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تسجيل مستخدم جديدTransient Detection and Classification
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Andrew C. Becker
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I provide an incomplete inventory of the astronomical variability that will be found by next-generation time-domain astronomical surveys. These phenomena span the distance range from near-Earth satellites to the farthest Gamma Ray Bursts. The surveys that detect these transients will issue alerts to the greater astronomical community; this decision process must be extremely robust to avoid a slew of ``false alerts, and to maintain the communitys trust in the surveys. I review the functionality required of both the surveys and the telescope networks that will be following them up, and the role of VOEvents in this process. Finally, I offer some ideas about object and event classification, which will be explored more thoroughly by other articles in these proceedings.
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ce pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as naive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.
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