A Study on the Light Curves of GRBs

It is generally believed that the complexity and variability of the light curves of gamma-ray bursts (GRBs) are caused by the internal shocks, which would occur when a rapid shell catches up a slower one and collides with it. The electrons in the shock layer are heated by the shocks and radiate via the mechanisms of synchrotron radiation and inverse Compton scattering. Based on relativistic kinematics, a relation between the photon number of the emission from the rapidly moving shock layer and the number of the photons received by an observer is derived. Then, employing the angular spreading of the internal shock emission, the curve equation and profile of a single pulse are obtained, and the shape is typically in the shape of a fast rise and exponential decline. Furthermore, by using the model of the successive collisions of multiple shells under the condition of reasonable parameters, the observed light curves are fitted with a rather good effect. Therefore, by this means, more different types of light curves of GRBs can be explained.     

Meteoroid Stream Searching: The Use of the Vectorial Elements

In this initial study, we propose a new distance function D _V involving heliocentric vectorial orbital elements. The function measures differences between: the orbital energies, the angular momentums vectors and the Laplace vectors. In comparison with the widely used D _SH criterion of Southworth and Hawkins, D _D criterion of Drummond and their hybrid D _H by Jopek, the new function contains one invariant with respect to the principal secular perturbation: the orbital energy. The new function proved to be useful in the classification amongst the IAU2003 meteoroids which we searched for streams by D _V function and also using D _SH and D _N -function given by Valsecchi et al. For major streams, the results agree very well. For minor, and near-ecliptical streams the results sometimes differ markedly.     

Object classification at the Nearby Supernova Factory

We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Automated probabilistic classification of transients and variables

There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow‐up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow‐up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow‐up observations, and discriminated or directed follow‐up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Sensitive response of desert vegetation to moisture change based on a near-annual resolution pollen record from Gahai Lake in the Qaidam Basin, northwest China

We present a 50-year pollen record at near-annual resolution from Gahai Lake in the Qaidam Basin on the northeastern Tibetan Plateau. Chronology of a 22-cm short core was established by ²¹⁰Pb and ¹³⁷Cs analysis. The pollen results at 0.5 cm intervals show large changes in Artemisia/Chenopodiaceae (A/C) ratios from < 0.2 to 0.95 in the last 50 years. High (low) A/C ratios represent increase (decrease) in steppe pollen production, which correspond to high (low) relative humidity observed at nearby Delingha weather station. On the basis of good correspondence with instrumental records and carbonate content from Gahai Lake, we conclude that A/C ratio is sensitive to moisture change and can be a very useful index in reconstructing paleoclimate of arid regions. Comparison with pollen and snow accumulation data from Dunde ice core suggests that effective moisture at low and high elevations shows the opposite relationship when mountain precipitation was extremely high, possibly due to topography-induced uplifting and subsiding air dynamics.     

Sinusoidal variables from the Tycho Epoch Photometry Annex

The Tycho Epoch Photometry Annex A, a data base of photometry of more than 34 000 bright stars, has been searched for periodic variable stars with approximately sinusoidal light curves. Advantage was taken of special properties of the observing programme (photometry in two wavebands, availability of repeated measurements) to use simple but efficient variable selection criteria. Details of 70 strong candidate variables are presented.     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.