Kernel-phases for high-contrast detection beyond the resolution limit

The detection of high contrast companions at small angular separation appears feasible in conventional direct images using the self-calibration properties of interferometric observable quantities. In the high-Strehl regime, available from space borne observatories and using AO in the mid-infrared, quantities comparable to the closure-phase that are used with great success in non-redundant masking inteferometry, can be extracted from direct images, even taken with a redundant aperture. These new phase-noise immune observable quantities, called Kernel-phases, are determined a-priori from the knowledge of the geometry of the pupil only. Re-analysis of HST/NICMOS archive and other ground based AO images, using this new Kernel-phase algorithm, demonstrates the power of the method, and its ability to detect companions at the resolution limit and beyond.Comment: 7 pages, 4 figures, 2011 SPIE conference proceeding

Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching

Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers. The minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion. Based on our method, each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers. Experimental results show that our technique gives significantly higher performance compared to traditional methods including the dense random code and the sparse random code both in terms of accuracy and classification times. Moreover, our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to the One-Versus-One.Comment: 7 pages, 3 figure

Investigating Light Curve Modulation via Kernel Smoothing. I. Application to 53 fundamental mode and first-overtone Cepheids in the LMC

Recent studies have revealed a hitherto unknown complexity of Cepheid pulsation. We implement local kernel regression to search for both period and amplitude modulations simultaneously in continuous time and to investigate their detectability, and test this new method on 53 classical Cepheids from the OGLE-III catalog. We determine confidence intervals using parametric and non-parametric bootstrap sampling to estimate significance and investigate multi-periodicity using a modified pre-whitening approach that relies on time-dependent light curve parameters. We find a wide variety of period and amplitude modulations and confirm that first overtone pulsators are less stable than fundamental mode Cepheids. Significant temporal variations in period are more frequently detected than those in amplitude. We find a range of modulation intensities, suggesting that both amplitude and period modulations are ubiquitous among Cepheids. Over the 12-year baseline offered by OGLE-III, we find that period changes are often non-linear, sometimes cyclic, suggesting physical origins beyond secular evolution. Our method more efficiently detects modulations (period and amplitude) than conventional methods reliant on pre-whitening with constant light curve parameters and more accurately pre-whitens time series, removing spurious secondary peaks effectively.Comment: Re-submitted including revisions to Astronomy and Astrophysic