Circumstellar emission in Be/X-ray binaries of the Magellanic Clouds and the Milky Way

Aims. We study the optical and near-infrared colour excesses produced by circumstellar emission in a sample of Be/X-ray binaries. Our main goals are exploring whether previously published relations, valid for isolated Be stars, are applicable to Be/X-ray binaries and computing the distance to these systems after correcting for the effects of the circumstellar contamination. Methods. Simultaneous UBVRI photometry and spectra in the 3500−7000 Å spectral range were obtained for 11 optical counterparts to Be/X-ray binaries in the LMC, 5 in the SMC and 12 in the Milky Way. As a measure of the amount of circumstellar emission we used the Hα equivalent width corrected for photospheric absorption. Results. We find a linear relationship between the strength of the Hα emission line and the component of E(B − V) originating from the circumstellar disk. This relationship is valid for stars with emission lines weaker than EW ≈ −15   Å. Beyond this point, the circumstellar contribution to E(B − V) saturates at a value ≈0.17   mag. A similar relationship is found for the (V − I) near infrared colour excess, albeit with a steeper slope and saturation level. The circumstellar excess in (B − V) is found to be about five times higher for Be/X-ray binaries than for isolated Be stars with the same equivalent width EW(Hα), implying significant differences in the physical properties of their circumstellar envelopes. The distance to Be/X-ray binaries (with non-shell Be star companions) can only be correctly estimated by taking into account the excess emission in the V band produced by free-free and free-bound transitions in the circumstellar envelope. We provide a simple method to determine the distances that includes this effect.

A new survey of cool supergiants in the Magellanic Clouds

Aims. In this study we conduct a pilot program aimed at the red supergiant population of the Magellanic Clouds. We intend to extend the current known sample to the unexplored low end of the brightness distribution of these stars, building a more representative dataset with which to extrapolate their behaviour to other Galactic and extra-galactic environments. Methods. We select candidates using only near infrared photometry, and with medium resolution multi-object spectroscopy, we perform spectral classification and derive their line-of-sight velocities, confirming the nature of the candidates and their membership in the clouds. Results. Around two hundred new red supergiants have been detected, hinting at a yet to be observed large population. Using near- and mid-infrared photometry we study the brightness distribution of these stars, the onset of mass-loss, and the effect of dust in their atmospheres. Based on this sample, new a priori classification criteria are investigated, combining mid- and near-infrared photometry to improve the observational efficiency of similar programs to this.

Magnetars: Properties, Origin and Evolution

Magnetars are neutron stars in which a strong magnetic field is the main energy source. About two dozens of magnetars, plus several candidates, are currently known in our Galaxy and in the Magellanic Clouds. They appear as highly variable X-ray sources and, in some cases, also as radio and/or optical pulsars. Their spin periods (2–12 s) and spin-down rates (∼10−13–10−10 s s−1) indicate external dipole fields of ∼1013−15 G, and there is evidence that even stronger magnetic fields are present inside the star and in non-dipolar magnetospheric components. Here we review the observed properties of the persistent emission from magnetars, discuss the main models proposed to explain the origin of their magnetic field and present recent developments in the study of their evolution and connection with other classes of neutron stars.

A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

Discontinuity spacing analysis in rock masses using 3D point clouds

The complete characterization of rock masses implies the acquisition of information of both, the materials which compose the rock mass and the discontinuities which divide the outcrop. Recent advances in the use of remote sensing techniques – such as Light Detection and Ranging (LiDAR) – allow the accurate and dense acquisition of 3D information that can be used for the characterization of discontinuities. This work presents a novel methodology which allows the calculation of the normal spacing of persistent and non-persistent discontinuity sets using 3D point cloud datasets considering the three dimensional relationships between clusters. This approach requires that the 3D dataset has been previously classified. This implies that discontinuity sets are previously extracted, every single point is labeled with its corresponding discontinuity set and every exposed planar surface is analytically calculated. Then, for each discontinuity set the method calculates the normal spacing between an exposed plane and its nearest one considering 3D space relationship. This link between planes is obtained calculating for every point its nearest point member of the same discontinuity set, which provides its nearest plane. This allows calculating the normal spacing for every plane. Finally, the normal spacing is calculated as the mean value of all the normal spacings for each discontinuity set. The methodology is validated through three cases of study using synthetic data and 3D laser scanning datasets. The first case illustrates the fundamentals and the performance of the proposed methodology. The second and the third cases of study correspond to two rock slopes for which datasets were acquired using a 3D laser scanner. The second case study has shown that results obtained from the traditional and the proposed approaches are reasonably similar. Nevertheless, a discrepancy between both approaches has been found when the exposed planes members of a discontinuity set were hard to identify and when the planes pairing was difficult to establish during the fieldwork campaign. The third case study also has evidenced that when the number of identified exposed planes is high, the calculated normal spacing using the proposed approach is minor than those using the traditional approach.

Datasets and results of the research [Discontinuity spacing analysis in rock masses using 3D point clouds]

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Characterization of rock slopes through slope mass rating using 3D point clouds

Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.