arXiv subjects

E. Stanway

Publications and source records attributed to E. Stanway.

11 recordsLinked to original sources

arXiv2026

Searching for outbursts from Symbiotic Binaries in GOTO and ATLAS data

Symbiotic Binaries contain a white dwarf accreting material from a red giant star through a wind. We present the results of a search for outbursts from Symbiotic Binaries using photometric data obtained using the GOTO all-sky survey taken from 2023 onwards. After identifying ten candidate outbursting systems, we used ATLAS photometry to characterise their photometric behaviour before 2023, leaving five systems which showed photometric behaviour consistent with an outburst. The ATLAS data showed how important the photometric history of an object is in determining whether a photometric feature is a likely outburst event. The outburst from LMC N67 is the first reported Z And-type outburst from a Symbiotic binary in the LMC. OGLE SMC-LPV-4044 and HK Sco show previously unreported outbursts. QW Sge and V4141 Sgr show outbursts starting in 2024, which have already been reported and are ongoing. By better identifying and characterising Z And-type outbursts from many systems, it will be possible to better understand the physics of these events, which are still not fully understood.

astro-ph.SR↗

arXiv2026

The Gravitational-wave Optical Transient Observer (GOTO) data pipeline and workflow for transient discovery

Wide-field and high-cadence sky surveys are the first step in the chain of discovery and characterisation of astrophysical transients such as supernovae, kilonovae, and tidal disruption events, each linked to the varied demise of stellar systems. The Gravitational-wave Optical Transient Observer (GOTO) is a telescope array of thirty-two 40 cm unit telescopes split over two almost antipodal sites. It performs a regular time-domain sky-survey in the optical to ~20 mag in addition to immediate scheduling of follow-up observations at the locations of external multi-wavelength and -messenger triggers. To facilitate the timely recovery of optical counterparts to these triggers, as well as the presence of serendipitous discoveries of astrophysical transients in the regular sky-survey, a low-latency data pipeline and workflow was developed. The implementation of this workflow is described herein and the quality of GOTO data delivered by it assessed, alongside its performance for prompt transient recovery. Utilising difference image analysis to identify candidate discoveries, the process is typically complete ~7 minutes after shutter close on the telescope. We further describe later processing of these candidates -- both automated and human-in-the-loop -- including reporting to the wider community and the triggering of more detailed observations, with a focus on immediate, intra-night characterisation. The workflow is meeting the needs of GOTO to promptly discover, report and characterise infant transients. Nevertheless, areas for further development and improvements are also highlighted.

astro-ph.IM↗

arXiv2026

GOTO identification and broadband modelling of the counterpart to the SVOM GRB 250818B

Rapid localisation and follow-up of gamma-ray bursts (GRBs) increasingly rely on low-latency triggers from new missions coupled to wide-field robotic optical facilities. We present the discovery and multi-wavelength follow-up of GRB 250818B, detected by the Space Variable Objects Monitor (SVOM) and localised optically by the Gravitational-wave Optical Transient Observer (GOTO). We compile and homogenise X-ray, optical/NIR, and radio data to build broadband light curves and spectral energy distributions. The afterglow is unusually luminous for a nominal short GRB, lying on the bright end of the short-GRB population in X-rays and optical and among the most luminous high-redshift short-GRB afterglows in the radio. MeerKAT detects the source at 3.1 GHz, while ALMA provides deep higher-frequency limits. Keck/LRIS spectroscopy shows continuum and metal absorption (Fe II, Mg II, Mg I), giving $z=1.216$. Synchrotron forward-shock modelling favours a constant-density medium and strongly prefers refreshed (energy-injection) emission, well described by a two-component jet with $E_{K,iso} \sim 4\times10^{52}$ erg, $n_0 \sim 3.6$ cm$^{-3}$, $\theta_j \simeq 0.10$ rad ($\sim 5.7$ deg), and $p \simeq 1.64$. The host association is ambiguous: the nearest LS DR10 galaxy candidate ($r_{AB} \sim 24.7$) is offset by $\sim 4$ arcsec ($\sim 34$ kpc) with chance-alignment probability $P_{cc} \sim 0.2$, and current imaging does not exclude a fainter, near-coincident host. SED fitting of the candidate host suggests a low-mass galaxy. GRB 250818B highlights the power of rapid wide-field counterpart identification in the SVOM era, while host-association uncertainty can still limit offset-based interpretation.

