Kendall Ackley’s research while affiliated with University of Warwick and other places

To support asteroid-related studies, current motion detectors are utilized to select moving object candidates based on their visualizations and movements in sequences of sky exposures. However, the existing detectors encounter the manual parameter settings which require experts to assign proper parameters. Moreover, although the deep learning approach could automate the detection process, these approaches still require synthetic images and hand-engineered features to improve their performance. In this work, we propose an end-to-end deep learning model consisting of two branches. The first branch is trained with contrastive learning to extract a contrastive feature from sequences of sky exposures. This learning method encourages the model to capture a lower-dimensional representation, ensuring that sequences with moving sources (i.e., potential asteroids) are distinct from those without moving sources. The second branch is designed to learn additional features from the sky exposure sequences, which are then concatenated into the movement features before being processed by subsequent layers for the detection of asteroid candidates. We evaluate our model on sufficiently long-duration sequences and perform a comparative study with detection software. Additionally, we demonstrate the use of our model to suggest potential asteroids using photometry filtering. The proposed model outperforms the baseline model for asteroid streak detection by +7.70% of f1-score. Moreover, our study shows promising performance for long-duration sequences and improvement after adding the contrastive feature. Additionally, we demonstrate the uses of our model with the filtering to detect potential asteroids in wide-field detection using the long-duration sequences. Our model could complement the software as it suggests additional asteroids to its detection result.

The search for the electromagnetic counterparts to gravitational wave (GW) events has been rapidly gathering pace in recent years thanks to the increasing number and capabilities of both gravitational wave detectors and wide field survey telescopes. Difficulties remain, however, in detecting these counterparts due to their inherent scarcity, faintness and rapidly evolving nature. To find these counterparts, it is important that one optimises the observing strategy for their recovery. This can be difficult due to the large number of potential variables at play. Such follow-up campaigns are also capable of detecting hundreds or potentially thousands of unrelated transients, particularly for GW events with poor localisation. Even if the observations are capable of detecting a counterpart, finding it among the numerous contaminants can prove challenging. Here we present the Gravitational wave Electromagnetic RecovRY code (GERry) to perform detailed analysis and survey-agnostic quantification of observing campaigns attempting to recover electromagnetic counterparts. GERry considers the campaign's spatial, temporal and wavelength coverage, in addition to Galactic extinction and the expected counterpart light curve evolution from the GW 3D localisation volume. It returns quantified statistics that can be used to: determine the probability of having detected the counterpart, identify the most promising sources, and assess and refine strategy. Here we demonstrate the code to look at the performance and parameter space probed by current and upcoming wide-field surveys such as GOTO & VRO.

The Gravitational-wave Optical Transient Observer (GOTO) is a project dedicated to identifying optical counter-parts to gravitational-wave detections using a network of dedicated, wide-field telescopes. After almost a decade of design, construction, and commissioning work, the GOTO network is now fully operational with two antipodal sites: La Palma in the Canary Islands and Siding Spring in Australia. Both sites host two independent robotic mounts, each with a field-of-view of 44 square degrees formed by an array of eight 40 cm telescopes, resulting in an instantaneous 88 square degree field-of-view per site. All four telescopes operate as a single integrated network, with the ultimate aim of surveying the entire sky every 2-3 days and allowing near-24-hour response to transient events within a minute of their detection. In the modern era of transient astronomy, automated telescopes like GOTO form a vital link between multi-messenger discovery facilities and in-depth follow-up by larger telescopes. GOTO is already producing a wide range of scientific results, assisted by an efficient discovery pipeline and a successful citizen science project: Kilonova Seekers.

The Gravitational-wave Optical Transient Observer (GOTO) is a project dedicated to identifying optical counterparts to gravitational-wave detections using a network of dedicated, wide-field telescopes. After almost a decade of design, construction, and commissioning work, the GOTO network is now fully operational with two antipodal sites: La Palma in the Canary Islands and Siding Spring in Australia. Both sites host two independent robotic mounts, each with a field-of-view of 44 square degrees formed by an array of eight 40 cm telescopes, resulting in an instantaneous 88 square degree field-of-view per site. All four telescopes operate as a single integrated network, with the ultimate aim of surveying the entire sky every 2-3 days and allowing near-24-hour response to transient events within a minute of their detection. In the modern era of transient astronomy, automated telescopes like GOTO form a vital link between multi-messenger discovery facilities and in-depth follow-up by larger telescopes. GOTO is already producing a wide range of scientific results, assisted by an efficient discovery pipeline and a successful citizen science project: Kilonova Seekers.

We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRB association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilizing a χ2-minimization code, MINIM, and numerous parameters are constrained. The median values of ejecta mass (Mej), magnetar’s initial spin period (Pi), and magnetic field (B) for GRB-SNe are determined to be ≈5.2 M⊙, 20.5 ms, and 20.1 × 1014 G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the three-dimensional parameter space encompassing Mej, Pi, and B for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervized ML clustering algorithms on the parameters Mej, Pi, and B for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of ∼95 per cent. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on Pi and B values results in an accuracy of ∼84 per cent. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for Mej, Pi, and B than those of SLSNe-I, FBOTs, LGRBs, and SGRBs. This indicates that magnetars with different Pi and B can give birth to distinct types of transients.

We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRBs association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilising a χ2 −minimisation code, MINIM, and numerous parameters are constrained. The median values of ejecta mass (Mej), magnetar's initial spin period (Pi) and magnetic field (B) for GRB-SNe are determined to be ≈ 5.2 M⊙, 20.5 ms and 20.1 × 10^14 G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the 3-dimensional parameter space encompassing Mej, Pi, and B for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervised ML clustering algorithms on the parameters Mej, Pi, and B for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of ∼95%. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on Pi and B values results in an accuracy of ∼84%. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for Mej, Pi, and B than those of SLSNe-I, FBOTs, LGRBs and SGRBs. This indicates that magnetars with different Pi and B can give birth to distinct types of transients.

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GW)2 and likely production sites for heavy element nucleosynthesis via rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright gamma-ray burst GRB 230307A. We show that GRB 230307A belongs to the class of long-duration gamma-ray bursts associated with compact object mergers4-6, and contains a kilonova similar to AT2017gfo, associated with the gravitational-wave merger GW1708177-12. We obtained James Webb Space Telescope mid-infrared (mid-IR) imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns which we interpret as tellurium (atomic mass A=130), and a very red source, emitting most of its light in the mid-IR due to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy element nucleosynthesis across the Universe.

The Interplanetary Network (IPN) is a detection, localization and alert system that utilizes the arrival time of transient signals in gamma-ray detectors on spacecraft separated by planetary baselines to geometrically locate the origin of these transients. Due to the changing astrophysical landscape and the new emphasis on time domain and multi-messenger astrophysics (TDAMM) from the Pathways to Discovery in Astronomy and Astrophysics for the 2020s, this Gamma-ray Transient Network Science Analysis Group was tasked to understand the role of the IPN and high-energy monitors in this new era. The charge includes describing the science made possible with these facilities, tracing the corresponding requirements and capabilities, and highlighting where improved operations of existing instruments and the IPN would enhance TDAMM science. While this study considers the full multiwavelength and multimessenger context, the findings are specific to space-based high-energy monitors. These facilities are important both for full characterization of these transients as well as facilitating follow-up observations through discovery and localization. The full document reports a brief history of this field, followed by our detailed analyses and findings in some 68 pages, providing a holistic overview of the role of the IPN and high-energy monitors in the coming decades.