An Atlas of Predicted Exotic Gravitational Lenses

Wide-field optical imaging surveys will contain tens of thousands of new strong gravitational lenses. Some of these will have new and unusual image configurations, and so will enable new applications: for example, systems with high image multiplicity will allow more detailed study of galaxy and group mass distributions, while high magnification is needed to super-resolve the faintest objects in the high redshift universe. Inspired by a set of six unusual lens systems [including five selected from the Sloan Lens ACS (SLACS) and Strong Lensing Legacy (SL2S) surveys, plus the cluster Abell 1703], we consider several types of multi-component, physically-motivated lens potentials, and use the ray-tracing code "glamroc" to predict exotic image configurations. We also investigate the effects of galaxy source profile and size, and use realistic sources to predict observable magnifications and estimate very approximate relative cross-sections. We find that lens galaxies with misaligned disks and bulges produce swallowtail and butterfly catastrophes, observable as "broken" Einstein rings. Binary or merging galaxies show elliptic umbilic catastrophes, leading to an unusual Y-shaped configuration of 4 merging images. While not the maximum magnification configuration possible, it offers the possibility of mapping the local small-scale mass distribution. We estimate the approximate abundance of each of these exotic galaxy-scale lenses to be ~1 per all-sky survey. In higher mass systems, a wide range of caustic structures are expected, as already seen in many cluster lens systems. We interpret the central ring and its counter-image in Abell 1703 as a "hyperbolic umbilic" configuration, with total magnification ~100 (depending on source size). The abundance of such configurations is also estimated to be ~1 per all-sky survey.Comment: 21 pages, 30 figures, accepted by MNRAS, copyright material cleared for re-printing. High-resolution version available from http://www.slac.stanford.edu/~pjm/atla

Spatially resolved kinematics of the central regions of M83: hidden mass signatures and the role of supernovae

The barred grand-design spiral M83 (NGC 5236) is one of the most studied galaxies given its proximity, orientation, and particular complexity. Nonetheless, many aspects of the central regions remain controversial conveying our limited understanding of the inner gas and stellar kinematics, and ultimately of the nucleus evolution. In this work, we present AO VLT-SINFONI data of its central ~235x140 pc with an unprecedented spatial resolution of ~0.2 arcsec, corresponding to ~4 pc. We have focused our study on the distribution and kinematics of the stars and the ionised and molecular gas by studying in detail the Pa_alpha and Br_gamma emission, the H_2 1-0S(1) line at 2.122 micron and the [FeII] line at 1.644 micron, together with the CO absorption bands at 2.293 micron and 2.323 micron. Our results reveal a complex situation where the gas and stellar kinematics are totally unrelated. Supernova explosions play an important role in shaping the gas kinematics, dominated by shocks and inflows at scales of tens of parsecs that make them unsuitable to derive general dynamical properties. We propose that the location of the nucleus of M83 is unlikely to be related to the off-centre 'optical nucleus'. The study of the stellar kinematics reveals that the optical nucleus is a gravitationally bound massive star cluster with M_dyn = (1.1 \pm 0.4)x10^7 M_sun, formed by a past starburst. The kinematic and photometric analysis of the cluster yield that the stellar content of the cluster is well described by an intermediate age population of log T(yr) = 8.0\pm0.4, with a mass of M \simeq (7.8\pm2.4)x10^6 M_sun.Comment: 14 pages, 10 figures, accepted for publication in Ap

Ten common statistical mistakes to watch out for when writing or reviewing a manuscript

Inspired by broader efforts to make the conclusions of scientific research more robust, we have compiled a list of some of the most common statistical mistakes that appear in the scientific literature. The mistakes have their origins in ineffective experimental designs, inappropriate analyses and/or flawed reasoning. We provide advice on how authors, reviewers and readers can identify and resolve these mistakes and, we hope, avoid them in the future

Biological motion drives perception and action

Marcus Missal Presenting a few dots moving coherently on a screen can yield to the perception of human motion. This perception is based on a specific network that is segregated from the traditional motion perception network and that includes the superior temporal sulcus (STS). In this study, we investigate whether this biological motion perception network could influence the smooth pursuit response evoked by a point-light walker. We found that smooth eye velocity during pursuit initiation was larger in response to the point-light walker than in response to one of its scrambled versions, to an inverted walker or to a single dot stimulus. In addition, we assessed the proximity to the point-light walker (i.e. the amount of information about the direction contained in the scrambled stimulus and extracted from local motion cue of biological motion) of each of our scrambled stimuli in a motion direction discrimination task with manual responses and found that the smooth pursuit response evoked by those stimuli moving across the screen was modulated by their proximity to the walker. Therefore, we conclude that biological motion facilitates smooth pursuit eye movements, hence influences both perception and action

AOtools - a Python package for adaptive optics modelling and analysis

AOtools is a Python package that is open-source and aimed at providing tools for adaptive optics users and researchers. We present version 1.0, which contains tools for adaptive optics processing, including analysing data in the pupil plane, images and point spread functions in the focal plane, wavefront sensors, modelling of atmospheric turbulence, physical optical propagation of wavefronts, and conversion between frequently used adaptive optics and astronomical units. The main drivers behind AOtools is that it should be easy to install and use. To achieve this the project features extensive documentation, automated unit testing and is registered on the Python Package Index. AOtools is under continuous active development to expand the features available, and we encourage everyone involved in adaptive optics to become involved and contribute to the project

Diffeomorphic Registration of Images with Variable Contrast Enhancement

Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement

A simulator-based autoencoder for focal plane wavefront sensing

editorial reviewedInstrumental aberrations strongly limit high-contrast imaging of exoplanets, especially when they produce quasistatic speckles in the science images. With the help of recent advances in deep learning, we have developed in previous works an approach that applies convolutional neural networks (CNN) to estimate pupil-plane phase aberrations from point spread functions (PSF). In this work we take a step further by incorporating into the deep learning architecture the physical simulation of the optical propagation occurring inside the instrument. This is achieved with an autoencoder architecture, which uses a differentiable optical simulator as the decoder. Because this unsupervised learning approach reconstructs the PSFs, knowing the true phase is not needed to train the models, making it particularly promising for on-sky applications. We show that the performance of our method is almost identical to a standard CNN approach, and that the models are sufficiently stable in terms of training and robustness. We notably illustrate how we can benefit from the simulator-based autoencoder architecture by quickly fine-tuning the models on a single test image, achieving much better performance when the PSFs contain more noise and aberrations. These early results are very promising and future steps have been identified to apply the method on real data.EPIC - NNEx