Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed sources. One ConvNet is trained with single \textit{r}-band galaxy images, hence basing the classification mostly on the morphology. While the other ConvNet is trained on \textit{g-r-i} composite images, relying mostly on colours and morphology. We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample. The new lens-finders achieve a higher accuracy and completeness in identifying gravitational lens candidates, especially the single-band ConvNet. Our analysis indicates that this is mainly due to improved simulations of the lensed sources. In particular, the single-band ConvNet can select a sample of lens candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed gravitational lenses in the LRG sample. With this particular setup and limited human intervention, it will be possible to retrieve, in future surveys such as Euclid, a sample of lenses exceeding in size the total number of currently known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA

The galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3

We aim to investigate the galaxy environment in GAMA Galaxy Groups Catalogue (G3C) using a volume-limited galaxy sample from the Kilo Degree Survey Data Release 3. The k-Nearest Neighbour technique is adapted to take into account the probability density functions (PDFs) of photometric redshifts in our calculations. This algorithm was tested on simulated KiDS tiles, showing its capability of recovering the relation between galaxy colour, luminosity and local environment. The characterization of the galaxy environment in G3C groups shows systematically steeper density contrasts for more massive groups. The red galaxy fraction gradients in these groups is evident for most of group mass bins. The density contrast of red galaxies is systematically higher at group centers when compared to blue galaxy ones. In addition, distinct group center definitions are used to show that our results are insensitive to center definitions. These results confirm the galaxy evolution scenario which environmental mechanisms are responsible for a slow quenching process as galaxies fall into groups and clusters, resulting in a smooth observed colour gradients in galaxy systems.Comment: 14 pages, Accepted to MNRA

Barred Galaxies in the Coma Cluster

We use ACS data from the HST Treasury survey of the Coma cluster (z~0.02) to study the properties of barred galaxies in the Coma core, the densest environment in the nearby Universe. This study provides a complementary data point for studies of barred galaxies as a function of redshift and environment. From ~470 cluster members brighter than M_I = -11 mag, we select a sample of 46 disk galaxies (S0--Im) based on visual classification. The sample is dominated by S0s for which we find an optical bar fraction of 47+/-11% through ellipse fitting and visual inspection. Among the bars in the core of the Coma cluster, we do not find any very large (a_bar > 2 kpc) bars. Comparison to other studies reveals that while the optical bar fraction for S0s shows only a modest variation across low-to-intermediate density environments (field to intermediate-density clusters), it can be higher by up to a factor of ~2 in the very high-density environment of the rich Coma cluster core.Comment: Proceedings of the Bash symposium, to appear in the Astronomical Society of the Pacific Conference Series, eds. L. Stanford, L. Hao, Y. Mao, J. Gree

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end of r<20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared bands are added. While the fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter $\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 $\mu$, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives $\delta z<4e-5$ and $\sigma_z<0.019$. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.Comment: A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz

Special Geometry of Euclidean Supersymmetry I: Vector Multiplets

We construct the general action for Abelian vector multiplets in rigid 4-dimensional Euclidean (instead of Minkowskian) N=2 supersymmetry, i.e., over space-times with a positive definite instead of a Lorentzian metric. The target manifolds for the scalar fields turn out to be para-complex manifolds endowed with a particular kind of special geometry, which we call affine special para-Kahler geometry. We give a precise definition and develop the mathematical theory of such manifolds. The relation to the affine special Kahler manifolds appearing in Minkowskian N=2 supersymmetry is discussed. Starting from the general 5-dimensional vector multiplet action we consider dimensional reduction over time and space in parallel, providing a dictionary between the resulting Euclidean and Minkowskian theories. Then we reanalyze supersymmetry in four dimensions and find that any (para-)holomorphic prepotential defines a supersymmetric Lagrangian, provided that we add a specific four-fermion term, which cannot be obtained by dimensional reduction. We show that the Euclidean action and supersymmetry transformations, when written in terms of para-holomorphic coordinates, take exactly the same form as their Minkowskian counterparts. The appearance of a para-complex and complex structure in the Euclidean and Minkowskian theory, respectively, is traced back to properties of the underlying R-symmetry groups. Finally, we indicate how our work will be extended to other types of multiplets and to supergravity in the future and explain the relevance of this project for the study of instantons, solitons and cosmological solutions in supergravity and M-theory.Comment: 74 page

