Multiple populations in Omega Centauri: a cluster analysis of spectroscopic data

Omega Cen is composed of several stellar populations. Their history might allow us to reconstruct the evolution of this complex object. We performed a statistical cluster analysis on the large data set provided by Johnson and Pilachowski (2010). Stars in Omega Cen divide into three main groups. The metal-poor group includes about a third of the total. It shows a moderate O-Na anticorrelation, and similarly to other clusters, the O-poor second generation stars are more centrally concentrated than the O-rich first generation ones. This whole population is La-poor, with a pattern of abundances for n-capture elements which is very close to a scaled r-process one. The metal-intermediate group includes the majority of the cluster stars. This is a much more complex population, with an internal spread in the abundances of most elements. It shows an extreme O-Na anticorrelation, with a very numerous population of extremely O-poor and He-rich second generation stars. This second generation is very centrally concentrated. This whole population is La-rich, with a pattern of the abundances of n-capture elements that shows a strong contribution by the s-process. The spread in metallicity within this metal-intermediate population is not very large, and we might attribute it either to non uniformities of an originally very extended star forming region, or to some ability to retain a fraction of the ejecta of the core collapse SNe that exploded first, or both. As previously noticed, the metal-rich group has an Na-O correlation, rather than anticorrelation. There is evidence for the contribution of both massive stars ending their life as core-collapse SNe, and intermediate/low mass stars, producing the s-capture elements. Kinematics of this population suggests that it formed within the cluster rather than being accreted.Comment: Accepted for publication in Astronomy and Astrophysic

The role of planets in shaping planetary nebulae

In 1997 Soker laid out a framework for understanding the formation and shaping of planetary nebulae (PN). Starting from the assumption that non-spherical PN cannot be formed by single stars, he linked PN morphologies to the binary mechanisms that may have formed them, basing these connections almost entirely on observational arguments. In light of the last decade of discovery in the field of PN, we revise this framework, which, although simplistic, can still serve as a benchmark against which to test theories of PN origin and shaping. Within the framework, we revisit the role of planets in shaping PN. Soker invoked a planetary role in shaping PN because there are not enough close binaries to shape the large fraction of non-spherical PN. In this paper we adopt a model whereby only ~20% of all 1-8 solar mass stars make a PN. This reduces the need for planetary shaping. Through a propagation of percentages argument, and starting from the assumption that planets can only shape mildly elliptical PN, we conclude, like in Soker, that ~20% of all PN were shaped via planetary and other substellar interactions but we add that this corresponds to only ~5% of all 1-8 solar mass stars. This may be in line with findings of planets around main sequence stars. PN shaping by planets is made plausible by the recent discovery of planets that have survived interactions with red giant branch (RGB) stars. Finally, we conclude that of the ~80% of 1-8 solar mass stars that do not make a PN, about one quarter do not even ascend the AGB due to interactions with stellar and substellar companions, while three quarters ascend the AGB but do not make a PN. Once these stars leave the AGB they evolve normally and can be confused with post-RGB, extreme horizontal branch stars. We propose tests to identify them.Comment: 23 pages, accepted by PAS

The Na-O anticorrelation in horizontal branch stars. IV. M22

We obtained high-resolution spectra for 94 candidate stars belonging to the HB of M22 with FLAMES. The HB stars we observed span a restricted temperature range (7,800<Teff<11,000 K), where about 60% of the HB stars of M22 are. Within our sample, we can distinguish three groups of stars segregated (though contiguous) in colours: Group 1 (49 stars) is metal-poor, N-normal, Na-poor and O-rich with abundances that match those determined for the primordial group of RGB stars from previous studies. Group 2 (23 stars) is still metal-poor, but it is N- and Na-rich, though only very mildly depleted in O. We can identify this intermediate group as the progeny of the metal-poor RGB stars that occupy an intermediate location along the Na-O anti-correlation. The third group (20 stars) is metal-rich, Na-rich, and O-rich and likely corresponds to the most O-rich component of the previously found metal-rich RGB population. We did not observe any severely O-depleted stars and we think that the progeny of these stars falls on the hotter part of the HB. The metal-rich population is also over-abundant in Sr, in agreement with results for corresponding RGB and SGB stars. However, we do not find any significant variation in the ratio between the sum of N and O abundances to Fe. There is some evidence of an enhancement of He content for Groups 2 and 3 stars (Y=0.338\pm 0.014\pm 0.05). Our results agree with the proposition that chemical composition drives the location of stars along the HB of a GC. Furthermore, we found a number of fast rotators. They are concentrated in a restricted temperature range along the HB of M22.Comment: Accepted for publication on Astronomy and Astrophysics. 23 pages, 21 figure

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling