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With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a Convolutional Neural Network (CNN) that analyses the outputs of semi-analytic methods which parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialised lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically re-initialise the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed S��rsic sources, accurately classifies source reconstructions of the same type with a precision $P > 0.99$ and recall $R > 0.99$. The same CNN, without re-training, achieves $P=0.89$ and $R=0.89$ when classifying source reconstructions of more complex lensed HUDF sources. Using the CNN predictions to re-initialise the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling., 13 pages, 13 figures, accepted for publication in MNRAS

The BEAn (Bragg Edge Analysis) software has been developed as a toolkit for analysis of Bragg edges and strain maps from data obtained at the time-of-flight imaging instrument IMAT or other compatible instruments. The code is built primarily using Python 3 and the Qt framework, and includes tools useful for neutron imaging such as principal component analysis. This paper introduces BEAn and its features, briefly discuses the scientific concepts behind them, and concludes with planned future work on the code.

Follow-up observations of (sub-)mm-selected gravitationally-lensed systems have allowed a more detailed study of the dust-enshrouded phase of star-formation up to very early cosmic times. Here, the case of the gravitationally lensed merger in HATLAS J142935.3-002836 (also known as H1429-0028; z_lens=0.218, z_bkg=1.027) is revisited following recent developments in the literature and new APEX observations targeting two carbon monoxide (CO) rotational transitions J_up=3 and 6. We show that the line-profiles comprise three distinct velocity components, where the fainter high-velocity one is less magnified and more compact. The modelling of the observed spectral line energy distribution of CO J_up=2 to 6 and [CI]3P_1-3P_0 assumes a large velocity gradient scenario, where the analysis is based on four statistical approaches. Since the detected gas and dust emission comes exclusively from only one of the two merging components (the one oriented North-South, NS), we are only able to determine upper-limits for the companion. The molecular gas in the NS component in H1429-0028 is found to have a temperature of ~70K, a volume density of log(n[/cm3])~3.7, to be expanding at ~10km/s/pc, and amounts to M_H2=4(-2,+3)*1e9 Msun. The CO to H2 conversion factor is estimated to be alpha_CO=0.4(-0.2,+0.3) Msun/(K.km/s.pc2). The NS galaxy is expected to have a factor of >10x more gas than its companion (M_H2, Accepted on MNRAS (14 pages, 12 figures)