Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

Context. Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13-21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 μas yr-1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution

Gaia focused product release: radial velocity time series of long-period variables

Stars and planetary system

A new evaluation function for entropy-based feature selection from incomplete data

LNCS, volume 8444Classification is an important and practical tool which uses a model built on historical data to predict class labels for new arrival data. In the last few years, there have been many interesting studies on classification in data streams. However, most such studies assume that those data streams are relatively balanced and stable. Actually, skewed data streams (e.g., few positive but lots of negatives) are very important and typical, which appear in many real world applications. Concept drifts and skewed distributions, two common properties of data streams, make the task of learning in streams particularly difficult and the traditional data mining algorithms no longer work. In this paper, we propose a method (Selectively Re-train Approach Based on Clustering) which can deal with concept-drifting and skewed distribution simultaneously. We evaluate our algorithm on both synthetic and real data sets simulating skewed data streams. Empirical results show the proposed method yields better performance than the previous work.Wenhao Shu, Hong Shen, Yingpeng Sang, Yidong Li, and Jun W