Signal detection for spectroscopy and polarimetry

The analysis of high spectral resolution spectroscopic and spectropolarimetric observations constitute a very powerful way of inferring the dynamical, thermodynamical, and magnetic properties of distant objects. However, these techniques are photon-starving, making it difficult to use them for all purposes. One of the problems commonly found is just detecting the presence of a signal that is buried on the noise at the wavelength of some interesting spectral feature. This is specially relevant for spectropolarimetric observations because typically, only a small fraction of the received light is polarized. We present in this note a Bayesian technique for the detection of spectropolarimetric signals. The technique is based on the application of the non-parametric relevance vector machine to the observations, which allows us to compute the evidence for the presence of the signal and compute the more probable signal. The method would be suited for analyzing data from experimental instruments onboard space missions and rockets aiming at detecting spectropolarimetric signals in unexplored regions of the spectrum such as the Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) sounding rocket experiment.Comment: 10 pages, 5 figures, accepted for publication in A&

Weakly Supervised Learning of Objects, Attributes and Their Associations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

The dust covering factor in active galactic nuclei

The primary source of emission of active galactic nuclei (AGNs), the accretion disc, is surrounded by an optically and geometrically thick dusty structure ('the so-called dusty torus'). The infrared radiation emitted by the dust is nothing but a reprocessed fraction of the accretion disc emission, so the ratio of the torus to the AGN luminosity (L-torus/L-AGN) should corresponds to the fraction of the sky obscured by dust, i.e. the covering factor. We undertook a critical investigation of the L-torus/L-AGN as the dust covering factor proxy. Using state-of-the-art 3D Monte Carlo radiative transfer code, we calculated a grid of spectral energy distributions (SEDs) emitted by the clumpy two-phase dusty structure. With this grid of SEDs, we studied the relation between L-torus/L-AGN and the dust covering factor for different parameters of the torus. We found that in the case of type 1 AGNs the torus anisotropy makes L-torus/L-AGN underestimate low covering factors and overestimate high covering factors. In type 2 AGNs L-torus/L-AGN always underestimates covering factors. Our results provide a novel easy-to-use method to account for anisotropy and obtain correct covering factors. Using two samples from the literature, we demonstrated the importance of our result for inferring the obscured AGN fraction. We found that after the anisotropy is properly accounted for, the dust covering factors show very weak dependence on L-AGN, with values in the range of approximate to 0.6-0.7. Our results also suggest a higher fraction of obscured AGNs at high luminosities than those found by X-ray surveys, in part owing to the presence of a Compton-thick AGN population predicted by population synthesis models

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

Nonparametric Feature Extraction from Dendrograms

We propose feature extraction from dendrograms in a nonparametric way. The Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the sequential combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies