| 摘要(英) |
In astronomical researches, the rotation periods of asteroids can be derived from their light curves which are the brightness as a function of time. Traditionally, a periodical analysis is performed on the light curve, and then astronomers determine the category of a possible period according to the folded light curve (i.e., a light curve folded to a particular period). This process was relied on human inspection, but it becomes very formidable due to the advancement in the technology of astronomical observation in the last decade that increases the volume of astronomical data set dramatically. Therefore, manual inspection is no longer feasible, and it is necessary to adopt an automatic method to replace the aforementioned time-consuming human review process.
In this research, we use the asteroid light curves obtained from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). The light curves are fitted using the second-order Fourier series to find the possible rotation periods from the periodogram (i.e., the reduced 2 as a function of period). Then, a folded light curve of a certain period is generated. When a folded light curve shows a clear trend with a double-peak feature (i.e., similar to W), it is identified as a full rotation period. If a folded light curve only shows a single peak, a half rotation period is suggested (i.e., similar to V). The other cases are seen as no period found. Therefore, we deployed a deep learning technology, using convolutional neural network (CNN) as a network architecture, to construct a model to classify the folded light curves to W, V, and other shapes. From the study, we found that our model can precisely and yet much more effectively recognize a result consistent with that of human inspection.
|
| 參考文獻 |
[1] N. Kaiser, H. Aussel, B. E. Burke, H. Boesgaard, K. Chambers, M. R. Chun et al., "Pan-STARRS: a large synoptic survey telescope array", Survey and Other Telescope Technologies and Discoveries, Vol 4836, pp.154-165, December 2002.
[2] Pan-STARRS official website. http://pswww.ifa.hawaii.edu/pswww/
[3] Space Telescope Science Institute to Host Data from World′s Largest Digital Sky Survey, Dec 19, 2016, http://hubblesite.org/news_release/news/2016-41/year/2016
[4] The Pan-STARRS1 data archive home page. https://panstarrs.stsci.edu/
[5] Pan-STARRS Sky Survey, Jan 28, 2019, http://hubblesite.org/image/4316/news_release/2019-12
[6] Light Curves and What They Can Tell Us. https://imagine.gsfc.nasa.gov /science/toolbox/timing1.html
[7] Russell and H.N, ”On the light variations of asteroids and satellites”, Astrophysical Journal, Vol 24, pp.1-18, July 1906.
[8] Fujiwara A., Kawaguchi J, Yeomans DK, et al., ” The Rubble-Pile Asteroid Itokawa as Observed by Hayabusa” , Science , Vol 312, Issue 5778, pp.1330-1334, June 2006.
[9] Harris and A.~W, “The Rotation Rates of Very Small Asteroids: Evidence for ′Rubble Pile′ Structure”, Lunar and Planetary Institute Science Conference, Vol 27, pp. 493, March 1996.
[10] Chan-Kao Chang, Hsing-Wen Line, Wing-Huen Ip, et al., ”Searching for Super-fast Rotators Using the Pan-STARRS 1”, The Astrophysical Journal Supplement, Vol 241, Number1, February 2019.
[11] Y. LeCun, B. Boser, J. S. Denker, et. al., “Backpropagation Applied to Handwritten Zip Code Recognition”, Neural computation, Vol 1, Issue:4, pp541-551, December 1989.
[12] Yann LeCun, Léeon Bottou, Yoshua Bengio, and Patrick Haffner, ” Gradient-Based Learning Applied to Document Recognition“, Proceedings of the IEEE, Vol 86, Issue: 11, pp2278 – 2324, November 1998.
[13] 陳柏樺,「CNN結合GA用於手寫字元之辨識」, 國立高雄應用科技大學電機工程系碩士論, 2011
[14] 李兆健,「卷積神經網路應用於中文字手寫風格辨識」, 國立成功大學工程科學系碩士論文, 2017。
[15] MNIST dataset official website. http://yann.lecun.com/exdb/mnist/
[16] TensorFlow official website. https://www.tensorflow.org/
[17] Keras official website. https://keras.io/
[18] Reduced chi-squared statistic. https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
[19] A.Krizhevsky, I.Sutskever, and G.E.Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, NIPS′12 Proceedings of the 25th International Conference on Neural Information Processing Systems, pp1097-1105, Lake Tahoe, Nevada , December 2012.
[20] K.Simonyan, A.Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition” Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition(CVPR), 1409.1556, June 2015.
[21] C.Szegedy, W.Liu, Y.Jia, P.Sermanet, S.Reed, D.Anguelov, D.Erhan, V.Vanhoucke, and A.Rabinovich, “Going Deeper with Convolutions”, Proc.of IEEE Int. Conf.on Computer Vision and Pattern Recognition(CVPR), pp.1-9, Boston,MA, June 2015.
[22] K.he, X.Zhang, S.Ren, and J.Sun, “Deep Residual Learning for Image Recognition”, Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition(CVPR), pp.770-778, Las Vegas, NV, June 2016.
[23] Deep Face Recognition with Keras, https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/
[24] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, Journal of Machine Learning Research, pp.1929-1958, June 2014.
[25] A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem”, pp.117-122, 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujście, Poland, May 2018.
|
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit