Sublimation Temperature of Circumstellar Dust Particles and Its Importance for Dust Ring Formation

Dust particles in orbit around a star drift toward the central star by the Poynting-Robertson effect and pile up by sublimation. We analytically derive the pile-up magnitude, adopting a simple model for optical cross sections. As a result, we find that the sublimation temperature of drifting dust particles plays the most important role in the pile-up rather than their optical property does. Dust particles with high sublimation temperature form a significant dust ring, which could be found in the vicinity of the sun through in-situ spacecraft measurements. While the existence of such a ring in a debris disk could not be identified in the spectral energy distribution (SED), the size of a dust-free zone shapes the SED. Since we analytically obtain the location and temperature of sublimation, these analytical formulae are useful to find such sublimation evidences.Comment: 9 pages, 5 figures, to be published in Earth Planets Spac

Duality of Super D-brane Actions in General Type IIB Supergravity Background

We examine duality transformations of supersymmetric and $\kappa$-symmetric Dp-brane actions in a general type II supergravity background where in particular the dilaton and the axion are supposed to not be zero or a constant but a general superfield. Due to non-constant dilaton and axion, we can explicitly show that the dilaton and the axion as well as the two 2-form gauge potentials transform as doublets under the $SL(2,R)$ transformation from the point of view of the world-volume field theory.Comment: 32 pages, LaTex 2

Single-epoch supernova classification with deep convolutional neural networks

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.Comment: 7 pages, published as a workshop paper in ICDCS2017, in June 201

Refinement of a Depth Averaged Flow Model in Curved Channel in Generalized Curvilinear Coordinate System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A depth averaged model of open channel flows with a horseshoe vortex

River engineeringNumerical modelling in river engineerin