On the nature of the short duration GRB 050906

The definitive version is available at www.blackwell-synergy.com. Copyright Blackwell Publishing DOI : 10.1111/j.1365-2966.2007.11953.xPeer reviewe

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

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

X-shooter and ALMA spectroscopy of GRB 161023A A study of metals and molecules in the line of sight towards a luminous GRB

Context. Long gamma-ray bursts (GRBs) are produced during the dramatic deaths of massive stars with very short lifetimes, meaning that they explode close to the birth place of their progenitors. Over a short period they become the most luminous objects observable in the Universe, being perfect beacons to study high-redshift star-forming regions. Aims. We aim to use the afterglow of GRB 161023A at a redshift z = 2.710 as a background source to study the environment of the explosion and the intervening systems along its line of sight. Methods. For the first time, we complement ultraviolet (UV), optical and near-infrared (NIR) spectroscopy with millimetre spectroscopy using the Atacama Large Millimeter Array (ALMA), which allows us to probe the molecular content of the host galaxy. The X-shooter spectrum shows a plethora of absorption features including fine-structure and metastable transitions of Fe, Ni, Si, C, and O. We present photometry ranging from 43 s to over 500 days after the burst. Results. We infer a host-galaxy metallicity of [Zn/H] = −1.11 ± 0.07, which, corrected for dust depletion, results in [X/H] = −0.94 ± 0.08. We do not detect molecular features in the ALMA data, but we derive limits on the molecular content of log(NCO/cm−2) < 15.7 and log(NHCO+/cm−-12, which are consistent with those that we obtain from the optical spectra, log(NH2/cm−2)< 15.2 and log(NCO/cm−2) < 14.5. Within the host galaxy, we detect three velocity systems through UV, optical and NIR absorption spectroscopy, all with levels that were excited by the GRB afterglow. We determine the distance from these systems to the GRB to be in the range between 0.7 and 1.0 kpc. The sight line to GRB 161023A shows nine independent intervening systems, most of them with multiple components. Conclusions. Although no molecular absorption was detected for GRB 161023A, we show that GRB millimetre spectroscopy is now feasible and is opening a new window on the study of molecular gas within star-forming galaxies at all redshifts. The most favoured lines of sight for this purpose will be those with high metallicity and dust

Processing GOTO data with the Rubin Observatory LSST Science Pipelines II: forced photometry and light curves

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 (1σ) for bright (i.e., ∼ 14th mag) sources to 200 mmag for faint (i.e., ∼ 18th mag) sources. Additionally, we compare our results to those obtained by GOTO’s own in-house pipeline, gotophoto, and obtain similar results. Based on repeatability measurements, we measure a 5σ 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., 17th 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

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

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