A new perspective on GCRT J1745-3009

Two WSRT observations were performed and five archival VLA data were reduced in order to redetect the enigmatic radio transient GCRT J1745-3009. The source was not redetected. We were, however, able to extract important new information from the discovery dataset. Our reanalysis excludes models that predict symmetric bursts, but the transient white dwarf pulsar is favoured. Although we now have more contraints on the properties of this source, we are still unsure about its basic model.Comment: 11 pages, 5 figure

In vitro digestibility and Caco-2 cell bioavailability of sea lettuce (Ulva fenestrata) proteins extracted using pH-shift processing

Seaweed is a promising sustainable source of vegan protein as its farming does not require arable land, pesticides/insecticides, nor freshwater supply. However, to be explored as a novel protein source the content and nutritional quality of protein in seaweed need to be improved. We assessed the influence of pH-shift processing on protein degree of hydrolysis (%DH), protein/peptide size distribution, accessibility, and cell bioavailability of Ulva fenestrata proteins after in vitro gastrointestinal digestion. pH-shift processing of Ulva, which concentrated its proteins 3.5-times, significantly improved the %DH from 27.7\ub12.6% to 35.7\ub12.1% and the amino acid accessibility from 56.9\ub14.1% to 72.7\ub10.6%. Due to the higher amino acid accessibility, the amount of most amino acids transported across the cell monolayers was higher in the protein extracts. Regarding bioavailability, both Ulva and protein extracts were as bioavailable as casein. The protein/peptide molecular size distribution after digestion did not disclose a clear association with bioavailability

Detailed study of the GRB 030329 radio afterglow deep into the non-relativistic phase

We explore the physics behind one of the brightest radio afterglows ever, GRB 030329, at late times when the jet is non-relativistic. We determine the physical parameters of the blast wave and its surroundings, in particular the index of the electron energy distribution, the energy of the blast wave, and the density (structure) of the circumburst medium. We then compare our results with those from image size measurements. We observed the GRB 030329 radio afterglow with the Westerbork Synthesis Radio Telescope and the Giant Metrewave Radio Telescope at frequencies from 325 MHz to 8.4 GHz, spanning a time range of 268-1128 days after the burst. We modeled all the available radio data and derived the physical parameters. The index of the electron energy distribution is p=2.1, the circumburst medium is homogeneous, and the transition to the non-relativistic phase happens at t_NR ~ 80 days. The energy of the blast wave and density of the surrounding medium are comparable to previous findings. Our findings indicate that the blast wave is roughly spherical at t_NR, and they agree with the implications from the VLBI studies of image size evolution. It is not clear from the presented dataset whether we have seen emission from the counter jet or not. We predict that the Low Frequency Array will be able to observe the afterglow of GRB 030329 and many other radio afterglows, constraining the physics of the blast wave during its non-relativistic phase even further.Comment: 9 pages, 2 figures; accepted for publication in Astronomy & Astrophysics after minor revisions; small changes in GMRT fluxes at 1280 MH

An automated archival VLA transients survey

In this paper we present the results of a survey for radio transients using data obtained from the Very Large Array archive. We have reduced, using a pipeline procedure, 5037 observations of the most common pointings - i.e. the calibrator fields. These fields typically contain a relatively bright point source and are used to calibrate `target' observations: they are therefore rarely imaged themselves. The observations used span a time range ~ 1984 - 2008 and consist of eight different pointings, three different frequencies (8.4, 4.8 and 1.4 GHz) and have a total observing time of 435 hours. We have searched for transient and variable radio sources within these observations using components from the prototype LOFAR transient detection system. In this paper we present the methodology for reducing large volumes of Very Large Array data; and we also present a brief overview of the prototype LOFAR transient detection algorithms. No radio transients were detected in this survey, therefore we place an upper limit on the snapshot rate of GHz frequency transients > 8.0 mJy to rho less than or equal to 0.032 deg^-2 that have typical timescales 4.3 to 45.3 days. We compare and contrast our upper limit with the snapshot rates - derived from either detections or non-detections of transient and variable radio sources - reported in the literature. When compared with the current Log N - Log S distribution formed from previous surveys, we show that our upper limit is consistent with the observed population. Current and future radio transient surveys will hopefully further constrain these statistics, and potentially discover dominant transient source populations. In this paper we also briefly explore the current transient commissioning observations with LOFAR, and the impact they will make on the field.Comment: Accepted for publication in MNRA

Identifying transient and variable sources in radio images

With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. We use a simple anomaly detection algorithm and a penalised logistic regression algorithm. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as PYTHON tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools)

MeerCRAB: MeerLICHT classification of real and bogus transients using deep learning

Instrumentatio