We report the results of optical follow-up observations of 29 gravitational-wave (GW) triggers during the first half of the LIGO–Virgo Collaboration (LVC) O3 run with the Gravitational-wave Optical Transient Observer (GOTO) in its prototype 4-telescope configuration (GOTO-4). While no viable electromagnetic (EM) counterpart candidate was identified, we estimate our 3D (volumetric) coverage using test light curves of on- and off-axis gamma-ray bursts and kilonovae. In cases where the source region was observable immediately, GOTO-4 was able to respond to a GW alert in less than a minute. The average time of first observation was 8.79 h after receiving an alert (9.90 h after trigger). A mean of 732.3 square degrees were tiled per event, representing on average 45.3 per cent of the LVC probability map, or 70.3 per cent of the observable probability. This coverage will further improve as the facility scales up alongside the localization performance of the evolving GW detector network. Even in its 4-telescope prototype configuration, GOTO is capable of detecting AT2017gfo-like kilonovae beyond 200 Mpc in favourable observing conditions. We cannot currently place meaningful EM limits on the population of distant ($\hat{D}_L = 1.3$ Gpc) binary black hole mergers because our test models are too faint to recover at this distance. However, as GOTO is upgraded towards its full 32-telescope, 2 node (La Palma & Australia) configuration, it is expected to be sufficiently sensitive to cover the predicted O4 binary neutron star merger volume, and will be able to respond to both northern and southern triggers.

The Gravitational-wave Optical Transient Observer (GOTO) is an array of wide-field optical telescopes, designed to exploit new discoveries from the next generation of gravitational wave detectors (LIGO, Virgo, and KAGRA), study rapidly evolving transients, and exploit multimessenger opportunities arising from neutrino and very high energy gamma-ray triggers. In addition to a rapid response mode, the array will also perform a sensitive, all-sky transient survey with few day cadence. The facility features a novel, modular design with multiple 40-cm wide-field reflectors on a single mount. In 2017 June, the GOTO collaboration deployed the initial project prototype, with 4 telescope units, at the Roque de los Muchachos Observatory (ORM), La Palma, Canary Islands. Here, we describe the deployment, commissioning, and performance of the prototype hardware, and discuss the impact of these findings on the final GOTO design. We also offer an initial assessment of the science prospects for the full GOTO facility that employs 32 telescope units across two sites.

We present high-cadence optical, ultraviolet (UV), and near-infrared data of the nearby (D ≈ 23 Mpc) Type II supernova (SN) 2021yja. Many Type II SNe show signs of interaction with circumstellar material (CSM) during the first few days after explosion, implying that their red supergiant (RSG) progenitors experience episodic or eruptive mass loss. However, because it is difficult to discover SNe early, the diversity of CSM configurations in RSGs has not been fully mapped. SN 2021yja, first detected within ≈ 5.4 hours of explosion, shows some signatures of CSM interaction (high UV luminosity and radio and x-ray emission) but without the narrow emission lines or early light-curve peak that can accompany CSM. Here we analyze the densely sampled early light curve and spectral series of this nearby SN to infer the properties of its progenitor and CSM. We find that the most likely progenitor was an RSG with an extended envelope, encompassed by low-density CSM. We also present archival Hubble Space Telescope imaging of the host galaxy of SN 2021yja, which allows us to place a stringent upper limit of ≲ 9 M _☉ on the progenitor mass. However, this is in tension with some aspects of the SN evolution, which point to a more massive progenitor. Our analysis highlights the need to consider progenitor structure when making inferences about CSM properties, and that a comprehensive view of CSM tracers should be made to give a fuller view of the last years of RSG evolution.

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in 2015 October, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1%–2.5% dips, named “ Elsie ,” “ Celeste ,” “ Skara Brae ,” and “ Angkor ,” which persist on timescales from several days to weeks. Our main results so far are as follows: (i) there are no apparent changes of the stellar spectrum or polarization during the dips and (ii) the multiband photometry of the dips shows differential reddening favoring non-gray extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale ≪1 μ m, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term “secular” dimming, which may be caused by independent processes, or probe different regimes of a single process.

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritize human vetting efforts and inform future model optimization via active learning. To fully realize the potential of this architecture, we present a fully automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1 per cent) compared against classifiers trained with fully human-labelled data sets, while being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community.