Computer Science – Learning
Scientific paper
Nov 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001spie.4477...96l&link_type=abstract
Proc. SPIE Vol. 4477, p. 96-106, Astronomical Data Analysis, Jean-Luc Starck; Fionn D. Murtagh; Eds.
Computer Science
Learning
2
Scientific paper
Measuring vector magnetic fields in the solar atmosphere using the profiles of the Stokes parameters of polarized spectral lines split by the Zeeman effect is known as Stokes Inversion. This inverse problem is usually solved by least-squares fitting of the Stokes profiles. However least-squares inversion is too slow for the new generation of solar instruments (THEMIS, SOLIS, Solar-B, ...) which will produce an ever-growing flood of spectral data. The solar community urgently requires a new approach capable of handling this information explosion, preferably in real-time. We have successfully applied pattern recognition and machine learning techniques to tackle this problem. For example, we have developed PCA-inversion, a database search technique based on Principal Component Analysis of the Stokes profiles. Search is fast because it is carried out in low dimensional PCA feature space, rather than the high dimensional space of the spectral signals. Such a data compression approach has been widely used for search and retrieval in many areas of data mining. PCA-inversion is the basis of a new inversion code called FATIMA (Fast Analysis Technique for the Inversion of Magnetic Atmospheres). Tests on data from HAO's Advanced Stokes Polarimeter show that FATIMA isover two orders of magnitude faster than least squares inversion. Initial tests on an alternative code (DIANNE - Direct Inversion based on Artificial Neural NEtworks) show great promise of achieving real-time performance. In this paper we present the latest achievements of FATIMA and DIANNE, two powerful examples of how pattern recognition techniques can revolutionize data analysis in astronomy.
Lites Bruce W.
López Ariste Arturo
Rees David E.
Socas-Navarro Hector
No associations
LandOfFree
Pattern recognition techniques and the measurement of solar magnetic fields does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Pattern recognition techniques and the measurement of solar magnetic fields, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Pattern recognition techniques and the measurement of solar magnetic fields will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1187818