Search

Stellar-mass and supermassive black holes abound in the Universe, whereas intermediate-mass black holes (IMBHs) of ~10^2-10^5 solar masses in between are largely missing observationally, with few cases found only. Here we report the real-time discovery of a long-duration X-ray transient, EP240222a, accompanied by an optical flare with prominent H and He emission lines revealed by prompt follow-up observations. Its observed properties evidence an IMBH located unambiguously in the halo of a nearby galaxy and flaring by tidally disrupting a star -- the only confirmed off-nucleus IMBH-tidal disruption event so far. This work demonstrates the potential of sensitive time-domain X-ray surveys, complemented by timely multi-wavelength follow-ups, in probing IMBHs, their environments, demographics, origins and connections to stellar-mass and supermassive black holes., Comment: 64 pages, 15 figures, submitted

The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both the UV/Optical Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to a trigger under different strategies using model skymaps, convolving these with the 2MPZ catalogue to produce an ordered list of observing fields, deriving the time taken for Swift to reach the correct field and simulating the instrumental responses to modelled kilonovae and short gamma-ray burst afterglows. We find that UVOT using the $u$ filter with an exposure time of order 120 s is optimal for most follow-up observations and that we are likely to detect counterparts in $\sim6$% of all binary neutron star triggers detectable by LVK in O4. We find that the gravitational wave 90% error area and measured distance to the trigger allow us to select optimal triggers to follow-up. Focussing on sources less than 300 Mpc away or 500 Mpc if the error area is less than a few hundred square degrees, distances greater than previously assumed, offer the best opportunity for discovery by Swift with $\sim5 - 30$% of triggers having detection probabilities $\geq 0.5$. At even greater distances, we can further optimise our follow-up by adopting a longer 250 s or 500 s exposure time., Comment: 16 pages, 13 figures. Final version accepted by MNRAS

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures

The Einstein Probe (EP) achieved its first detection and localization of a bright X-ray flare, EP240219a, on 2024 February 19, during its commissioning phase. Subsequent targeted searches triggered by the EP240219a alert identified a faint, untriggered gamma-ray burst (GRB) in the archived data of Fermi Gamma-ray Burst Monitor (GBM), Swift Burst Alert Telescope (BAT), and Insight-HXMT/HE. The EP Wide-field X-ray Telescope (WXT) light curve reveals a long duration of approximately 160 s with a slow decay, whereas the Fermi/GBM light curve shows a total duration of approximately 70 s. The peak in the Fermi/GBM light curve occurs slightly later with respect to the peak seen in the EP/WXT light curve. Our spectral analysis shows that a single cutoff power-law (PL) model effectively describes the joint EP/WXT--Fermi/GBM spectra in general, indicating coherent broad emission typical of GRBs. The model yielded a photon index of $\sim -1.70 \pm 0.05$ and a peak energy of $\sim 257 \pm 134$ keV. After detection of GRB 240219A, long-term observations identified several candidates in optical and radio wavelengths, none of which was confirmed as the afterglow counterpart during subsequent optical and near-infrared follow-ups. The analysis of GRB 240219A classifies it as an X-ray rich GRB (XRR) with a high peak energy, presenting both challenges and opportunities for studying the physical origins of X-ray flashes, XRRs, and classical GRBs. Furthermore, linking the cutoff PL component to nonthermal synchrotron radiation suggests that the burst is driven by a Poynting flux-dominated outflow., Comment: 15 pages, 8 figures, 3 tables

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., Comment: 20 pages, 15 figures. Accepted in MNRAS

