Classification Of Astronomical Objects Research Articles

Data smashing: uncovering lurking order in data.

From automatic speech recognition to discovering unusual stars, underlying almost all automated discovery tasks is the ability to compare and contrast data streams with each other, to identify connections and spot outliers. Despite the prevalence of data, however, automated methods are not keeping pace. A key bottleneck is that most data comparison algorithms today rely on a human expert to specify what 'features' of the data are relevant for comparison. Here, we propose a new principle for estimating the similarity between the sources of arbitrary data streams, using neither domain knowledge nor learning. We demonstrate the application of this principle to the analysis of data from a number of real-world challenging problems, including the disambiguation of electro-encephalograph patterns pertaining to epileptic seizures, detection of anomalous cardiac activity from heart sound recordings and classification of astronomical objects from raw photometry. In all these cases and without access to any domain knowledge, we demonstrate performance on a par with the accuracy achieved by specialized algorithms and heuristics devised by domain experts. We suggest that data smashing principles may open the door to understanding increasingly complex observations, especially when experts do not know what to look for.

An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps

Aims. A new method is applied to the segmentation, and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia satellite DPAC (Data Processing and Analysis Consortium) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in Gaia final archive. Methods. Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. Self-Organizing Maps (SOM) networks are used to organize the information in clusters of objects, as homogeneous as possible, according to their spectral energy distributions (SED), and to project them onto a 2-D grid where the data structure can be visualized. Results. We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as "UNKNOWN". Firstly, our method can help to distinguish between astrophysical objects and instrumental artifacts. Additionally, the application of our algorithm to SDSS objects of unknown nature has allowed us to identify classes of objects of similar astrophysical nature. In addition, the method allows for the potential discovery of hundreds of novel objects, such as white dwarfs and quasars. Therefore, the proposed method is shown to be very promising for data exploration and knowledge discovery in very large astronomical databases, such as the upcoming Gaia mission.

COMMISSION 5: DOCUMENTATION AND ASTRONOMICAL DATA

IAU Commission 5 deals with data management issues, and its working groups and task groups deal specifically with information handling, with data centres and networks, with technical aspects of collection, archiving, storage and dissemination of data, with designations and classification of astronomical objects, with library services, editorial policies, computer communications, ad hoc methodologies, and with various standards, reference frames, etc., FITS, astronomys Flexible Image Transport System, the major data exchange format, is controlled, maintained and updated by the Working Group FITS.

Commission 5: Documentation and Astronomical Data: (Documentation et Donnees Astronomiques)

Four working groups and three task groups of IAU Commission 5 deal specifically with information handling, technical aspects of collection, archiving, storage and dissemination of data, with designations and classification of astronomical objects, with library services, editorial policies, computer communications, ad hoc methodologies, and with various standards, reference frames etc. Information about Commission 5 working and task groups and their activities may be found in http://nut.inasan.rssi.ru/IAU/.