Symplectic fillings of virtually overtwisted contact structures on lens spaces

Symplectic fillings of standard tight contact structures on lens spaces are understood and classified. The situation is different if one considers non-standard tight structures (i.e. those that are virtually overtwisted), for which a classification scheme is still missing. In this work we use different approaches and employ various techniques to improve our knowledge of symplectic fillings of virtually overtwisted contact structures. We study curves configurations on surfaces to solve the problem in the case of a specific family of lens spaces. Then we give general constraints on the topology of Stein fillings of any lens space by looking at algebraic properties of integer lattices and at geometric slicing of solid tori. Furthermore, we try to place these manifolds in the context of algebraic geometry, in order to determine whether Stein fillings can be realized as Milnor fibers of hypersurfce singularities, finding a series of necessary conditions for this to happen. In the concluding part of the thesis, we focus on the connections between planar contact 3-manifolds and the theory of Artin presentations

Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

Lens modeling is the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known abundance of lenses, we find that a lens sample may be generated that is ~100% pure, but only ~20% complete. This shortfall is due primarily to the over-simplicity of the lens model. With a more optimistic robot, ~90% completeness can be achieved while rejecting ~90% of the candidate objects. The remaining candidates must be classified by human inspectors. We are able to classify lens candidates by eye at a rate of a few seconds per system, suggesting that a future 1000 square degree imaging survey containing 10^7 BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched in a modest amount of time. [Abridged]Comment: 17 pages, 11 figures, submitted to Ap

CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding

Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying the population of strong lenses or by measuring time delays in lensed quasars. Due to the rarity of galaxy-scale strongly lensed systems, fast and reliable automated lens finding methods will be essential in the era of large surveys such as LSST, Euclid, and WFIRST. To tackle this challenge, we introduce CMU DeepLens, a new fully automated galaxy-galaxy lens finding method based on Deep Learning. This supervised machine learning approach does not require any tuning after the training step which only requires realistic image simulations of strongly lensed systems. We train and validate our model on a set of 20,000 LSST-like mock observations including a range of lensed systems of various sizes and signal-to-noise ratios (S/N). We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1.4" and S/N larger than 20 on individual $g$-band LSST exposures. Finally, we emphasize the importance of realistically complex simulations for training such machine learning methods by demonstrating that the performance of models of significantly different complexities cannot be distinguished on simpler simulations. We make our code publicly available at https://github.com/McWilliamsCenter/CMUDeepLens .Comment: 12 pages, 9 figures, submitted to MNRA

Deep Convolutional Neural Networks as strong gravitational lens detectors

Future large-scale surveys with high resolution imaging will provide us with a few $10^5$ new strong galaxy-scale lenses. These strong lensing systems however will be contained in large data amounts which are beyond the capacity of human experts to visually classify in a unbiased way. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the Strong Lensing challenge organised by the Bologna Lens Factory. It achieved first and third place respectively on the space-based data-set and the ground-based data-set. The goal was to find a fully automated lens finder for ground-based and space-based surveys which minimizes human inspect. We compare the results of our CNN architecture and three new variations ("invariant" "views" and "residual") on the simulated data of the challenge. Each method has been trained separately 5 times on 17 000 simulated images, cross-validated using 3 000 images and then applied to a 100 000 image test set. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score and the recall with no false positive ($\mathrm{Recall}_{\mathrm{0FP}}$). For ground based data our best method achieved an AUC score of $0.977$ and a $\mathrm{Recall}_{\mathrm{0FP}}$ of $0.50$. For space-based data our best method achieved an AUC score of $0.940$ and a $\mathrm{Recall}_{\mathrm{0FP}}$ of $0.32$. On space-based data adding dihedral invariance to the CNN architecture diminished the overall score but achieved a higher no contamination recall. We found that using committees of 5 CNNs produce the best recall at zero contamination and consistenly score better AUC than a single CNN. We found that for every variation of our CNN lensfinder, we achieve AUC scores close to $1$ within $6\%$.Comment: 9 pages, accepted to A&

RingFinder: automated detection of galaxy-scale gravitational lenses in ground-based multi-filter imaging data

We present RingFinder, a tool for finding galaxy-scale strong gravitational lenses in multiband imaging data. By construction, the method is sensitive to configurations involving a massive foreground early-type galaxy and a faint, background, blue source. RingFinder detects the presence of blue residuals embedded in an otherwise smooth red light distribution by difference imaging in two bands. The method is automated for efficient application to current and future surveys, having originally been designed for the 150-deg2 Canada France Hawaii Telescope Legacy Survey (CFHTLS). We describe each of the steps of RingFinder. We then carry out extensive simulations to assess completeness and purity. For sources with magnification mu>4, RingFinder reaches 42% (resp. 25%) completeness and 29% (resp. 86%) purity before (resp. after) visual inspection. The completeness of RingFinder is substantially improved in the particular range of Einstein radii 0.8 < REin < 2. and lensed images brighter than g = 22.5, where it can be as high as 70%. RingFinder does not introduce any significant bias in the source or deflector population. We conclude by presenting the final catalog of RingFinder CFHTLS galaxy-scale strong lens candidates. Additional information obtained with Hubble Space Telescope and Keck Adaptive Optics high resolution imaging, and with Keck and Very Large Telescope spectroscopy, is used to assess the validity of our classification, and measure the redshift of the foreground and the background objects. From an initial sample of 640,000 early type galaxies, RingFinder returns 2500 candidates, which we further reduce by visual inspection to 330 candidates. We confirm 33 new gravitational lenses from the main sample of candidates, plus an additional 16 systems taken from earlier versions of RingFinder. First applications are presented in the SL2S galaxy-scale Lens Sample paper series.Comment: 32 pages (aastex 2col format), 6 figs, ApJ Accepte

A spectroscopic look at the gravitationally lensed type Ia SN 2016geu at z=0.409

The spectacular success of type Ia supernovae (SNe Ia) in SN-cosmology is based on the assumption that their photometric and spectroscopic properties are invariant with redshift. However, this fundamental assumption needs to be tested with observations of high-z SNe Ia. To date, the majority of SNe Ia observed at moderate to large redshifts (0.4 < z < 1.0) are faint, and the resultant analyses are based on observations with modest signal-to-noise ratios that impart a degree of ambiguity in their determined properties. In rare cases however, the Universe offers a helping hand: to date a few SNe Ia have been observed that have had their luminosities magnified by intervening galaxies and galaxy clusters acting as gravitational lenses. In this paper we present long-slit spectroscopy of the lensed SNe Ia 2016geu, which occurred at a redshift of z=0.409, and was magnified by a factor of ~55 by a galaxy located at z=0.216. We compared our spectra, which were obtained a couple weeks to a couple months past peak light, with the spectroscopic properties of well-observed, nearby SNe Ia, finding that SN 2016geu's properties are commensurate with those of SNe Ia in the local universe. Based primarily on the velocity and strength of the Si II 6355 absorption feature, we find that SN 2016geu can be classified as a high-velocity, high-velocity gradient and "core-normal" SN Ia. The strength of various features (measured though their pseudo-equivalent widths) argue against SN 2016geu being a faint, broad-lined, cool or shallow-silicon SN Ia. We conclude that the spectroscopic properties of SN 2016geu imply that it is a normal SN Ia, and when taking previous results by other authors into consideration, there is very little, if any, evolution in the observational properties of SNe Ia up to z~0.4. [Abridged]Comment: 12 pages, 5 figures, 4 tables. Submitted to MNRAS. Comments welcome