Gamma-ray bursts (GRBs) are traditionally classified as either short GRBs with durations $\lesssim 2$ s that are powered by compact object mergers, or long GRBs with durations $\gtrsim 2$ s that powered by the deaths of massive stars. Recent results, however, have challenged this dichotomy and suggest that there exists a population of merger-driven long bursts. One such example, GRB 191019A, has a $t_{90} \approx 64$ s but many of its other properties -- including its host galaxy, afterglow luminosity and lack of associated supernova -- are more consistent with a short GRB. Here we propose an alternative interpretation: that GRB 191019A (which is located in the nucleus of its host) is an atypical jetted tidal disruption event (TDE). In particular, we suggest the short timescale and rapid decline, not expected for standard TDEs, are the result of an "ultra-deep" encounter, in which the star came well within the tidal radius of the black hole and promptly self-intersected, circularised, accreted, and launched a relativistic outflow. This model reproduces the timescale and luminosity through a prompt super-Eddington accretion phase and accounts for the lack of late optical emission. This would make GRB 191019A only the fifth jetted TDE and the first discovered ultra-deep TDE. The ultra-deep TDE model can be distinguished from merger-driven long GRBs via the soft X-ray flash that results from prompt self-intersection of the debris stream; the detection of this flash will be possible with wide-field and soft-X-ray satellites such as $\textit{Einstein Probe}$ or $\textit{SVOM}$., Comment: 10 pages, 2 figures and 1 table. Accepted to ApJL

We present the first release of the Gravitational Wave AfterglowPy Analysis (GWAPA) webtool. GWAPA is designed to provide the community with an interactive tool for rapid analysis of gravitational wave afterglow counterparts and can be extended to the general case of gamma-ray burst afterglows seen at different angles. It is based on the afterglowpy package and allows users to upload observational data and vary afterglow parameters to infer the properties of the explosion. Multiple jet structures, including top hat, Gaussian and power laws, in addition to a spherical outflow model are implemented. A Python script for MCMC fitting is also available to download, with initial guesses taken from GWAPA., Comment: Published in RNAAS. GWAPA will be available at https://gwapa.web.roma2.infn.it/ once essential server repairs are complete

In recent years, searches of archival X-ray data have revealed galaxies exhibiting nuclear quasi-periodic eruptions with periods of several hours. These are reminiscent of the tidal disruption of a star by a supermassive black hole, and the repeated, partial stripping of a white dwarf in an eccentric orbit around a ~10^5 solar mass black hole provides an attractive model. A separate class of periodic nuclear transients, with significantly longer timescales, have recently been discovered optically, and may arise from the partial stripping of a main-sequence star by a ~10^7 solar mass black hole. No clear connection between these classes has been made. We present the discovery of an X-ray nuclear transient which shows quasi-periodic outbursts with a period of weeks. We discuss possible origins for the emission, and propose that this system bridges the two existing classes outlined above. This discovery was made possible by the rapid identification, dissemination and follow up of an X-ray transient found by the new live \swift-XRT transient detector, demonstrating the importance of low-latency, sensitive searches for X-ray transients., Comment: To be published in Nature Astronomy at 1600 BST on September 7th. This version for arXiv includes the main article, Methods and Supplementary Information combined into a single file

The composition of relativistic gamma-ray burst (GRB) jets and their emission mechanisms are still debated, and they could be matter or magnetically dominated. One way to distinguish these mechanisms arises because a Poynting flux dominated jet may produce low-frequency radio emission during the energetic prompt phase, through magnetic reconnection at the shock front. We present a search for radio emission coincident with three GRB X-ray flares with the LOw Frequency ARray (LOFAR), in a rapid response mode follow-up of long GRB 210112A (at z~2) with a 2 hour duration, where our observations began 511 seconds after the initial swift-BAT trigger. Using timesliced imaging at 120-168 MHz, we obtain upper limits at 3 sigma confidence of 42 mJy averaging over 320 second snapshot images, and 87 mJy averaging over 60 second snapshot images. LOFAR's fast response time means that all three potential radio counterparts to X-ray flares are observable after accounting for dispersion at the estimated source redshift. Furthermore, the radio pulse in the magnetic wind model was expected to be detectable at our observing frequency and flux density limits which allows us to disfavour a region of parameter space for this GRB. However, we note that stricter constraints on redshift and the fraction of energy in the magnetic field are required to further test jet characteristics across the GRB population., Comment: 13 pages, 7 figures, accepted for publication in MNRAS