astro-ph.HE↗

arXiv2024

SN 2023tsz: A helium-interaction driven supernova in a very low-mass galaxy

SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to originate from the collapse of massive Wolf-Rayet (WR) stars, though their progenitor systems still remain poorly understood. In terms of energetics and spectrophotometric evolution, SN 2023tsz is largely a typical example of the class, although line profile asymmetries in the nebular phase are seen, which may indicate the presence of dust formation or unshocked circumstellar material. Intriguingly, SN 2023tsz is located in an extraordinarily low-mass host galaxy that is in the 2nd percentile for SESN host masses and star formation rates (SFR). The host has a radius of 1.0 kpc, a $g$-band absolute magnitude of $-12.73$, and an estimated metallicity of $\log(Z_{*}/Z_{\odot}$) = $-1.56$. The SFR and metallicity of the host galaxy raise questions about the progenitor of SN 2023tsz. The low SFR suggests that a star with sufficient mass to evolve into a WR would be uncommon in this galaxy. Further, the very low-metallicity is a challenge for single stellar evolution to enable H and He stripping of the progenitor and produce a SN Ibn explosion. The host galaxy of SN 2023tsz adds another piece to the ongoing puzzle of SNe Ibn progenitors, and demonstrates that they can occur in hosts too faint to be observed in contemporary sky surveys at a more typical SN Ibn redshift.

astro-ph.HE↗

arXiv2024

$\textit{Kilonova Seekers}$: the GOTO project for real-time citizen science in time-domain astrophysics

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 $\textit{Kilonova Seekers}$ citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. $\textit{Kilonova Seekers}$ 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.

astro-ph.IM↗

arXiv2022

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.

cs.CV↗

arXiv2021

Searching For Fermi GRB Optical Counterparts With The Prototype Gravitational-Wave Optical Transient Observer (GOTO)

The typical detection rate of $\sim1$ gamma-ray burst (GRB) per day by the \emph{Fermi} Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the \emph{Fermi} localization typically prevents rapid identification of multi-wavelength counterparts. We report the follow-up of 93 \emph{Fermi} GRBs with the Gravitational-wave Optical Transient Observer (GOTO) prototype on La Palma. We selected 53 events (based on favourable observing conditions) for detailed analysis, and to demonstrate our strategy of searching for optical counterparts. We apply a filtering process consisting of both automated and manual steps to 60\,085 candidates initially, rejecting all but 29, arising from 15 events. With $\approx3$ GRB afterglows expected to be detectable with GOTO from our sample, most of the candidates are unlikely to be related to the GRBs. Since we did not have multiple observations for those candidates, we cannot confidently confirm the association between the transients and the GRBs. Our results show that GOTO can effectively search for GRB optical counterparts thanks to its large field of view of $\approx40$ square degrees and its depth of $\approx20$ mag. We also detail several methods to improve our overall performance for future follow-up programs of \emph{Fermi} GRBs.

astro-ph.HE↗

arXiv2021

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.

astro-ph.IM↗

arXiv2021

Processing GOTO data with the Rubin Observatory LSST Science Pipelines II: Forced Photometry and light curves

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 Rubin Observatory 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 PanSTARRS photometry, and find that the two agree to within 10~mmag (1$σ$) for bright (i.e., $\sim14^{\rm th}$~mag) sources to 200~mmag for faint (i.e., $\sim18^{\rm th}$~mag) sources. Additionally, we compare our results to those obtained by GOTO's own in-house pipeline, {\sc gotophoto}, and obtain similar results. Based on repeatability measurements, we measure a $5σ$ L-band survey depth of between 19 and 20 magnitudes, depending on observing conditions. We assess, using repeated observations of non-varying standard SDSS stars, the accuracy of our uncertainties, which we find are typically overestimated by roughly a factor of two for bright sources (i.e., $<15^{\rm th}$~mag), but slightly underestimated (by roughly a factor of 1.25) for fainter sources ($>17^{\rm th}$~mag). 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 Rubin Observatory LSST Science Pipelines still undergoing active development, our results show that they are already delivering robust forced photometry measurements from GOTO data.

astro-ph.IM↗

arXiv2021

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.

astro-ph.IM↗

arXiv2020

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 $\sim50$ mmag at $m_L\sim16$ and $\sim200$ mmag at $m_L\sim18$. 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.

astro-ph.IM↗