Evolution of the Fundamental Plane of 0.2<z<1.2 Early-type galaxies in the EGS

The Fundamental Plane relates the structural properties of early-type galaxies such as its surface brightness and effective radius with its dynamics. The study of its evolution has therefore important implications for models of galaxy formation and evolution. This work aims to identify signs of evolution of early-type galaxies through the study of parameter correlations using a sample of 135 field galaxies extracted from the Extended Groth Strip in the redshift range 0.2<z<1.2. Using DEEP2 data, we calculate the internal velocity dispersions by extracting the stellar kinematics from absorption line spectra, using a maximum penalized likelihood approach. Morphology was determined through visual classification using the V+I images of ACS. The structural parameters of these galaxies were obtained by fitting de Vaucouleurs stellar profiles to the ACS I-band images, using the GALFIT code. S\'ersic and bulge-to-disc decomposition models were also fitted to our sample of galaxies, and we found a good agreement in the Fundamental Plane derived from the three models. Assuming that effective radii and velocity dispersions do not evolve with redshift, we have found a brightening of 0.68 mag in the B-band and 0.52 mag in the g-band at =0.7. However, the scatter in the FP is reduced by half when we allow the FP slope to evolve, suggesting a different evolution of early-type galaxies according to their intrinsic properties. The study of the Kormendy relation shows the existence of a population of very compact (Re<2 Kpc) and bright galaxies (-21.5>Mg>-22.5), of which there are only a small fraction (0.4%) at z=0. The evolution of these compact objects is mainly caused by an increase in size that could be explained by the action of dry minor mergers, and this population is responsible for the evolution detected in the Fundamental Plane.Comment: Accepted for publication in A&A. 12 pages, 10 Figures, and 1 online tabl

MOSFIRE Spectroscopy of Quiescent Galaxies at 1.5 < z < 2.5. I - Evolution of Structural and Dynamical Properties

We present deep near-infrared spectra for a sample of 24 quiescent galaxies in the redshift range 1.5 < z < 2.5 obtained with the MOSFIRE spectrograph at the W. M. Keck Observatory. In conjunction with a similar dataset we obtained in the range 1 < z < 1.5 with the LRIS spectrograph, we analyze the kinematic and structural properties for 80 quiescent galaxies, the largest homogeneously-selected sample to date spanning 3 Gyr of early cosmic history. Analysis of our Keck spectra together with measurements derived from associated HST images reveals increasingly larger stellar velocity dispersions and smaller sizes to redshifts beyond z~2. By classifying our sample according to Sersic indices, we find that among disk-like systems the flatter ones show a higher dynamical to stellar mass ratio compared to their rounder counterparts which we interpret as evidence for a significant contribution of rotational motion. For this subset of disk-like systems, we estimate that V/sigma, the ratio of the circular velocity to the intrinsic velocity dispersion, is a factor of two larger than for present-day disky quiescent galaxies. We use the velocity dispersion measurements also to explore the redshift evolution of the dynamical to stellar mass ratio, and to measure for the first time the physical size growth rate of individual systems over two distinct redshift ranges, finding a faster evolution at earlier times. We discuss the physical origin of this time-dependent growth in size in the context of the associated reduction of the systematic rotation.Comment: Updated to match the published versio

Plane-extraction from depth-data using a Gaussian mixture regression model

We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroundings and to navigate in three dimensions. We propose to do this by fitting the data with a piecewise-linear Gaussian mixture regression model whose components are skewed over planes, making them flat in appearance rather than being ellipsoidal, by embedding an outlier-trimming process that is formally incorporated into the proposed expectation-maximization algorithm, and by selectively fusing contiguous, coplanar components. Part of our motivation is an attempt to estimate more accurate plane-extraction by allowing each model component to make use of all available data through probabilistic clustering. The algorithm is thoroughly evaluated against a standard benchmark and is shown to rank among the best of the existing state-of-the-art methods.Comment: 11 pages, 2 figures, 1 tabl

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers