Aims. We aim to locate previously unknown stellar clusters using the VISTA variables in the Vía Láctea Survey (VVV) catalogue data. Methods. The method fits a mixture model of Gaussian densities and background noise and uses the expectation maximization algorithm to pre-filtered near-infrared survey stellar catalogue data; it was developed by the authors for the UKIDSS Galactic Plane Survey (GPS). Results. The search located 88 previously unknown candidates, most of which are embedded stellar cluster candidates, and 39 previously unknown sites of star formation in the 562 deg 2 covered by VVV in the Galactic bulge and the southern disk.
Context. Data mining techniques must be developed and applied to analyse the large public data bases containing hundreds to thousands of millions entries. Aims. We develop methods for locating previously unknown stellar clusters from the UKIDSS Galactic Plane Survey (GPS) catalogue data. Methods. The cluster candidates are computationally searched from pre-filtered catalogue data using a method that fits a mixture model of Gaussian densities and background noise using the expectation maximization algorithm. The catalogue data contains a significant number of false sources clustered around bright stars. A large fraction of these artefacts were automatically filtered out before or during the cluster search. The UKIDSS data reduction pipeline tends to classify marginally resolved stellar pairs and objects seen against variable surface brightness as extended objects (or "galaxies" in the archive parlance). 10% or 66 × 10 6 of the sources in the UKIDSS GPS catalogue brighter than 17 m in the K band are classified as "galaxies". Young embedded clusters create variable NIR surface brightness because the gas/dust clouds in which they were formed scatters the light from the cluster members. Such clusters appear therefore as clusters of "galaxies" in the catalogue and can be found using only a subset of the catalogue data. The detected "galaxy clusters" were finally screened visually to eliminate the remaining false detections due to data artefacts. Besides the embedded clusters the search also located locations of non clustered embedded star formation. Results. The search covered an area of 1302 deg 2 and 137 previously unknown cluster candidates and 30 previously unknown sites of star formation were found.
Aims. We present an automated system called neoranger that regularly computes asteroid-Earth impact probabilities for objects on the Minor Planet Center's (MPC) Near-Earth-Object Confirmation Page (NEOCP) and sends out alerts of imminent impactors to registered users. In addition to potential Earth-impacting objects, neoranger also monitors for other types of interesting objects such as Earth's natural temporarily-captured satellites. Methods. The system monitors the NEOCP for objects with new data and solves, for each object, the orbital inverse problem, which results in a sample of orbits that describes the, typically highly-nonlinear, orbital-element probability density function (PDF). The PDF is propagated forward in time for seven days and the impact probability is computed as the weighted fraction of the sample orbits that impact the Earth. Results. The system correctly predicts the then-imminent impacts of 2008 TC 3 and 2014 AA based on the first data sets available. Using the same code and configuration we find that the impact probabilities for objects typically on the NEOCP, based on eight weeks of continuous operations, are always less than one in ten million, whereas simulated and real Earth-impacting asteroids always have an impact probability greater than 10% based on the first two tracklets available.
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