Developing European conservation and mitigation tools for pollination services: approaches of the STEP (Status and Trends of European Pollinators) project

Pollinating insects form a key component of European biodiversity, and provide a vital ecosystem service to crops and wild plants. There is growing evidence of declines in both wild and domesticated pollinators, and parallel declines in plants relying upon them. The STEP project (Status and Trends of European Pollinators, 2010-2015, www.stepproject.net) is documenting critical elements in the nature and extent of these declines, examining key functional traits associated with pollination deficits, and developing a Red List for some European pollinator groups. Together these activities are laying the groundwork for future pollinator monitoring programmes. STEP is also assessing the relative importance of potential drivers of pollinator declines, including climate change, habitat loss and fragmentation, agrochemicals, pathogens, alien species, light pollution, and their interactions. We are measuring the ecological and economic impacts of declining pollinator services and floral resources, including effects on wild plant populations, crop production and human nutrition. STEP is reviewing existing and potential mitigation options, and providing novel tests of their effectiveness across Europe. Our work is building upon existing and newly developed datasets and models, complemented by spatially-replicated campaigns of field research to fill gaps in current knowledge. Findings are being integrated into a policy-relevant framework to create evidence-based decision support tools. STEP is establishing communication links to a wide range of stakeholders across Europe and beyond, including policy makers, beekeepers, farmers, academics and the general public. Taken together, the STEP research programme aims to improve our understanding of the nature, causes, consequences and potential mitigation of declines in pollination services at local, national, continental and global scales

Inhibition of PFKFB3 Hampers the Progression of Atherosclerosis and Promotes Plaque Stability

Aims: 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase (PFKFB)3-mediated glycolysis is pivotal in driving macrophage- and endothelial cell activation and thereby inflammation. Once activated, these cells play a crucial role in the progression of atherosclerosis. Here, we analyzed the expression of PFKFB3 in human atherosclerotic lesions and investigated the therapeutic potential of pharmacological inhibition of PFKFB3 in experimental atherosclerosis by using the glycolytic inhibitor PFK158. Methods and Results: PFKFB3 expression was higher in vulnerable human atheromatous carotid plaques when compared to stable fibrous plaques and predominantly expressed in plaque macrophages and endothelial cells. Analysis of advanced plaques of human coronary arteries revealed a positive correlation of PFKFB3 expression with necrotic core area. To further investigate the role of PFKFB3 in atherosclerotic disease progression, we treated 6–8 weeks old male Ldlr–/– mice. These mice were fed a high cholesterol diet for 13 weeks, of which they were treated for 5 weeks with the glycolytic inhibitor PFK158 to block PFKFB3 activity. The incidence of fibrous cap atheroma (advanced plaques) was reduced in PFK158-treated mice. Plaque phenotype altered markedly as both necrotic core area and intraplaque apoptosis decreased. This coincided with thickening of the fibrous cap and increased plaque stability after PFK158 treatment. Concomitantly, we observed a decrease in glycolysis in peripheral blood mononuclear cells compared to the untreated group, which alludes that changes in the intracellular metabolism of monocyte and macrophages is advantageous for plaque stabilization. Conclusion: High PFKFB3 expression is associated with vulnerable atheromatous human carotid and coronary plaques. In mice, high PFKFB3 expression is also associated with a vulnerable plaque phenotype, whereas inhibition of PFKFB3 activity leads to plaque stabilization. This data implies that inhibition of inducible glycolysis may reduce inflammation, which has the ability to subsequently attenuate atherogenesis