Extended stellar systems in the solar neighborhood -- V. Discovery of coronae of nearby star clusters

In this paper, we present a novel view on the morphology and the dynamical state of 10 prominent, nearby ($\leq$ 500 pc), and young ($\sim$30-300 Myr) open star clusters with Gaia DR2: $\alpha\,$Per, Blanco 1, IC 2602, IC 2391, Messier 39, NGC 2451A, NGC 2516, NGC 2547, Platais 9, and the Pleiades. We introduce a pioneering member identification method that is informed by cluster bulk velocities and deconvolves the spatial distribution with a mixture of Gaussians. Our approach enables inferring the clusters' true spatial distribution by effectively filtering field star contaminants while at the same time mitigating the impact of positional errors along the line of sight. This first application of the method reveals the existence of vast stellar coronae, extending for $\gtrsim\,$100 pc and surrounding the, by comparison tiny and compact, cluster cores. The coronae and cores form intertwined, co-eval, and co-moving extended cluster populations, each encompassing tens of thousands of cubic parsec and stretching across tens of degrees on the sky. Our analysis shows that the coronae are gravitationally unbound but largely comprise the bulk of the populations' stellar mass. Most systems are in a highly dynamic state, showing evidence of expansion and sometimes simultaneous contraction along different spatial axes. The velocity field of the extended populations for the cluster cores appears asymmetric but is aligned along a spatial axis unique to each cluster. The overall spatial distribution and the kinematic signature of the populations are largely consistent with the differential rotation pattern of the Milky Way. This finding underlines the important role of global Galactic dynamics to the fate of stellar systems. Our results highlight the complexity of the Milky Way's open cluster population and call for a new perspective on the characterization and dynamical state of open clusters.Comment: published in Astronomy & Astrophysics (update 23.07.21: fixed citation

A graph based model for the detection of tidal channels using marked point processes

In this paper we propose a new method for the automatic extraction of tidal channels in digital terrain models (DTM) using a sampling approach based on marked point processes. In our model, the tidal channel system is represented by an undirected, acyclic graph. The graph is iteratively generated and fitted to the data using stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. The nodes of the graph represent junction points of the channel system and the edges straight line segments with a certain width in between. In each sampling step, the current configuration of nodes and edges is modified. The changes are accepted or rejected depending on the probability density function for the configuration which evaluates the conformity of the current status with a pre-defined model for tidal channels. In this model we favour high DTM gradient magnitudes at the edge borders and penalize a graph configuration consisting of non-connected components, overlapping segments and edges with atypical intersection angles. We present the method of our graph based model and show results for lidar data, which serve of a proof of concept of our approach.Ministry of Environment, Energy, and Climate ProtectionMinistry of Science and Culture of Lower Saxon

Contextual classification of point cloud data by exploiting individual 3d neigbourhoods

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification

Forest point processes for the automatic extraction of networks in raster data

International audienceIn this paper, we propose a new stochastic approach for the automatic detection of network structures in raster data. We represent a network as a set of trees with acyclic planar graphs. We embed this model in the probabilistic framework of spatial point processes and determine the most probable configuration of trees by stochastic sampling. That is, different configurations are constructed randomly by modifying the graph parameters and by adding or removing nodes and edges to/ from the current trees. Each configuration is evaluated based on the probabilities for these changes and an energy function describing the conformity with a predefined model. By using the Reversible jump Markov chain Monte Carlo sampler, an approximation of the global optimum of the energy function is iteratively reached. Although our main target application is the extraction of rivers and tidal channels in digital terrain models, experiments with other types of networks in images show the transferability to further applications. Qualitative and quantitative evaluations demonstrate the competitiveness of our approach with respect to existing algorithms

The star formation history of the Sco-Cen association: Coherent star formation patterns in space and time

