Long Term Spectral Evolution of Tidal Disruption Candidates Selected by Strong Coronal Lines

We present results of follow-up optical spectroscopic observations of seven rare, extreme coronal line emitting galaxies reported by Wang et al. (2012) with Multi-Mirror Telescope (MMT). Large variations in coronal lines are found in four objects, making them strong candidates of tidal disruption events (TDE). For the four TDE candidates, all the coronal lines with ionization status higher than [Fe VII] disappear within 5-9 years. The [Fe VII] faded by a factor of about five in one object (J0952+2143) within 4 years, whereas emerged in other two without them previously. A strong increment in the [O III] flux is observed, shifting the line ratios towards the loci of active galactic nucleus on the BPT diagrams. Surprisingly, we detect a non-canonical [O III]5007/[O III]4959 2 in two objects, indicating a large column density of O$^{2+}$ and thus probably optical thick gas. This also requires a very large ionization parameter and relatively soft ionizing spectral energy distribution (e.g. blackbody with $T < 5\times 10^4$ K). Our observations can be explained as echoing of a strong ultraviolet to soft X-ray flare caused by tidal disruption events, on molecular clouds in the inner parsecs of the galactic nuclei. Re-analyzing the SDSS spectra reveals double-peaked or strongly blue-shouldered broad lines in three of the objects, which disappeared in the MMT spectra in two objects, and faded by a factor of ten in 8 years in the remaining object with a decrease in both the line width and centroid offset. We interpret these broad lines as arising from decelerating biconical outflows. Our results demonstrate that the signatures of echoing can persist for as long as ten years, and can be used to probe the gas environment in the quiescent galactic nuclei.Comment: 30 Pages, 10 Figures, 2 Tables, Accepted for Publication in Ap

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level