The LOFAR Two-Metre Sky Survey (LoTSS) is an invaluable new tool for investigating the properties of sources at low frequencies and has helped to open up the study of galaxy populations in this regime. In this work, we perform a search for host galaxies of gamma-ray bursts (GRBs). We use the relative density of sources in Data Release 2 of LoTSS to define the probability of a chance alignment, $P_{\rm chance}$, and find 18 sources corresponding to 17 GRBs which meet a $P_{\rm chance}$<1% criterion. We examine the nature and properties of these radio sources using both LOFAR data and broadband information, including their radio spectral index, star formation rate estimates and any contributions from active galactic nucleus emission. Assuming the radio emission is dominated by star formation, we find that our sources show high star formation rates ($10^1$-$10^3$ $M_{\odot}$ yr$^{-1}$) compared with both a field galaxy sample and a sample of core-collapse supernova hosts, and the majority of putative hosts are consistent with ultraluminous infrared galaxy (ULIRG) classifications. As a result of our analyses, we define a final sample of eight likely GRB host candidates in the LoTSS DR2 survey., Comment: 15 pages, 9 figures and 6 tables. Accepted by MNRAS

We report the discovery of the unusually bright long-duration gamma-ray burst (GRB), GRB 221009A, as observed by the Neil Gehrels Swift Observatory (Swift), Monitor of All-sky X-ray Image (MAXI), and Neutron Star Interior Composition Explorer Mission (NICER). This energetic GRB was located relatively nearby (z = 0.151), allowing for sustained observations of the afterglow. The large X-ray luminosity and low Galactic latitude (b = 4.3 degrees) make GRB 221009A a powerful probe of dust in the Milky Way. Using echo tomography we map the line-of-sight dust distribution and find evidence for significant column densities at large distances (~> 10kpc). We present analysis of the light curves and spectra at X-ray and UV/optical wavelengths, and find that the X-ray afterglow of GRB 221009A is more than an order of magnitude brighter at T0 + 4.5 ks than any previous GRB observed by Swift. In its rest frame GRB 221009A is at the high end of the afterglow luminosity distribution, but not uniquely so. In a simulation of randomly generated bursts, only 1 in 10^4 long GRBs were as energetic as GRB 221009A; such a large E_gamma,iso implies a narrow jet structure, but the afterglow light curve is inconsistent with simple top-hat jet models. Using the sample of Swift GRBs with redshifts, we estimate that GRBs as energetic and nearby as GRB 221009A occur at a rate of ~<1 per 1000 yr - making this a truly remarkable opportunity unlikely to be repeated in our lifetime., Comment: 30 pages, 13 figures, submitted to ApJL

We present simulated optical light curves of super-Eddington tidal disruption events (TDEs) using the zero-Bernoulli accretion (ZEBRA) flow model, which proposes that during the super-Eddington phase, the disc is quasi-spherical, radiation-pressure dominated, and accompanied by the production of strong jets. We construct light curves for both on- and off-axis (with respect to the jet) observers to account for the anisotropic nature of the jetted emission. We find that at optical wavelengths, emission from the accretion flow is orders of magnitude brighter than that produced by the jet, even with boosting from synchrotron self-Compton. Comparing to the observed jetted TDE Swift J2058.4+0516, we find that the ZEBRA model accurately captures the timescale for which accretion remains super-Eddington and reproduces the luminosity of the transient. However, we find the shape of the light curves deviate at early times and the radius and temperature of our modelled ZEBRA are $\sim2.7 - 4.1$ times smaller and $\sim1.4 - 2.3$ times larger, respectively, than observed. We suggest that this indicates the ZEBRA inflates more, and more rapidly, than currently predicted by the model, and we discuss possible extensions to the model to account for this. Such refinements, coupled with valuable new data from upcoming large scale surveys, could help to resolve the nature of super-Eddington TDEs and how they are powered., Comment: Accepted to MNRAS. 9 pages and 5 figures

