On the peak radio and X-ray emission from neutron star and black hole candidate X-ray transients

We have compiled and analysed reports from the literature of (quasi-)simultaneous observations of X-ray transients at radio and X-ray wavelengths and compared them with each other and with more unusual radio-bright sources such as Cygnus X-3, GRS 1915+105 and Circinus X-1. There exists a significant (>97% likelihood) positive (rank) correlation between the peak X-ray flux P_X and radio flux density P_R for the black hole candidate (BHC) systems, and a marginally significant positive (rank) correlation for the neutron star (NS) systems. This is further evidence for a coupling between accretion and outflows in X-ray binary systems, in this case implying a relation between peak disc-accretion-rate and the number of synchroton-emitting electrons ejected. However, we also show that the distribution of `radio loudness', P_R/P_X, is significantly different for the two samples, in the sense that the BHCs generally have a higher ratio of P_R/P_X. The origin of this discrepancy is uncertain, but probably reflects differences in the energetics and/or radiative efficiency of flows around the neutron stars and black holes; we briefly discuss some of these possibilities. We conclude that these data point to the formation of a mildly relativistic jet whose luminosity is a function of the accretion rate, in the majority, if not all, of X-ray transient outbursts, but whose relation to the observed X-ray emission is dependent on the nature of the accreting compact object. (Abridged).Comment: Accepted for publication in MNRA

Interplanetary and Geomagnetic Consequences of Interacting CMEs of 13-14 June 2012

We report on the kinematics of two interacting CMEs observed on 13 and 14 June 2012. Both CMEs originated from the same active region NOAA 11504. After their launches which were separated by several hours, they were observed to interact at a distance of 100 Rs from the Sun. The interaction led to a moderate geomagnetic storm at the Earth with Dst index of approximately, -86 nT. The kinematics of the two CMEs is estimated using data from the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) onboard the Solar Terrestrial Relations Observatory (STEREO). Assuming a head-on collision scenario, we find that the collision is inelastic in nature. Further, the signatures of their interaction are examined using the in situ observations obtained by Wind and the Advance Composition Explorer (ACE) spacecraft. It is also found that this interaction event led to the strongest sudden storm commencement (SSC) (approximately 150 nT) of the present Solar Cycle 24. The SSC was of long duration, approximately 20 hours. The role of interacting CMEs in enhancing the geoeffectiveness is examined.Comment: 17 pages, 5 figures, Accepted in Solar Physics Journa

Candidate genes for field resistance to cassava brown streak disease revealed through the analysis of multiple data sources

Open Access JournalCassava (Manihot esculenta Crantz) is a food and industrial storage root crop with substantial potential to contribute to managing risk associated with climate change due to its inherent resilience and in providing a biodegradable option in manufacturing. In Africa, cassava production is challenged by two viral diseases, cassava brown streak disease (CBSD) and cassava mosaic disease. Here we detect quantitative trait loci (QTL) associated with CBSD in a biparental mapping population of a Tanzanian landrace, Nachinyaya and AR37-80, phenotyped in two locations over three years. The purpose was to use the information to ultimately facilitate either marker-assisted selection or adjust weightings in genomic selection to increase the efficiency of breeding. Results from this study were considered in relation to those from four other biparental populations, of similar genetic backgrounds, that were phenotyped and genotyped simultaneously. Further, we investigated the co-localization of QTL for CBSD resistance across populations and the genetic relationships of parents based on whole genome sequence information. Two QTL on chromosome 4 for resistance to CBSD foliar symptoms and one on each of chromosomes 11 and 18 for root necrosis were of interest. Of significance within the candidate genes underlying the QTL on chromosome 4 are Phenylalanine ammonia-lyase (PAL) and Cinnamoyl-CoA reductase (CCR) genes and three PEPR1-related kinases associated with the lignin pathway. In addition, a CCR gene was also underlying the root necrosis-resistant QTL on chromosome 11. Upregulation of key genes in the cassava lignification pathway from an earlier transcriptome study, including PAL and CCR, in a CBSD-resistant landrace compared to a susceptible landrace suggests a higher level of basal lignin deposition in the CBSD-resistant landrace. Earlier RNAscope® in situ hybridisation imaging experiments demonstrate that cassava brown streak virus (CBSV) is restricted to phloem vessels in CBSV-resistant varieties, and phloem unloading for replication in mesophyll cells is prevented. The results provide evidence for the involvement of the lignin pathway. In addition, five eukaryotic initiation factor (eIF) genes associated with plant virus resistance were found within the priority QTL regions

The Physical Processes of CME/ICME Evolution

As observed in Thomson-scattered white light, coronal mass ejections (CMEs) are manifest as large-scale expulsions of plasma magnetically driven from the corona in the most energetic eruptions from the Sun. It remains a tantalizing mystery as to how these erupting magnetic fields evolve to form the complex structures we observe in the solar wind at Earth. Here, we strive to provide a fresh perspective on the post-eruption and interplanetary evolution of CMEs, focusing on the physical processes that define the many complex interactions of the ejected plasma with its surroundings as it departs the corona and propagates through the heliosphere. We summarize the ways CMEs and their interplanetary CMEs (ICMEs) are rotated, reconfigured, deformed, deflected, decelerated and disguised during their journey through the solar wind. This study then leads to consideration of how structures originating in coronal eruptions can be connected to their far removed interplanetary counterparts. Given that ICMEs are the drivers of most geomagnetic storms (and the sole driver of extreme storms), this work provides a guide to the processes that must be considered in making space weather forecasts from remote observations of the corona.Peer 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

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