Galaxy interactions are the dominant trigger for local type 2 quasars

The triggering mechanism for the most luminous, quasar-like active galactic nuclei (AGN) remains a source of debate, with some studies favouring triggering via galaxy mergers, but others finding little evidence to support this mechanism. Here, we present deep Isaac Newton Telescope/Wide Field Camera imaging observations of a complete sample of 48 optically-selected type 2 quasars $-$ the QSOFEED sample (L$_{\rm [OIII]}>$10$^{8.5}$$L_{\odot}$; $z < 0.14$). Based on visual inspection by eight classifiers, we find clear evidence that galaxy interactions are the dominant triggering mechanism for quasar activity in the local universe, with 65$^{+6}_{-7}$ per cent of the type 2 quasar hosts showing morphological features consistent with galaxy mergers or encounters, compared with only 22$^{+5}_{-4}$ per cent of a stellar-mass- and redshift-matched comparison sample of non-AGN galaxies $-$ a 5$\sigma$ difference. The type 2 quasar hosts are a factor 3.0$^{+0.5}_{-0.8}$ more likely to be morphologically disturbed than their matched non-AGN counterparts, similar to our previous results for powerful 3CR radio AGN of comparable [OIII] emission-line luminosity and redshift. In contrast to the idea that quasars are triggered at the peaks of galaxy mergers as the two nuclei coalesce, and only become visible post-coalescence, the majority of morphologically-disturbed type 2 quasar sources in our sample are observed in the pre-coalescence phase (61$^{+8}_{-9}$ per cent). We argue that much of the apparent ambiguity that surrounds observational results in this field is a result of differences in the surface brightness depths of the observations, combined with the effects of cosmological surface brightness dimming.Comment: Accepted for publication in MNRAS. 17 pages, 7 figure

'Glocal' disorder: causes, conduct and consequences of the 2008 Greek unrest

This article examines the unrest that emanated in Athens and rolled out across Greek cities in December 2008 as a case through which to advance understanding of how local, national and international arenas may together shape localised episodes of disorder. We begin by addressing the proximate and structural causes of the unrest, before turning to explore the multifarious character of protest actions, including novel and derivative forms of contestation deployed by protestors, and public debate about the appropriate apportioning of blame amongst the variety of actors involved. Finally, we look at the diverse outcomes of the unrest and their impact upon extant socio-political tensions. For each stage of the lifecycle of the unrest, we evaluate the relevance of international actors, practices and discourses. Our analysis of the Greek unrest of 2008 suggests, first, that the array of intersections between global, national and local dimensions of unrest are more diverse than has heretofore been recognised by pertinent scholarship; and second, that international or transnational factors may play a significant role in the emergence, conduct and consequences of disorder even in instances where national and local dynamics remain predominant

Time-varying double-peaked emission lines following the sudden ignition of the dormant galactic nucleus AT2017bcc

We present a pan-chromatic study of AT2017bcc, a nuclear transient that was discovered in 2017 within the skymap of a reported burst-like gravitational wave candidate, G274296. It was initially classified as a superluminous supernova, and then reclassified as a candidate tidal disruption event. Its optical light curve has since shown ongoing variability with a structure function consistent with that of an active galactic nucleus, however earlier data shows no variability for at least 10 years prior to the outburst in 2017. The spectrum shows complex profiles in the broad Balmer lines: a central component with a broad blue wing, and a boxy component with time-variable blue and red shoulders. The H$\alpha$ emission profile is well modelled using a circular accretion disc component, and a blue-shifted double Gaussian which may indicate a partially obscured outflow. Weak narrow lines, together with the previously flat light curve, suggest that this object represents a dormant galactic nucleus which has recently been re-activated. Our time-series modelling of the Balmer lines suggests that this is connected to a disturbance in the disc morphology, and we speculate this could involve a sudden violent event such as a tidal disruption event involving the central supermassive black hole. Although we find that the redshifts of AT2017bcc ($z=0.13$) and G274296 ($z>0.42$) are inconsistent, this event adds to the growing diversity of both nuclear transients and multi-messenger contaminants.Comment: Submitted to MNRA

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at mL∼16 and ∼200 mmag at mL∼18. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

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 32x32 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% with a false positive rate of 1.5%. 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, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe 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 presentan approach for creating a training set by using all detections in the science images to be thesample 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-by-21pixel stamps centered 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% prediction accuracy on the real detections at a false alarm rate of 1%

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 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

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

Searching for Fermi GRB optical counterparts with the prototype gravitational-wave optical transient observer (GOTO)

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 multi-wavelength 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 square degrees and its depth of ≈20 mag. We also detail several methods to improve our overall performance for future follow-up programs of Fermi GRBs

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide-field, high-cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy; however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO) whose primary science objective is the optical follow-up of gravitational wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near-real time to fully exploit the time domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this ‘off-the-shelf’ pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to subpixel levels, and that measured L-band photometries are accurate to ∼ 50 mmag at mL ∼ 16 and ∼ 200 mmag at mL ∼ 18. These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic ‘obs package’ that others can build upon, should they wish to use the LSST Science Pipelines to process data from other facilities. Keywords: astronomy data analysis – surveys – atrometry – photometry</p