Empirical analysis of secondary microdata, bespoke social mobility surveys and administrative school attendance data reveal the extent of learning losses that have evolved in the four nations of the UK over the year of the pandemic. They are sizable in all four nations, with a common feature of disadvantaged pupils suffering particularly large losses during two periods of school closures, the durations of which varied across the home nations. Between March 2020 and April 2021, the following maximum number of classroom days were lost over one calendar year: 110 days (England); 119 days (Northern Ireland); 119 days (Scotland); 124 days (Wales). These compare to a full year during normal times of 190 classroom days. Considering learning undertaken at home and in the classroom, pupils in England lost 61 days of schooling. Larger average losses occurred in Scotland (64 days) and Wales (66 days), while pupils in Northern Ireland also lost 61 days. The differences across the nations arise both because of variations in learning loss at home, and due to education policy differences (both historical differences in term times and from specific policy choices during the pandemic). Rising school absences in June/July 2021 once prompt discussion about credible policies to address learning losses. Survey responses reveal that 53 percent of 10,000 adults support extending the school day, while seven in ten respondents support allowing greater flexibility for pupils to repeat a whole school year.

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.

We present the Living Swift-XRT Point Source catalogue (LSXPS) and real-time transient detector. This system allows us for the first time to carry out low-latency searches for new transient X-ray events fainter than those available to the current generation of wide-field imagers, and report their detection in near real-time. Previously, such events could only be found in delayed searches, e.g. of archival data; our low-latency analysis now enables rapid and ongoing follow up of these events, enabling the probing of timescales previously inaccessible. The LSXPS is, uniquely among X-ray catalogues, updated in near real-time, making this the first up-to-date record of the point sources detected by a sensitive X-ray telescope: the Swift-X-ray Telescope (XRT). The associated upper limit calculator likewise makes use of all available data allowing contemporary upper limits to be rapidly produced on-demand. These facilities, which enable the low-latency transient system are also fully available to the community, providing a powerful resource for time-domain and multi-messenger astrophysics., Comment: Replaced with post-refereeing accepted version of the paper. To appear in MNRAS. 12 pages, plus 18 of appendices. For the sake of rainforests, printing the appendices is not advised. For the sake of sanity, nor is reading them

The Second Swift-XRT Point Source catalogue offers a combination of sky coverage and sensitivity and presents an invaluable opportunity for transient discovery. We search the catalogue at the positions of inactive and active galaxies, and identify transient candidates by comparison with XMM-Newton and ROSAT. We recover 167 previously known transients and find 19 sources consistent with being new sources, estimating a completeness of $\sim65\%$. These 19 new sources are split approximately equally between inactive and active hosts and their peak X-ray luminosities span $\sim 10^{42} - 10^{47}$ erg s$^{-1}$. We find eight are best fit with non-thermal spectral models and one with a blackbody. We also discuss our methodology and its application to the forthcoming Living Swift-XRT Point Source catalogue for the potential near real time serendipitous discovery of $\sim$ few new X-ray transients per year., Comment: 12 pages, 7 figures, 8 tables plus appendix with 2 additional figures. Accepted by MNRAS

The purpose of this brief paper is to present initial findings from the recently collected London School of Economics and Political Science-Centre for Economic Performance (LSE-CEP) Social Mobility survey, which was undertaken as part of the UK Research and Innovation (UKRI) project 'Generation COVID and Social Mobility: Evidence and Policy'. These are the first results from a project that is producing a detailed assessment of COVID-19's impact on education and economic inequalities and offering an assessment for the longer term consequences for social mobility in the UK. Alongside the survey findings, the authors have also analysed economic and education outcomes of individuals in April 2020 in the Understanding Society (USoc) national household panel data. Before the pandemic, younger generations were already facing declining absolute social mobility and real wage decline (Elliot Major and Machin, 2018, 2020a). Education inequalities were also widening. Here new findings are presented on inequalities in the workplace and the classroom that have emerged following the pandemic.

