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 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.