publications

2026

1. ApJ

   Identifying Transient Hosts in LSST’s Deep Drilling Fields with Galaxy Catalogs

   J. G. Weston, D. R. Young, S. J. Smartt, and 3 more authors

   The Astrophysical Journal, Mar 2026

   Abs DOI

   The upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will enable astronomers to discover rare and distant astrophysical transients. Host-galaxy association is crucial for selecting the most scientifically interesting transients for follow-up. LSST deep drilling field (DDF) observations will detect distant transients occurring in galaxies below the detection limits of most all-sky catalogs. Here, we investigate the use of preexisting, field-specific catalogs for host identification in the DDFs and a ranking of their usefulness. We have compiled a database of 70 deep catalogs that overlap with the Rubin DDFs and constructed thin catalogs to be homogenized and combined for transient-host matching. A systematic ranking of their utility is discussed and applied based on the inclusion of information such as spectroscopic redshifts and morphological information. Utilizing this data against a Dark Energy Survey sample of supernovae with pre-identified hosts in the XMM-Large Scale Structure and the Extended Chandra Deep Field-South fields, we evaluate different methods for transient-host association in terms of both accuracy and processing speed. We also apply light data-cleaning techniques to identify and remove contaminants within our associations, such as diffraction spikes and blended galaxies where the correct host cannot be determined with confidence. We use a lightweight machine learning approach in the form of extreme gradient boosting to generate confidence scores in our contaminant selections and associated metrics. Finally, we discuss the computational expense of implementation within the LSST transient alert brokers, which will require efficient, fast-paced processing to handle the large stream of survey data.

2. arXiv

   Automatically distinguishing Rubin transients from AGN using variability metrics

   Dylan Magill, Matt Nicholl, Philip Wiseman, and 7 more authors

   2026

   Abs

   Stochastic variability of active galactic nuclei (AGN) can produce contaminants in the search for explosive extragalactic transients (such as supernovae and tidal disruption events). In the new era of the Rubin Observatory’s Legacy Survey of Space and Time (LSST), previously uncatalogued AGN, especially those with luminosity near the survey detection limits, are expected to produce a flood of detections that have the potential to contaminate surveys targeting other transients, leading to inefficient use of spectroscopic follow-up time. For surveys aiming to statistically characterise transient demographics, it is advantageous to use easily modelled and reproducible selection criteria to distinguish AGN from other transients, rather than machine learning. We test enacting cuts based on simple data-driven photometric variability parameters to distinguish non-AGN extragalactic transients from standard AGN variability on both Zwicky Transient Facility photometry and simulated LSST photometry from the MALLORN data set. We also investigate the impact of light curve history availability, redshift range and filter selection on selection efficiency. We find that a two-dimensional cut incorporating the ratio of detection flux and pre-detection standard deviation and the ratio of detection flux to pre-detection mean flux is the most effective cut. This approach is easily scalable as these values are included in the LSST alert packets. We provide estimates of the completeness and purity of the sample produced by enacting this cut, and gauge the AGN contamination avoided. The parameters utilised in this approach could also be implemented as features for identifying AGN in a photometric classifier.

3. SHPS

   Does Understanding Require Consensus? Explanatory Reasoning in Astroinformatics (Under Review)

   M.E. Miyata-Sturm and J. G. Weston

   2026

   Abs

   Machine learning (ML) models are increasingly used in scientific research. In some data-heavy fields, ML significantly increases the number of discoveries we can make as the amount of data collected is simply too large for humans to sift through unaided by automated systems. For example, and as we describe in detail here, in astronomy ML models can be used to discover supernovae by correctly distinguishing ‘real’ astrophysical transients within survey imaging from ‘bogus’ artefacts. How can such models contribute to our understanding of transients and other astronomical phenomena? To contribute to understanding these models must be accurate enough, which immediately gives rise to the question of how we can measure the accuracy of these models. We explore this question through two case studies of non-expert systems contributing to discovery in astronomy: first, using ML to discover supernovae, and second, using citizen science for the labelling of training data for the development of ML models for the same task. The salient measurement of accuracy is expert validation of the model’s output, but recent results showcasing agreement rates for the labelling of this data, both between experts and citizen scientists, show that the experts often disagree among themselves. How should we evaluate the models when the experts disagree? We argue that we can use explanatory reasoning to ‘stabilise’ the understanding the ML model provides, allowing us to gain understanding even in cases of moderate disagreement. In particular, taking seriously the well-known claim that there is no ‘dictatorship condition’ in explanatory reasoning – that is, there is no one criterion which trumps all other explanatory virtues – shows how we can gain understanding through the use of ML models also when they are less than perfectly accurate.

4. MNRAS

   ATLAS100 ─ I. A volume-limited sample of supernovae and related transients within 100 Mpc

   S. Srivastav, S. J. Smartt, T. Moore, and 29 more authors

   \mnras, Jul 2026

   DOI
5. arXiv

   Anomaly Hunter for Alerts (AHA): Anomaly Detection in the ZTF Transient Alert Stream

   Leyla Iskandarli, Chris J. Lintott, Steve Croft, and 2 more authors

   2026

   Abs

   Modern time-domain surveys produce alert streams at a scale that makes exhaustive manual inspection infeasible, requiring automated methods to identify unusual transients for follow-up. In this work, we present an unsupervised anomaly detection pipeline applied to the ZTF alert stream using the Lasair broker. We define normal objects as SN Ia, SN II, and SN Ib/c. Anomalous objects include (i) more exotic transients (AGN, TDEs, SLSNe, CVs, and nuclear transients) and (ii) supernova-labeled objects, either spectroscopically or by Lasair, with anomalous properties, such as incorrect or absent host associations, or non-supernova-like light curves. Our pipeline consists of three independently trained simple autoencoders operating on distinct alert stream data products: object features, triplet image cutouts, and light curves. Each model is trained on predominantly normal transients, and performance is assessed using the recall of exotic objects and the purity of all anomalous objects across both a spectroscopically classified held-out test set and the live alert stream. In the test set, performance is evaluated at a fixed rank corresponding to the top ten scoring candidates, while in the alert stream it is evaluated using an anomaly threshold defined from test set behavior. Across both settings, the algorithms consistently recover exotic transients and anomalous supernovae among their top-ranked candidates. Over 25 days of live alert stream application, we identify 87 unusual supernova candidates for follow-up. The overlap between anomalies flagged by different autoencoders in the test set is non-existent, and in the alert stream is small, with maximum overlap between any two algorithms being 11 objects. The framework is data-efficient, requiring only a few thousand training examples, making it well suited for early and ongoing application to the Rubin Observatory alert stream.

2024

1. RASTI

   Training a convolutional neural network for real–bogus classification in the ATLAS survey

   J G Weston, K W Smith, S J Smartt, and 2 more authors

   RAS Techniques and Instruments, 2024

   Abs DOI

   We present a convolutional neural network (CNN) for use in the real–bogus classification of transient detections made by the Asteroid Terrestrial-impact Last Alert System (ATLAS) and subsequent efforts to improve performance since initial development. In transient detection surveys, the number of alerts made outstrips the capacity for human scanning, necessitating the use of machine learning aids to reduce the number of false positives presented to annotators. We take a sample of recently annotated data from each of the three operating ATLAS telescope with \\∼\340 000 real (known transients) and \\∼\1030 000 bogus detections per model. We retrained the CNN architecture with these data specific to each ATLAS unit, achieving a median false positive rate (FPR) of 0.72 per cent for a 1.00 per cent missed detection rate. Further investigations indicate that if we reduce the input image size it results in increased FPR. Finally architecture adjustments and comparisons to contemporary CNNs indicate that our retrained classifier is providing an optimal FPR. We conclude that the periodic retraining and readjustment of classification models on survey data can yield significant improvements as data drift arising from changes in the optical and detector performance can lead to new features in the model and subsequent deteriorations in performance.

2. ApJL

   Discovery of the Optical and Radio Counterpart to the Fast X-Ray Transient EP 240315a

   J. H. Gillanders, L. Rhodes, S. Srivastav, and 36 more authors

   The Astrophysical Journal Letters, 2024

   Abs DOI

   Fast X-ray Transients (FXTs) are extragalactic bursts of soft X-rays first identified >10 years ago. Since then, nearly 40 events have been discovered, although almost all of these have been recovered from archival Chandra and XMM-Newton data. To date, optical sky surveys and follow-up searches have not revealed any multi-wavelength counterparts. The Einstein Probe, launched in January 2024, has started surveying the sky in the soft X-ray regime (0.5-4 keV) and will rapidly increase the sample of FXTs discovered in real time. Here, we report the first discovery of both an optical and radio counterpart to a distant FXT, the fourth source publicly released by the Einstein Probe. We discovered a fast-fading optical transient within the 3 arcmin localisation radius of EP240315a with the all-sky optical survey ATLAS, and our follow-up Gemini spectrum provides a redshift, z=4.859+/-0.002. Furthermore, we uncovered a radio counterpart in the S-band (3.0 GHz) with the MeerKAT radio interferometer. The optical (rest-frame UV) and radio luminosities indicate the FXT most likely originates from either a long gamma-ray burst or a relativistic tidal disruption event. This may be a fortuitous early mission detection by the Einstein Probe or may signpost a mode of discovery for high-redshift, high-energy transients through soft X-ray surveys, combined with locating multi-wavelength counterparts.

3. Nature

   Quasi-periodic X-ray eruptions years after a nearby tidal disruption event

   M. Nicholl, D. R. Pasham, A. Mummery, and 61 more authors

   Nature, Oct 2024

   Abs DOI

   Quasi-periodic Eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs), undergoing instabilities or interacting with a stellar object in a close orbit. It has been suggested that this disk could be created when the SMBH disrupts a passing star, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs, and two observed TDEs have exhibited X-ray flares consistent with individual eruptions. TDEs and QPEs also occur preferentially in similar galaxies. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 hours from AT2019qiz, a nearby and extensively studied optically-selected TDE. We detect and model the X-ray, ultraviolet and optical emission from the accretion disk, and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

2023

1. ApJL

   SN 2022jli: A Type Ic Supernova with Periodic Modulation of Its Light Curve and an Unusually Long Rise

   T. Moore, S. J. Smartt, M. Nicholl, and 42 more authors

   The Astrophysical Journal Letters, 2023

   Abs DOI

   We present multi-wavelength photometry and spectroscopy of SN 2022jli, an unprecedented Type Ic supernova discovered in the galaxy NGC 157 at a distance of ≈23 Mpc. The multi-band light curves reveal many remarkable characteristics. Peaking at a magnitude of g = 15.11 \pm 0.02, the high-cadence photometry reveals 12.5 \pm 0.2 day periodic undulations superimposed on the 200 day supernova decline. This periodicity is observed in the light curves from nine separate filter and instrument configurations with peak-to-peak amplitudes of ≃0.1 mag. This is the first time that repeated periodic oscillations, over many cycles, have been detected in a supernova light curve. SN 2022jli also displays an extreme early excess which fades over ≈25 days followed by a rise to a peak luminosity of L_\rm opt = 10^42.1 erg s^-1. Although the exact explosion epoch is not constrained by data, the time from explosion to maximum light is ≳59 days. The luminosity can be explained by a large ejecta mass (M_\rm ej ≈12 \pm 6 M_⊙) powered by ^56Ni but we find difficulty in quantitatively modelling the early excess with circumstellar interaction and cooling. Collision between the supernova ejecta and a binary companion is a possible source of this emission. We discuss the origin of the periodic variability in the light curve, including interaction of the SN ejecta with nested shells of circumstellar matter and neutron stars colliding with binary companions.