Related papers: Finding rare objects and building pure samples: Pr…
We present a catalog of 100,563 unresolved, UV-excess (UVX) quasar candidates to g=21 from 2099 deg^2 of the Sloan Digital Sky Survey (SDSS) Data Release One (DR1) imaging data. Existing spectra of 22,737 sources reveals that 22,191 (97.6%)…
The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two…
We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm (Richards et al.…
High redshift quasars (HZQs) with redshifts of z >~ 6 are so rare that any photometrically-selected sample of sources with HZQ-like colours is likely to be dominated by Galactic stars and brown dwarfs scattered from the stellar locus. It is…
We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift…
Tens of millions of new variable objects are expected to be identified in over a billion time series from the Gaia mission. Crossmatching known variable sources with those from Gaia is crucial to incorporate current knowledge, understand…
We describe methods designed to determine the astrophysical parameters of quasars based on spectra coming from the red and blue spectrophotometers of the Gaia satellite. These methods principally rely on two already published algorithms…
We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of >65000 color templates. The method aims for extracting the information content of object colors in a statistically…
Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We apply the machine learning classification to 85,613,922 objects in the…
The nearest stars provide a fundamental constraint for our understanding of stellar physics and the Galaxy. The nearby sample serves as an anchor where all objects can be seen and understood with precise data. This work is triggered by the…
Aims:The Gaia astrometric survey mission will, as a consequence of its scanning law, obtain low resolution optical (330-1000 nm) spectrophotometry of several million unresolved galaxies brighter than V=22. We present the first steps in a…
Wide-field photometric surveys enable searches of rare yet interesting objects, such as strongly lensed quasars or quasars with a bright host galaxy. Past searches for lensed quasars based on their optical and near infrared properties have…
The radiological characterization of contaminated elements (walls, grounds, objects) from nuclear facilities often suffers from a too small number of measurements. In order to determine risk prediction bounds on the level of contamination,…
Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…
We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The…
We consider the problem of weakly supervised object detection, where the training samples are annotated using only image-level labels that indicate the presence or absence of an object category. In order to model the uncertainty in the…
Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested…
We present an algorithm for classifying the nearby transient objects detected by the Gaia satellite. The algorithm will use the low-resolution spectra from the blue and red spectro-photometers on board of the satellite. Taking a Bayesian…
Gaia's exceptional resolution (FWHM $\sim$ 0.1$^{\prime\prime}$) allows identification and cataloguing of the multiple images of gravitationally lensed quasars. We investigate a sample of 49 known lensed quasars in the SDSS footprint, with…
Context. Large, high-dimensional astronomical surveys require efficient data analysis. Automatic fitting of lightcurve variability and machine learning may assist in identification of sources including candidate quasars. Aims. We aim to…