V1460 Her: a fast spinning white dwarf accreting from an evolved donor star

We present time-resolved optical and ultraviolet (UV) spectroscopy and photometry of V1460 Her, an eclipsing cataclysmic variable with a 4.99-h orbital period and an overluminous K5-type donor star. The optical spectra show emission lines from an accretion disc along with absorption lines from the donor. We use these to measure radial velocities, which, together with constraints upon the orbital inclination from photometry, imply masses of M1 = 0.869 ± 0.006 M☉ and M2 = 0.295 ± 0.004 M☉ for the white dwarf and the donor. The radius of the donor, R2 = 0.43 ± 0.002 R☉, is ≈50 per cent larger than expected given its mass, while its spectral type is much earlier than the M3.5 type that would be expected from a main-sequence star with a similar mass. Hubble Space Telescope (HST) spectra show strong N V 1240-Å emission but no C IV 1550-Å emission, evidence for CNO-processed material. The donor is therefore a bloated, overluminous remnant of a thermal time-scale stage of high mass transfer and has yet to reestablish thermal equilibrium. Remarkably, the HST UV data also show a strong 30 per cent peak-to-peak, 38.9 s pulsation that we explain as being due to the spin of the white dwarf, potentially putting V1460 Her in a similar category to the propeller system AE Aqr in terms of its spin frequency and evolutionary path. AE Aqr also features a post-thermal time-scale mass donor, and V1460 Her may therefore be its weak magnetic field analogue since the accretion disc is still present, with the white dwarf spin-up a result of a recent high accretion rate

V1460 Her: a fast spinning white dwarf accreting from an evolved donor star

We present time-resolved optical and ultraviolet (UV) spectroscopy and photometry of V1460 Her, an eclipsing cataclysmic variable with a 4.99-h orbital period and an overluminous K5-type donor star. The optical spectra show emission lines from an accretion disc along with absorption lines from the donor. We use these to measure radial velocities, which, together with constraints upon the orbital inclination from photometry, imply masses of M1 = 0.869 ± 0.006 M☉ and M2 = 0.295 ± 0.004 M☉ for the white dwarf and the donor. The radius of the donor, R2 = 0.43 ± 0.002 R☉, is ≈50 per cent larger than expected given its mass, while its spectral type is much earlier than the M3.5 type that would be expected from a main-sequence star with a similar mass. Hubble Space Telescope (HST) spectra show strong N V 1240-Å emission but no C IV 1550-Å emission, evidence for CNO-processed material. The donor is therefore a bloated, overluminous remnant of a thermal time-scale stage of high mass transfer and has yet to reestablish thermal equilibrium. Remarkably, the HST UV data also show a strong 30 per cent peak-to-peak, 38.9 s pulsation that we explain as being due to the spin of the white dwarf, potentially putting V1460 Her in a similar category to the propeller system AE Aqr in terms of its spin frequency and evolutionary path. AE Aqr also features a post-thermal time-scale mass donor, and V1460 Her may therefore be its weak magnetic field analogue since the accretion disc is still present, with the white dwarf spin-up a result of a recent high accretion rate

Multi-wavelength observations of the EUV variable metal-rich white dwarf GD 394

We present new Hubble Space Telescope (HST) ultraviolet and ground-based optical observations of the hot, metal-rich white dwarf GD 394. Extreme-ultraviolet (EUV) observations in 1992-1996 revealed a 1.15d periodicity with a 25 percent amplitude, hypothesised to be due to metals in a surface accretion spot. We obtained phase-resolved HST/Space Telescope Imaging Spectrograph (STIS) high-resolution far-ultraviolet (FUV) spectra of GD 394 that sample the entire period, along with a large body of supplementary data. We find no evidence for an accretion spot, with the flux, accretion rate and radial velocity of GD 394 constant over the observed timescales at ultraviolet and optical wavelengths. We speculate that the spot may have no longer been present when our observations were obtained, or that the EUV variability is being caused by an otherwise undetected evaporating planet. The atmospheric parameters obtained from separate fits to optical and ultraviolet spectra are inconsistent, as is found for multiple hot white dwarfs. We also detect non-photospheric, high-excitation absorption lines of multiple volatile elements, which could be evidence for a hot plasma cocoon surrounding the white dwarf

NGTS and HST insights into the long period modulation in GW Librae

Light curves of the accreting white dwarf pulsator GW Librae spanning a 7.5 month period in 2017 were obtained as part of the Next Generation Transit Survey. This data set comprises 787 hours of photometry from 148 clear nights, allowing the behaviour of the long (hours) and short period (20min) modulation signals to be tracked from night to night over a much longer observing baseline than has been previously achieved. The long period modulations intermittently detected in previous observations of GW Lib are found to be a persistent feature, evolving between states with periods ~83min and 2-4h on time-scales of several days. The 20min signal is found to have a broadly stable amplitude and frequency for the duration of the campaign, but the previously noted phase instability is confirmed. Ultraviolet observations obtained with the Cosmic Origin Spectrograph onboard the Hubble Space Telescope constrain the ultraviolet-to-optical flux ratio to ~5 for the 4h modulation, and <=1 for the 20min period, with caveats introduced by non-simultaneous observations. These results add further observational evidence that these enigmatic signals must originate from the white dwarf, highlighting our continued gap in theoretical understanding of the mechanisms that drive them

The Gravitational-wave Optical Transient Observer (GOTO): Prototype performance and prospects for transient science

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

<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

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

Transient-optimized real-bogus classification with Bayesian convolutional neural networks - sifting the GOTO candidate stream

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

The Next Generation Transit Survey (NGTS)

We describe the Next Generation Transit Survey (NGTS), which is a ground-based project searching for transiting exoplanets orbiting bright stars. NGTS builds on the legacy of previous surveys, most notably WASP, and is designed to achieve higher photometric precision and hence find smaller planets than have previously been detected from the ground. It also operates in red light,maximizing sensitivity to late K and earlyMdwarf stars. The survey specifications call for photometric precision of 0.1 per cent in red light over an instantaneous field of view of 100 deg 2 , enabling the detection of Neptune-sized exoplanets around Sun-like stars and super-Earths around M dwarfs. The survey is carried out with a purpose-built facility at Cerro Paranal, Chile, which is the premier site of the European Southern Observatory (ESO). An array of twelve 20 cm f/2.8 telescopes fitted with back-illuminated deep-depletion CCD cameras is used to survey fields intensively at intermediateGalactic latitudes. The instrument is also ideally suited to ground-based photometric follow-up of exoplanet candidates from space telescopes such as TESS, Gaia and PLATO. We present observations that combine precise autoguiding and the superb observing conditions at Paranal to provide routine photometric precision of 0.1 per cent in 1 h for stars with I-band magnitudes brighter than 13. We describe the instrument and data analysis methods as well as the status of the survey, which achieved first light in 2015 and began full-survey operations in 2016. NGTS data will be made publicly available through the ESO archive

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