MSShin-Machine_Learning_Algorithm_in_Period_Estimation.ppt
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The document discusses using machine learning algorithms to estimate the periods of variable stars from their light curves. It reviews existing period estimation methods and the problem of discriminating the true period from spurious periods. It then explores using a machine learning approach, focusing on extracting features from light curves and using algorithms like Prism to classify samples and determine periods. The preliminary experiments showed Prism had the highest performance but challenges remained around feature selection and noise elimination preprocessing.
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- 3. Process of period estimation II (review) The sum of Cubic Spline functions is fitted to folded time-series data.
- 4. Problem is… What is the most appropriate period of the variable star? Discrimination of the most appropriate period from spurious periods by a machine learning algorithm
- 5. Machine learning algorithm Intelligent behavior requires knowledge. If we can program computers to learn from experience, we can bring various tasks within automation. Learning = Inference + Memorization Data Knowledge
- 6. Usage of a machine learning algorithm in other cases in astronomy Classification of spectrum Determination of morphology
- 7. Light curve and machine learning (1)
- 8. Light curve and machine learning (2)
- 9. Experiment for determination of periods Selection of the software “WEKA” Input parameters from a light curve are: Flat portion Dispersion in mag. Dispersion in phase # of the local dim points # of repeated local dim points # of repeated local bright points Slope around local dim points Max interval in phase Used Algorithms are: ZeroR, ID3, and etc.
- 10. Pre-processing Noise elimination by the gravity model Extraction of features from the noiseless light curve
- 11. Result of a simple experiment Among 300 samples, 1/3 is a true sample. Others are false samples. The “Prism” algorithm showed the highest performance!
- 12. Preliminary analysis The “Prism” algorithm is one of the rule-based algorithms, which constructs if-then rules for learning structure. When complex rule is adopted, the performance is generally improved.
- 13. Noise elimination deforms the shape of a light curve. No detection of some features Problems…
- 14. Future plan What are appropriate features of light curves? Without preprocessing, image pattern recognition and classification? I need more help from computer scientists in using machine learning technologies. But… I hope…
- 15. Hunting for new variable stars in 2004… The discovery of YSTAR in December 2003?