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Z. Garai T. Pribulla
. Hamblek R. Errmann Ch. Adam S. Buder T. Butterley V. S. Dhillon B. Dincel H. Gilbert Ch. Ginski L. K. Hardy A. Kellerer M. Kitze E. Kundra S. P. Littlefair M. Mugrauer J. Nedoro&#x;
ík R. Neuhuser A. Pannicke S. Raetz J. G. Schmidt T. O. B. Schmidt M. Seeliger M. Va&#x;ko R. W. Wilson 《Astronomische Nachrichten》2016,337(3):261-285
We report results of the first five observing campaigns for the open stellar cluster NGC 7243 in the frame of project Young Exoplanet Transit Initiative (YETI). The project focuses on the monitoring of young and nearby stellar clusters, with the aim to detect young transiting exoplanets and to study other variability phenomena on time‐scales from minutes to years. After five observing campaigns and additional observations during 2013 and 2014, a clear and repeating transit‐like signal was detected in the light curve of J221550.6+495611. Furthermore, we detected and analysed 37 new eclipsing binary stars in the studied region. The best fit parameters and light curves of all systems are given. Finally, we detected and analysed 26 new, presumably pulsating variable stars in the studied region. The follow‐up investigation of these objects, including spectroscopic measurements of the exoplanet candidate, is currently planned. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim) 相似文献
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In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm. 相似文献
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