame rise times. We find that this simple model provides a reliable description for the early emission of most SNe in our sample and successfully constrains their rise time, a key parameter for characterising SN categories and probing progenitor origins. This multi-survey approach, only using standard survey products, provides a powerful method to characterise large numbers of SNe that would otherwise be lost, a technique that will be essential for maximising the scientific return from current and future surveys such as GOTO, BlackGEM, the Vera C. Rubin Observatory’s LSST, and Global Open Transient Telescope Array (GOTTA).

Context. Black holes (BHs) in microquasars can launch powerful relativistic jets that have the capacity to travel up to several parsecs from the compact object and interact with the interstellar medium. Recently, the detection of large-scale very-high-energy (VHE) gamma-ray emission around the black hole transient V4641 Sgr and other BH-jet systems suggested that jets from microquasars may play an important role in the production of galactic cosmic rays. Aims. V4641 Sgr is known for its superluminal radio jet discovered in 1999, but no radio counterpart of a large-scale jet has been observed. The goal of this work is to search for a radio counterpart of the extended VHE source. Methods. We observed V4641 Sgr with the MeerKAT radio telescope at the L and UHF bands and produced deep maps of the field using high dynamic range techniques. Results. We report the discovery of a large-scale (∼35 pc), bow-tie-shaped, diffuse, radio structure around V4641 Sgr, with similar angular size to the extended X-ray emission discovered by XRISM. However, it is not spatially coincident with the extended VHE emission. After discussing the association of the structure with V4641 Sgr, we investigate the nature of the emission mechanism. We suggest that the bow-tie structure arose from the long-term action of large-scale jets or disk winds from V4641 Sgr. If the emission mechanism is of synchrotron origin, the radio/X-ray extended structure implies acceleration of electrons up to more than 100 TeV as far as tens of parsecs from the black hole.

We present the longest and the densest quasi-simultaneous radio, X-ray, and optical campaign of the black hole low-mass X-ray binary GX 339−4, covering five years of weekly GX 339−4 monitoring with MeerKAT, Swift/XRT, and MeerLICHT, respectively. Complementary high-frequency radio data with the Australia Telescope Compact Array are presented to track in more detail the evolution of GX 339−4 and its transient ejecta. During the five years, GX 339−4 has been through two ‘hard-only’ outbursts and two ‘full’ outbursts, allowing us to densely sample the rise, quenching, and re-activation of the compact jets. Strong radio flares were also observed close to the transition between the hard and the soft states. Following the radio flare, a transient optically thin ejection was spatially resolved during the 2020 outburst, and was observed for a month. We also discuss the radio/X-ray correlation of GX 339−4 during this five year period, which covers several states in detail from the rising phase to the quiescent state. This campaign allowed us to follow ejection events and provide information on the jet proper motion and its intrinsic velocity. With this work, we publicly release the weekly MeerKAT L-band radio maps from data taken between 2018 September and 2023 October.

Black holes (BHs) in microquasars can launch powerful relativistic jets that have the capacity to travel up to several parsecs from the compact object and interact with the interstellar medium. Recently, the detection of large-scale very-high-energy (VHE) gamma-ray emission around the black hole transient V4641 Sgr and other BH-jet systems suggested that jets from microquasars may play an important role in the production of galactic cosmic rays. V4641 Sgr is known for its superluminal radio jet discovered in 1999, but no radio counterpart of a large-scale jet has been observed. The goal of this work is to search for a radio counterpart of the extended VHE source. We observed V4641 Sgr with the MeerKAT radio telescope at the L and UHF bands and produced deep maps of the field using high dynamic range techniques. We report the discovery of a large-scale (35 pc), bow-tie-shaped, diffuse, radio structure around V4641 Sgr, with similar angular size to the extended X-ray emission discovered by XRISM. However, it is not spatially coincident with the extended VHE emission. After discussing the association of the structure with V4641 Sgr, we investigate the nature of the emission mechanism. We suggest that the bow-tie structure arose from the long-term action of large-scale jets or disk winds from V4641 Sgr. If the emission mechanism is of synchrotron origin, the radio/X-ray extended structure implies acceleration of electrons up to more than 100 TeV as far as tens of parsecs from the black hole.

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely η _ν and V _ν , and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

The sensitivity and field of view of the MeerKAT radio telescope provide excellent opportunities for commensal transient searches. We carry out a commensal transient search in supernova and short gamma-ray burst fields using methodologies established by S. I. Chastain et al. We search for transients in MeerKAT L-band images with integration times of 30 minutes, finding 13 variable sources. We compare these sources to the VLASS and RACS survey data, and examine possible explanations for the variability. Additionally, for one of these sources we examine archival Chandra ACIS data. We find that 12 of these sources are consistent with variability due to interstellar scintillation. The remaining source could possibly have some intrinsic variability. We also split the MeerKAT L band into upper and lower halves, and search for transients in images with an integration time of 8 s. We find a source with a duration of 8–16 s that is highly polarized at the lowest frequencies. This source is spatially coincident with a star detected by the Transiting Exoplanet Survey Satellite. We conclude that this source may be consistent with a stellar flare. Finally, we calculate accurate upper and lower limits on the transient rate using transient simulations.

A programme to search for radio emission from dwarf-novae-type cataclysmic variables was conducted with the South African MeerKAT radio telescope. The dwarf novae RU Pegasi, V426 Ophiuchi, and IP Pegasi were detected during outburst at L band (1284 MHz central frequency). Previously, only one cataclysmic variable was radio-detected at a frequency this low. We now bring the number to four. With these three newly found radio-emitters, the population of dwarf novae confirmed to be radio-emitting at any frequency reaches 10 systems. We found that the radio luminosity is correlated with the optical luminosity. For V426 Ophiuchi and RU Pegasi we found a radio decline contemporary with the outburst’s optical decline. The peak radio luminosity of dwarf novae in outburst is very similar to that of novalike Cataclysmic Variables and no correlation with orbital period is seen.

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.