A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions, is fully automated and thus particularly well adapted to large cosmological surveys. The source code is available for download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method, we use a seeing limited near-infrared ($K_s$ band, $2,16\mu m$) sample observed with WIRCam at CFHT at a median redshift of $z\sim0.8$. The machine is trained with a simulated sample built from a local visually classified sample from the SDSS chosen in the high-redshift sample's rest-frame (i band, $0.77\mu m$) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters and other caracteristics of galaxies such as luminosity and redshift. We show that a qualitative separation in two main morphological types (late type and early type) can be obtained with an error lower than 20% up to the completeness limit of the sample ($KAB\sim 22$) which is more than 2 times better that what would be obtained with a classical C/A classification on the same sample and indeed comparable to space data. The method is optimized to solve a specific problem, offering an objective and automated estimate of errors that enables a straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution images are available on reques

Interpretable deep learning for guided structure-property explorations in photovoltaics

The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad

Urban detection, delimitation and morphology: comparative analysis of selective "megacities"

Postprint (published version

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

The zCOSMOS Redshift Survey: the role of environment and stellar mass in shaping the rise of the morphology-density relation from z~1

For more than two decades we have known that galaxy morphological segregation is present in the Local Universe. It is important to see how this relation evolves with cosmic time. To investigate how galaxy assembly took place with cosmic time, we explore the evolution of the morphology-density relation up to redshift z~1 using about 10000 galaxies drawn from the zCOSMOS Galaxy Redshift Survey. Taking advantage of accurate HST/ACS morphologies from the COSMOS survey, of the well-characterised zCOSMOS 3D environment, and of a large sample of galaxies with spectroscopic redshift, we want to study here the evolution of the morphology-density relation up to z~1 and its dependence on galaxy luminosity and stellar mass. The multi-wavelength coverage of the field also allows a first study of the galaxy morphological segregation dependence on colour. We further attempt to disentangle between processes that occurred early in the history of the Universe or late in the life of galaxies. The zCOSMOS field benefits of high-resolution imaging in the F814W filter from the Advanced Camera for Survey (ACS). We use standard morphology classifiers, optimised for being robust against band-shifting and surface brightness dimming, and a new, objective, and automated method to convert morphological parameters into early, spiral, and irregular types. We use about 10000 galaxies down to I_AB=22.5 with a spectroscopic sampling rate of 33% to characterise the environment of galaxies up to z~1 from the 100 kpc scales of galaxy groups up to the 100 Mpc scales of the cosmic web. ABRIDGEDComment: 23 pages, 12 figures, accepted for publication in Astronomy and Astrophysic

Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey II: Multiwavelength Classification

We describe the application of the `shapelet' linear decomposition of galaxy images to multi-wavelength morphological classification using the $u,g,r,i,$ and $z$-band images of 1519 galaxies from the Sloan Digital Sky Survey. We utilize elliptical shapelets to remove to first-order the effect of inclination on morphology. After decomposing the galaxies we perform a principal component analysis on the shapelet coefficients to reduce the dimensionality of the spectral morphological parameter space. We give a description of each of the first ten principal component's contribution to a galaxy's spectral morphology. We find that galaxies of different broad Hubble type separate cleanly in the principal component space. We apply a mixture of Gaussians model to the 2-dimensional space spanned by the first two principal components and use the results as a basis for classification. Using the mixture model, we separate galaxies into three classes and give a description of each class's physical and morphological properties. We find that the two dominant mixture model classes correspond to early and late type galaxies, respectively. The third class has, on average, a blue, extended core surrounded by a faint red halo, and typically exhibits some asymmetry. We compare our method to a simple cut on $u-r$ color and find the shapelet method to be superior in separating galaxies. Furthermore, we find evidence that the $u-r=2.22$ decision boundary may not be optimal for separation between early and late type galaxies, and suggest that the optimal cut may be $u-r \sim 2.4$.Comment: 42 pages, 18 figs, revised version in press at AJ. Some modification to the technique, more discussion, addition/deletion/modification of several figures, color figures have been added. A high resolution version may be obtained at http://bllac.as.arizona.edu/~bkelly/shapelets/shapelets_ugriz.ps.g