We reconstruct the star formation history of the Sco-Cen OB association using a novel high-resolution age map of the region. We develop an approach to produce robust ages for Sco-Cen's recently identified 37 stellar clusters using the \texttt{SigMA} algorithm. The Sco-Cen star formation timeline reveals four periods of enhanced star formation activity, or bursts, remarkably separated by about 5 Myr. Of these, the second burst, which occurred 15 million years ago, is by far the dominant, and most of Sco-Cen's stars and clusters were in place by the end of this burst. The formation of stars and clusters in Sco-Cen is correlated, but not linearly, meaning that more stars were formed per cluster during the peak of star formation rate. Most of the clusters, which are large enough to have supernova precursors, were formed during the 15 Myr period. Star and cluster formation activity has been continuously declining since then. We have clear evidence that Sco-Cen formed from the inside out and contains 100-pc long correlated chains of contiguous clusters exhibiting well-defined age gradients, from massive older clusters to smaller young clusters. These observables suggest an important role for feedback in the formation of about half of Sco-Cen stars, although follow-up work is needed to quantify this statement. Finally, we confirm that the Upper-Sco age controversy discussed in the literature during the last decades is solved: the region toward Upper-Sco, a benchmark region for planet formation studies, contains not one but up to nine clusters spanning ages from 3 to 19 Myr.Comment: 19 pages, 14 figures, preliminary version of this work. Comments welcome. Soon to be submitted to A&

VISIONS: The VISTA Star Formation Atlas -- I. Survey overview

© The Authors 2023. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0).VISIONS is an ESO public survey of five nearby (d < 500 pc) star-forming molecular cloud complexes that are canonically associated with the constellations of Chamaeleon, Corona Australis, Lupus, Ophiuchus, and Orion. The survey was carried out with VISTA, using VIRCAM, and collected data in the near-infrared passbands J, H, and Ks. With a total on-sky exposure time of 49.4 h VISIONS covers an area of 650 deg$^2$, and it was designed to build an infrared legacy archive similar to that of 2MASS. Taking place between April 2017 and March 2022, the observations yielded approximately 1.15 million images, which comprise 19 TB of raw data. The observations are grouped into three different subsurveys: The wide subsurvey comprises shallow, large-scale observations and has visited the star-forming complexes six times over the course of its execution. The deep subsurvey of dedicated high-sensitivity observations has collected data on the areas with the largest amounts of dust extinction. The control subsurvey includes observations of areas of low-to-negligible dust extinction. Using this strategy, the VISIONS survey offers multi-epoch position measurements, is able to access deeply embedded objects, and provides a baseline for statistical comparisons and sample completeness. In particular, VISIONS is designed to measure the proper motions of point sources with a precision of 1 mas/yr or better, when complemented with data from VHS. Hence, VISIONS can provide proper motions for sources inaccessible to Gaia. VISIONS will enable addressing a range of topics, including the 3D distribution and motion of embedded stars and the nearby interstellar medium, the identification and characterization of young stellar objects, the formation and evolution of embedded stellar clusters and their initial mass function, as well as the characteristics of interstellar dust and the reddening law.Peer reviewe

Contextual Classification of Full Waveform Lidar Data in the Wadden Sea

The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3D point cloud. In Wadden Sea areas the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification we combine a Conditional Random Fields framework with a Random Forests approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilise a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighbouring points

VISIONS: the VISTA Star Formation Atlas

The VISIONS public survey provides large-scale, multi-epoch imaging of five nearby star-forming regions at sub-arcsecond resolution in the near-infrared. All data collected within the program and provided by the European Southern Observatory (ESO) science archive are processed with a custom end-to-end pipeline infrastructure to provide science-ready images and source catalogs. The data reduction environment has been specifically developed for the purpose of mitigating several shortcomings of the bona fide data products processed with software provided by the Cambridge Astronomical Survey Unit (CASU), such as spatially variable astrometric and photometric biases of up to 100 mas and 0.1 mag, respectively. At the same time, the resolution of co-added images is up to 20% higher compared to the same products from the CASU processing environment. Most pipeline modules are written in Python and make extensive use of C extension libraries for numeric computations, thereby simultaneously providing accessibility, robustness, and high performance. The astrometric calibration is performed relative to the Gaia reference frame, and fluxes are calibrated with respect to the source magnitudes provided in the Two Micron All Sky Survey (2MASS). For bright sources, absolute astrometric errors are typically on the order of 10–15 mas and fluxes are determined with sub-percent precision. Moreover, the calibration with respect to 2MASS photometry is largely free of color terms. The pipeline produces data that are compliant with the ESO Phase 3 regulations and furthermore provides curated source catalogs that are structured similarly to those provided by the 2MASS survey