Publications

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

We present results of our analysis of up to 15 years of photometric data from eight AM CVn systems with orbital periods between 22.5 and 26.8 min. Our data has 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 ultra-compact 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 super outbursts and standstills, SU UMa systems with regular, shorter super outbursts, and nova-like systems which 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 super outbursts in an AM CVn system. Our results will inform theoretical modelling of the outbursts of hydrogen deficient systems.

Status: Accepted to MNRAS
Authors: C. Duffy, G. Ramsay, D. Steeghs, V. Dhillon, Mark R. Kennedy, D. Mata Sánchez, K. Ackley, M. Dyer, J. Lyman, K. Ulaczyk, D. K. Galloway, P. O’Brien, K. Noysena, L. Nuttall, D. Pollacco

Preprint: arXiv:2110.04428

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, 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 June 2017 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.

Status: Accepted to MNRAS
Authors: Steeghs, D. et al.

Preprint: arXiv:2110.05539

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification.

Published: May 2021
Authors: Burhanudin, U. F., et al.

Preprint: arXiv:2105.11169

Transient-optimised 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 prioritise human vetting efforts and inform future model optimisation via active learning. To fully realise 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%) compared against classifiers trained with fully human-labelled datasets, whilst 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.

Published: March 2021
Authors: Killestein, T. L., et al.
DOI: 10.1093/mnras/stab633

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I : Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this “off-the-shelf” pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at m_L∼16 and ∼200 mmag at m_L∼18. These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic “obs package” that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities.

Published: October 2020
Authors: Mullaney J. R., et al.
Preprint: arXiv:2010.15142

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.

Published: October 2020
Authors: Mong, Y.-L. et al.
DOI: 10.1093/mnras/staa3096

Searching for Electromagnetic Counterparts to Gravitational-wave Merger Events with the Prototype Gravitational-wave Optical Transient Observer (GOTO-4)

We report the results of optical follow-up observations of 29 gravitational-wave 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 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 hours after receiving an alert (9.90 hours 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 localisation performance of the evolving gravitational-wave 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 electromagnetic limits on the population of distant 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.

Published: July 2020-07-10
Authors: Gompertz, B. P. et al.
DOI: 10.1093/mnras/staa1845 

A Telescope Control And Scheduling System For The Gravitational-Wave Optical Transient Observer (GOTO)

This paper describes the GOTO Telescope Control System (G-TeCS), a custom robotic control system written in Python which autonomously manages the telescope hardware and nightly operations.

The Gravitational-wave Optical Transient Observer (GOTO) is a wide-field telescope project aimed at detecting optical counterparts to gravitational wave sources. The prototype instrument was inaugurated in July 2017 on La Palma in the Canary Islands. We describe the GOTO Telescope Control System (G-TeCS), a custom robotic control system written in Python which autonomously manages the telescope hardware and nightly operations. The system comprises of multiple independent control daemons, which are supervised by a master control program known as the “pilot”. Observations are decided by a “just-in-time” scheduler, which instructs the pilot what to observe in real time and provides quick follow-up of transient events.

Published: 2018-07-10
Authors: Dyer, Martin J.; Dhillon, Vik S.; Littlefair, Stuart; Steeghs, Danny; Ulaczyk, Krzysztof; Chote, Paul; Galloway, Duncan; Rol, Evert
DOI: 10.1117/12.2311865