Evidence that short-period AM CVn systems are diverse in outburst behaviour

We present results of our analysis of up to 15 yr of photometric data from eight AM CVn systems with orbital periods between 22.5 and 26.8 min. Our data have been collected from the GOTO, ZTF, Pan-STARRS, ASAS-SN, and Catalina all-sky surveys and amateur observations collated by the AAVSO. We find evidence that these interacting ultracompact binaries show a similar diversity of long-term optical properties as the hydrogen accreting dwarf novae. We found that AM CVn systems in the previously identified accretion disc instability region are not a homogenous group. Various members of the analysed sample exhibit behaviour reminiscent of Z Cam systems with long superoutbursts (SOs) and standstills, SU UMa systems with regular, shorter SOs, and nova-like systems that appear only in a high state. The addition of TESS full frame images of one of these systems, KL Dra, reveals the first evidence for normal outbursts appearing as a precursor to SOs in an AM CVn system. Our results will inform theoretical modelling of the outbursts of hydrogen deficient systems

Processing GOTO survey data with the Rubin Observatory LSST Science Pipelines II: Forced Photometry and lightcurves

We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines to process data from the Gravitational-wave Optical Transient Observer (GOTO) prototype. In this paper, we describe how we used the LSST Science Pipelines to conduct forced photometry measurements on nightly GOTO data. By comparing the photometry measurements of sources taken on multiple nights, we find that the precision of our photometry is typically better than 20 mmag for sources brighter than 16 mag. We also compare our photometry measurements against colour-corrected Panoramic Survey Telescope and Rapid Response System photometry and find that the two agree to within 10 mmag (1 ) for bright (i.e., ) sources to 200 mmag for faint (i.e., ) sources. Additionally, we compare our results to those obtained by GOTO’s own in-house pipeline, gotophoto, and obtain similar results. Based on repeatability measurements, we measure a L-band survey depth of between 19 and 20 magnitudes, depending on observing conditions. We assess, using repeated observations of non-varying standard Sloan Digital Sky Survey stars, the accuracy of our uncertainties, which we find are typically overestimated by roughly a factor of two for bright sources (i.e., ), but slightly underestimated (by roughly a factor of 1.25) for fainter sources ( ). Finally, we present lightcurves for a selection of variable sources and compare them to those obtained with the Zwicky Transient Factory and GAIA. Despite the LSST Software Pipelines still undergoing active development, our results show that they are already delivering robust forced photometry measurements from GOTO data

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially increasing. Human inspection alone is not an effective way to identify genuine transients from the data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We present an approach for creating a training set by using all detections in the science images to be the sample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21 × 21 pixel stamps centred at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95 per cent prediction accuracy on the real detections at a false alarm rate of 1 per cent⁠

<i>Kilonova Seekers</i><b>: the GOTO project for real-time citizen science in time-domain astrophysics</b>

Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in July 2023 and received over 600,000 classifications from approximately 2,000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a ‘gold-standard’ training set of 17,682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.</p