A formal method for identifying distinct states of variability in time-varying sources: SgrA* as an example

Continuously time variable sources are often characterized by their power spectral density and flux distribution. These quantities can undergo dramatic changes over time if the underlying physical processes change. However, some changes can be subtle and not distinguishable using standard statistical approaches. Here, we report a methodology that aims to identify distinct but similar states of time variability. We apply this method to the Galactic supermassive black hole, where 2.2 um flux is observed from a source associated with SgrA*, and where two distinct states have recently been suggested. Our approach is taken from mathematical finance and works with conditional flux density distributions that depend on the previous flux value. The discrete, unobserved (hidden) state variable is modeled as a stochastic process and the transition probabilities are inferred from the flux density time series. Using the most comprehensive data set to date, in which all Keck and a majority of the publicly available VLT data have been merged, we show that SgrA* is sufficiently described by a single intrinsic state. However the observed flux densities exhibit two states: a noise-dominated and a source-dominated one. Our methodology reported here will prove extremely useful to assess the effects of the putative gas cloud G2 that is on its way toward the black hole and might create a new state of variability.Comment: Submitted to ApJ; 33 pages, 4 figures; comments welcom

On Markovian solutions to Markov Chain BSDEs

We study (backward) stochastic differential equations with noise coming from a finite state Markov chain. We show that, for the solutions of these equations to be `Markovian', in the sense that they are deterministic functions of the state of the underlying chain, the integrand must be of a specific form. This allows us to connect these equations to coupled systems of ODEs, and hence to give fast numerical methods for the evaluation of Markov-Chain BSDEs

Emission lines in the atmosphere of the irradiated brown dwarf WD0137−349B

ESL acknowledges the support of STFC studentship. SLC acknowledges support from the University of Leicester College of Science and Engineering. CH highlights the financial support of the European community under the FP7 ERC starting grant 257431. This work was supported by the Science and Technology Facilities Council [ST/M001040/1].We present new optical and near-infrared spectra of WD0137−349; a close white dwarf–brown dwarf non-interacting binary system with a period of ≈114 min. We have confirmed the presence of H α emission and discovered He, Na, Mg, Si, K, Ca, Ti and Fe emission lines originating from the brown-dwarf atmosphere. This is the first brown-dwarf atmosphere to have been observed to exhibit metal emission lines as a direct result of intense irradiation. The equivalent widths of many of these lines show a significant difference between the day-side and night-side of the brown dwarf. This is likely an indication that efficient heat redistribution may not be happening on this object, in agreement with models of hot Jupiter atmospheres. The H α line strength variation shows a strong phase dependency as does the width. We have simulated the Ca ii emission lines using a model that includes the brown-dwarf Roche geometry and limb darkening, and we estimate the mass ratio of the system to be 0.135 ± 0.004. We also apply a gas-phase equilibrium code using a prescribed drift-phoenix model to examine how the chemical composition of the brown-dwarf upper atmosphere would change given an outward temperature increase, and discuss the possibility that this would induce a chromosphere above the brown-dwarf atmosphere.Publisher PDFPeer reviewe

Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction

This paper presents preliminary results for the classification of Pap smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textual features. We outline a method of nuclear segmentation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modified form of the GLCM are extracted over several angle and distance measures. Linear Discriminant Analysis is preformed on these features to reduce the dimensionality of the feature space, and a classifier with hyper quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassification rate of 3.3% on a data set of 61 cells