7 research outputs found

    Search for gravitational waves from Scorpius X-1 in LIGO O3 data with corrected orbital ephemeris

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    Improved observational constraints on the orbital parameters of the low-mass X-ray binary Scorpius X-1 were recently published in Killestein et al. In the process, errors were corrected in previous orbital ephemerides, which have been used in searches for continuous gravitational waves from Sco X-1 using data from the Advanced LIGO detectors. We present the results of a reanalysis of LIGO detector data from the third observing run of Advanced LIGO and Advanced Virgo using a model-based cross-correlation search. The corrected region of parameter space, which was not covered by previous searches, was about 1/3 as large as the region searched in the original O3 analysis, reducing the required computing time. We have confirmed that no detectable signal is present over a range of gravitational-wave frequencies from 25 to 1600 Hz, analogous to the null result of Abbott et al. Our search sensitivity is comparable to that of Abbott et al., who set upper limits corresponding, between 100 and 200 Hz, to an amplitude h0 of about 10−25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4 × 10−26 assuming the optimal orientation

    Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

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    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

    GRB 230911A: The first discovery of a Fermi GRB optical counterpart with the gravitational-wave optical transient observer (GOTO)

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    We report on the detection of candidate optical counterpart GOTO23akf/AT2023shv to the GRB 230911A with the Gravitational-wave Optical Transient Observer (GOTO) instruments located at La Palma, Canary Islands, and Siding Spring Observatory, Australia. The Fermi Gamma-ray Burst Monitor, which finds gamma-ray bursts (GRBs) nearly every two days, detected GRB 230911A with a statistical uncertainty of 4fdg1. However, the large (∼10–100 deg2) localization areas mostly impede the rapid identification of an optical counterpart. GOTO facilities fully covered 90% localization area of the GRB 230911A. We proposed GOTO23akf as the optical afterglow of GRB 230911A, subsequently confirmed through Swift-X-Ray Telescope observations in which an uncatalogued X-ray source spatially coincident with the GOTO candidate was detected. This is the first optical afterglow discovery for a Fermi GRB with the newly expanded GOTO network

    Joint Application of Magnetic Resonance Imaging and Biochemical Biomarkers in Diagnosis of Multiple Sclerosis

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