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  • Oxford University Press (OUP)  (19)
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  • Oxford University Press (OUP)  (19)
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  • 1
    Online Resource
    Online Resource
    First joint observation by the underground gravitational-wave detector KAGRA with GEO 600
    Oxford University Press (OUP) ; 2022
    In:  Progress of Theoretical and Experimental Physics Vol. 2022, No. 6 ( 2022-06-09)
    Details
    In: Progress of Theoretical and Experimental Physics, Oxford University Press (OUP), Vol. 2022, No. 6 ( 2022-06-09)
    Abstract: We report the results of the first joint observation of the KAGRA detector with GEO 600. KAGRA is a cryogenic and underground gravitational-wave detector consisting of a laser interferometer with 3 km arms, located in Kamioka, Gifu, Japan. GEO 600 is a British–German laser interferometer with 600 m arms, located near Hannover, Germany. GEO 600 and KAGRA performed a joint observing run from April 7 to 20, 2020. We present the results of the joint analysis of the GEO–KAGRA data for transient gravitational-wave signals, including the coalescence of neutron-star binaries and generic unmodeled transients. We also perform dedicated searches for binary coalescence signals and generic transients associated with gamma-ray burst events observed during the joint run. No gravitational-wave events were identified. We evaluate the minimum detectable amplitude for various types of transient signals and the spacetime volume for which the network is sensitive to binary neutron-star coalescences. We also place lower limits on the distances to the gamma-ray bursts analyzed based on the non-detection of an associated gravitational-wave signal for several signal models, including binary coalescences. These analyses demonstrate the feasibility and utility of KAGRA as a member of the global gravitational-wave detector network.
    Type of Medium: Online Resource
    ISSN: 2050-3911
    URL: Article
    DOI: 10.1093/ptep/ptac073
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2705045-2
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  • 2
    Online Resource
    Online Resource
    GRB 201015A and the nature of low-luminosity soft gamma-ray bursts
    Oxford University Press (OUP) ; 2023
    In:  Monthly Notices of the Royal Astronomical Society Vol. 523, No. 4 ( 2023-06-22), p. 4923-4937
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 523, No. 4 ( 2023-06-22), p. 4923-4937
    Abstract: GRB 201015A is a peculiarly low luminosity, spectrally soft gamma-ray burst (GRB), with T90 = 9.8 ± 3.5 s (time interval of detection of 90  per cent of photons from the GRB), and an associated supernova (likely to be type Ic or Ic-BL). GRB 201015A has an isotropic energy $E_{\gamma , \rm iso}$$= 1.75 ^{+0.60} _{-0.53} \times 10^{50}$ erg, and photon index $\Gamma = 3.00 ^{+0.50} _{-0.42}$ (15–150 keV). It follows the Amati relation, a correlation between $E_{\gamma , \rm iso}$ and spectral peak energy Ep followed by long GRBs. It appears exceptionally soft based on Γ, the hardness ratio of HR  = 0.47 ± 0.24, and low-Ep, so we have compared it to other GRBs sharing these properties. These events can be explained by shock breakout, poorly collimated jets, and off-axis viewing. Follow-up observations of the afterglow taken in the X-ray, optical, and radio reveal a surprisingly late flattening in the X-ray from t = (2.61 ± 1.27) × 104 s to $t = 1.67 ^{+1.14} _{-0.65} \times 10^6$ s. We fit the data to closure relations describing the synchrotron emission, finding the electron spectral index to be $p = 2.42 ^{+0.44} _{-0.30}$ and evidence of late-time energy injection with coefficient $q = 0.24 ^{+0.24} _{-0.18}$. The jet half opening angle lower limit (θj ≥ 16°) is inferred from the non-detection of a jet break. The launch of SVOM and Einstein Probe in 2023 should enable detection of more low-luminosity events like this, providing a fuller picture of the variety of GRBs.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stad1703
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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  • 3
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    Online Resource
    Light-curve classification with recurrent neural networks for GOTO: dealing with imbalanced data
    Oxford University Press (OUP) ; 2021
    In:  Monthly Notices of the Royal Astronomical Society Vol. 505, No. 3 ( 2021-06-23), p. 4345-4361
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 505, No. 3 ( 2021-06-23), p. 4345-4361
    Abstract: 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 overrepresented 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, 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.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stab1545
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2016084-7
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  • 4
    Online Resource
    Online Resource
    The Gravitational-wave Optical Transient Observer (GOTO): prototype performance and prospects for transient science
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 511, No. 2 ( 2022-01-12), p. 2405-2422
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 511, No. 2 ( 2022-01-12), p. 2405-2422
    Abstract: 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, and 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 2017 June, 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.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stac013
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
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  • 5
    Online Resource
    Online Resource
    Searching for electromagnetic counterparts to gravitational-wave merger events with the prototype Gravitational-Wave Optical Transient Observer (GOTO-4)
    Oxford University Press (OUP) ; 2020
    In:  Monthly Notices of the Royal Astronomical Society Vol. 497, No. 1 ( 2020-09-01), p. 726-738
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 497, No. 1 ( 2020-09-01), p. 726-738
    Abstract: We report the results of optical follow-up observations of 29 gravitational-wave (GW) 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 (EM) 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 h after receiving an alert (9.90 h 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 localization performance of the evolving GW 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 EM limits on the population of distant ($\hat{D}_L = 1.3$ Gpc) 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.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/staa1845
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2016084-7
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  • 6
    Online Resource
    Online Resource
    Machine learning for transient recognition in difference imaging with minimum sampling effort
    Oxford University Press (OUP) ; 2020
    In:  Monthly Notices of the Royal Astronomical Society Vol. 499, No. 4 ( 2020-11-10), p. 6009-6017
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 499, No. 4 ( 2020-11-10), p. 6009-6017
    Abstract: 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 $95{{\ \rm per\ cent}}$ prediction accuracy on the real detections at a false alarm rate of $1{{\ \rm per\ cent}}$.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/staa3096
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2016084-7
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  • 7
    Online Resource
    Online Resource
    Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream
    Oxford University Press (OUP) ; 2021
    In:  Monthly Notices of the Royal Astronomical Society Vol. 503, No. 4 ( 2021-04-09), p. 4838-4854
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 503, No. 4 ( 2021-04-09), p. 4838-4854
    Abstract: 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 prioritize human vetting efforts and inform future model optimization via active learning. To fully realize 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 per cent) compared against classifiers trained with fully human-labelled data sets, while 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.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stab633
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2016084-7
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  • 8
    Online Resource
    Online Resource
    Self-supervised clustering on image-subtracted data with deep-embedded self-organizing map
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 518, No. 1 ( 2022-11-17), p. 752-762
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 518, No. 1 ( 2022-11-17), p. 752-762
    Abstract: 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 32 × 32 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 $6.6{{\ \rm per\,cent}}$ with a false-positive rate of $1.5{{\ \rm per\,cent}}$. 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, e.g. built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stac3103
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
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  • 9
    Online Resource
    Online Resource
    Searching for Fermi GRB optical counterparts with the prototype Gravitational-wave Optical Transient Observer (GOTO)
    Oxford University Press (OUP) ; 2021
    In:  Monthly Notices of the Royal Astronomical Society Vol. 507, No. 4 ( 2021-09-16), p. 5463-5476
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 507, No. 4 ( 2021-09-16), p. 5463-5476
    Abstract: The typical detection rate of ∼1 gamma-ray burst (GRB) per day by the Fermi Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the Fermi localization typically prevents rapid identification of multiwavelength counterparts. We report the follow-up of 93 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 ≈3 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 ≈40 deg2 and its depth of ≈20 mag. We also detail several methods to improve our overall performance for future follow-up programmes of Fermi GRBs.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stab2499
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2016084-7
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  • 10
    Online Resource
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    Evidence that short-period AM CVn systems are diverse in outburst behaviour
    Oxford University Press (OUP) ; 2021
    In:  Monthly Notices of the Royal Astronomical Society Vol. 502, No. 4 ( 2021-02-27), p. 4953-4962
    Details
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 502, No. 4 ( 2021-02-27), p. 4953-4962
    Abstract: We present results of our analysis of up to 15 yr of photometric data from eight AM CVn systems with orbital periods between 22.5 and 26.8 min. Our data have 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 ultracompact 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 superoutbursts (SOs) and standstills, SU UMa systems with regular, shorter SOs, and nova-like systems that 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 SOs in an AM CVn system. Our results will inform theoretical modelling of the outbursts of hydrogen deficient systems.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    URL: Article
    DOI: 10.1093/mnras/stab389
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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