Spectral Energy Distribution Mapping of Two Elliptical Galaxies on sub-kpc scales

We use high-resolution Herschel-PACS data of 2 nearby elliptical galaxies, IC1459 & NGC2768 to characterize their dust and stellar content. IC1459 & NGC2768 have an unusually large amount of dust for elliptical galaxies (1-3 x 10^5 Msun), this dust is also not distributed along the stellar content. Using data from GALEX (ultraviolet) to PACS (far-infrared), we analyze the spectral energy distribution (SED) of these galaxies with CIGALEMC as a function of the projected position, binning images in 7.2" pixels. From this analysis, we derive maps of SED parameters, such as the metallicity, the stellar mass, the fraction of young star and the dust mass. The larger amount of dust in FIR maps seems related in our model to a larger fraction of young stars which can reach up to 4% in the dustier area. The young stellar population is fitted as a recent (~ 0.5 Gyr) short burst of star formation for both galaxies. The metallicities, which are fairly large at the center of both galaxies, decrease with the radial distance with fairly steep gradient for elliptical galaxies.Comment: 14 pages, 26 figures, to be published in Ap

Gaussian Processes with Context-Supported Priors for Active Object Localization

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure

Informative Path Planning for Active Field Mapping under Localization Uncertainty

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation Letters (and IEEE International Conference on Robotics and Automation

Gridmapping the northern plains of Mars: Geomorphological, Radar and Water-Equivalent Hydrogen results from Arcadia Plantia

A project of mapping ice-related landforms was undertaken to understand the role of sub-surface ice in the northern plains. This work is the first continuous regional mapping from CTX (“ConTeXt Camera”, 6 m/pixel; Malin et al., 2007) imagery in Arcadia Planitia along a strip 300 km across stretching from 30°N to 80°N centred on the 170° West line of longitude. The distribution and morphotypes of these landforms were used to understand the permafrost cryolithology. The mantled and textured signatures occur almost ubiquitously between 35° N and 78° N and have a positive spatial correlation with inferred ice stability based on thermal modelling, neutron spectroscopy and radar data. The degradational features into the LDM (Latitude Dependent Mantle) include pits, scallops and 100 m polygons and provide supporting evidence for sub-surface ice and volatile loss between 35-70° N in Arcadia with the mantle between 70-78° N appearing much more intact. Pitted terrain appears to be much more pervasive in Arcadia than in Acidalia and Utopia suggesting that the Arcadia study area had more wide-spread near-surface sub-surface ice, and thus was more susceptible to pitting, or that the ice was less well-buried by sediments. Correlations with ice stability models suggest that lack of pits north of 65-70° N could indicate a relatively young age (~1Ma), however this could also be explained through regional variations in degradation rates. The deposition of the LDM is consistent with an airfall hypothesis however there appears to be substantial evidence for fluvial processes in southern Arcadia with older, underlying processes being equally dominant with the LDM and degradation thereof in shaping the landscape