Short term aperiodic variability of X-ray binaries: its origin and implications

In this review I briefly describe the latest advances in studies of aperiodic variability of accreting X-ray binaries and outline the model which currently describe the majority of observational appearances of variability of accreting sources in the best way. Then I concentrate on the case of luminous accreting neutron star binaries (in the soft/high spectral state), where study of variability of X-ray emission of sources allowed us to resolve long standing problem of disentangling the contribution of accretion disk and boundary/spreading layer components to the time average spectrum of sources. The obtained knowledge of the shape of the spectrum of the boundary layer allowed us to make estimates of the mass and radii of accreting neutron stars.Comment: 11 pages, 5 figures. Proceedings article of the conference "Cool Discs, Hot Flows: The Varying Faces of Accreting Compact Objects", Ed. M. Axelsson, AIP Conference Proceedings 105

Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars

We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multi-band digital surveys. In particular, we applied the MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) method to the optical data of the Sloan Digital Sky Survey - Data Release 10, investigating whether photometric data alone suffice to disentangle different classes of objects as they are defined in the SDSS spectroscopic classification. We discuss three groups of classification problems: (i) the simultaneous classification of galaxies, quasars and stars; (ii) the separation of stars from quasars; (iii) the separation of galaxies with normal spectral energy distribution from those with peculiar spectra, such as starburst or starforming galaxies and AGN. While confirming the difficulty of disentangling AGN from normal galaxies on a photometric basis only, MLPQNA proved to be quite effective in the three-class separation. In disentangling quasars from stars and galaxies, our method achieved an overall efficiency of 91.31% and a QSO class purity of ~95%. The resulting catalogue of candidate quasars/AGNs consists of ~3.6 million objects, of which about half a million are also flagged as robust candidates, and will be made available on CDS VizieR facility.Comment: Accepted for publication by MNRAS, 13 pages, 6 figure

Challenges in Disentangling Independent Factors of Variation

We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set. Our weak labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. To make use of weak labels we introduce an autoencoder model and train it through constraints on image pairs and triplets. We formally prove that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature. We call this issue the reference ambiguity. Moreover, we show the role of the feature dimensionality and adversarial training. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.Comment: Submitted to ICLR 201

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Disentangling instrumental broadening

A new procedure aiming at disentangling the instrumental profile broadening and the relevant X-ray powder diffraction (XRPD) profile shape is presented. The technique consists of three steps: denoising by means of wavelet transforms, background suppression by morphological functions and deblurring by a Lucy--Richardson damped deconvolution algorithm. Real XRPD intensity profiles of ceria samples are used to test the performances. Results show the robustness of the method and its capability of efficiently disentangling the instrumental broadening affecting the measurement of the intrinsic physical line profile. These features make the whole procedure an interesting and user-friendly tool for the pre-processing of XRPD data.Comment: 9 pages, 1 table, 1 figure; typos correcte