Candidate Tidal Dwarf Galaxies in the Compact Group CG J1720-67.8

This is the second part of a detailed study of the ultracompact group CG J1720-67.8: in the first part we have focused the attention on the three main galaxies of the group and we have identified a number of candidate tidal dwarf galaxies (TDGs). Here we concentrate on these candidate TDGs. Absolute photometry of these objects in BVRJHKs bands confirms their relatively blue colors, as we already expected from the inspection of optical and near-infrared color maps and from the presence of emission-lines in the optical spectra. The physical conditions in such candidate TDGs are investigated through the application of photoionization models, while the optical colors are compared with grids of spectrophotometric evolutionary synthesis models from the literature. Although from our data self-gravitation cannot be proved for these objects, their general properties are consistent with those of other TDG candidates. Additionally we present the photometry of a few ``knots'' detected in the immediate surroundings of CG J1720-67.8 and consider the possibility that these objects might belong to a dwarf population associated with the compact group.Comment: Accepted for publication in the Astrophysical Journa

Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved

Morphological shape generation through user-controlled group metamorphosis

Morphological shape design is interpreted in this paper as a search for new shapes from a particular application domain represented by a set of selected shape instances. This paper proposes a new foundation for morphological shape design and generation. In contrast to existing generative procedures, an approach based on a user-controlled metamorphosis between functionally based shape models is presented. A formulation of the pairwise metamorphosis is proposed with a variety of functions described for the stages of deformation, morphing and offsetting. This formulation is then extended to the metamorphosis between groups of shapes with user-defined, dynamically correlated and weighted feature elements. A practical system was implemented in the form of plugin to Maya and tested by an industrial designer on a group of representative shapes from a particular domain. © 2013 Elsevier Ltd

Multi-frequency study of DEM L299 in the Large Magellanic Cloud

We have studied the HII region DEM L299 in the Large Magellanic Cloud to understand its physical characteristics and morphology in different wavelengths. We performed a spectral analysis of archived XMM-Newton EPIC data and studied the morphology of DEM L299 in X-ray, optical, and radio wavelengths. We used H alpha, [SII], and [OIII] data from the Magellanic Cloud Emission Line Survey and radio 21 cm line data from the Australia Telescope Compact Array (ATCA) and the Parkes telescope, and radio continuum data from ATCA and the Molonglo Synthesis Telescope. Our morphological studies imply that, in addition to the supernova remnant SNR B0543-68.9 reported in previous studies, a superbubble also overlaps the SNR in projection. The position of the SNR is clearly defined through the [SII]/H alpha flux ratio image. Moreover, the optical images show a shell-like structure that is located farther to the north and is filled with diffuse X-ray emission, which again indicates the superbubble. Radio 21 cm line data show a shell around both objects. Radio continuum data show diffuse emission at the position of DEM L299, which appears clearly distinguished from the HII region N 164 that lies south-west of it. We determined the spectral index of SNR B0543-68.9 to be alpha=-0.34, which indicates the dominance of thermal emission and therefore a rather mature SNR. We determined the basic properties of the diffuse X-ray emission for the SNR, the superbubble, and a possible blowout region of the bubble, as suggested by the optical and X-ray data. We obtained an age of 8.9 (3.5-18.1) kyr for the SNR and a temperature of 0.64 (0.44-1.37) keV for the hot gas inside the SNR, and a temperature of the hot gas inside the superbubble of 0.74 (0.44-1.1) keV. We conclude that DEM L299 consists of a superposition of SNR B0543-68.9 and a superbubble, which we identified based on optical data.Comment: Accepted for publication in Astronomy and Astrophysics. 17 pages, 16 figure

Polygonal Building Segmentation by Frame Field Learning

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.Comment: CVPR 2021 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2021, Pittsburg / Virtual, United State