Evidence from unexpected temporary school closures and reduced instruction time suggests school closures will reduce educational achievement, both in the short and long term. Children from disadvantaged backgrounds are likely to be affected more than others by school closures, with fewer family resources and less access to online learning resources to offset lost instruction time. In England, the total cost of the resources lost in each week of state school closure is more than £1 billion. Educational deficits from time lost to school shutdowns can be made up with additional hours of teaching when schools reopen, though schools might need to put back more hours than were lost and it may not be feasible to do this within the traditional school year. Compensating lost instruction time through additional resources, without additional hours, is likely to be even more expensive.

Femoroacetabular impingement (FAI) cam morphology is routinely assessed using two-dimensional alpha angles which do not provide specific data on cam size characteristics. The purpose of this study is to implement a novel, automated three-dimensional (3D) pipeline, CamMorph, for segmentation and measurement of cam volume, surface area and height from magnetic resonance (MR) images in patients with FAI. The CamMorph pipeline involves two processes: i) proximal femur segmentation using an approach integrating 3D U-net with focused shape modelling (FSM); ii) use of patient-specific anatomical information from 3D FSM to simulate healthy femoral bone models and pathological region constraints to identify cam bone mass. Agreement between manual and automated segmentation of the proximal femur was evaluated with the Dice similarity index (DSI) and surface distance measures. Independent t-tests or Mann-Whitney U rank tests were used to compare the femoral head volume, cam volume, surface area and height data between female and male patients with FAI. There was a mean DSI value of 0.964 between manual and automated segmentation of proximal femur volume. Compared to female FAI patients, male patients had a significantly larger mean femoral head volume (66.12cm3 v 46.02cm3, p<0.001). Compared to female FAI patients, male patients had a significantly larger mean cam volume (1136.87mm3 v 337.86mm3, p<0.001), surface area (657.36mm2 v 306.93mm2 , p<0.001), maximum-height (3.89mm v 2.23mm, p<0.001) and average-height (1.94mm v 1.00mm, p<0.001). Automated analyses of 3D MR images from patients with FAI using the CamMorph pipeline showed that, in comparison with female patients, male patients had significantly greater cam volume, surface area and height., Comment: 24 pages (including appendices), 8 figures, 2 tables, 4 Appendices

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 multi-messenger 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., Comment: 19 pages, 16 Figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

Compact binaries such as double neutron stars or a neutron star paired with a black-hole, are strong sources of gravitational waves during coalescence and also the likely progenitors of various electromagnetic phenomena, notably short-duration gamma-ray bursts (SGRBs), and kilonovae. In this work, we generate populations of synthetic binaries and place them in galaxies from the large-scale hydrodynamical galaxy evolution simulation EAGLE. With our zELDA code, binaries are seeded in proportion to star formation rate, and we follow their evolution to merger using both the BPASS and COSMIC binary stellar evolution codes. We track their dynamical evolution within their host galaxy potential, to estimate the galactocentric distance at the time of the merger. Finally, we apply observational selection criteria to allow comparison of this model population with the legacy sample of SGRBs. We find a reasonable agreement with the redshift distribution (peaking at $0.526$)., Comment: Accepted for publication in MNRAS, 21 Pages (6 Tables, 14 Figures), 14 Pages Appendix (4 Tables, 16 Figures)

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.

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., Comment: 16 pages, 12 figures, to be published in Monthly Notices of the Royal Astronomical Society

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$\sigma$) 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 $5\sigma$ 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., Comment: Accepted for publication in PASA

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., Comment: 17 pages, 12 figures, resubmitted to MNRAS following reviewer comments

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., Comment: 17 pages, 13 figures. Accepted for publication in PASA

We consider a fully practical finite element approximation of a diffuse interface model for tumour growth that takes the form of a degenerate parabolic system. In addition to showing stability bounds for the approximation, we prove convergence, and hence existence of a solution to this system in two space dimensions. Several numerical experiments demonstrate the practicality and robustness of the proposed method.

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe 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 presentan approach for creating a training set by using all detections in the science images to be thesample 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-by-21pixel stamps centered 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% prediction accuracy on the real detections at a false alarm rate of 1%., Comment: 9 pages, 8 figures