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名稱Title
2020天文年會
中研院天文所 國際會議廳

論文摘要

Machine learning & time series analysis on Mira variables: from OGLE to ZTF.

[ Poster ]

JiaYu Ou(Graduate Institute of Astronomy, National Central University);Chow-Choong Ngeow(Graduate Institute of Astronomy, National Central University)

Nowadays , there are a lot of sky survey datasets with cadence of days, and machine learning is one of the most popular techniques to analyze the data due to its very powerful classification ability. There are several survey projects that we can obtain light curve data for interested variable stars, one of them being Mira variables.  Mira variables are asymptotic giant branch pulsating stars that exhibit large cyclical variation spanning 100 to 700 days, but in some extreme cases the variations can go beyond 1500 days.  Mira variables can be divided into O-rich and C-rich Miras. We collected 2015 confirmed Miras light curve data in LMC and SMC from OGLE database. Based on the light curves we found Mira can also be divided into regular Miras and multi-periodic Miras. We used python package Feature analysis for time series (FATs) to extract the light curve features, then we used these features to separate out the regular Mira and multi-periodic Mira using machine learning techniques. We found in regular Miras magnitude of maximum light can improve the period-luminosity relation, and we found that regular Miras and multi-period Miras exhibit difference in color index using the OGLE photometric dataset. We also found that light curves of the multi-periodic Miras can be decomposed to a short pulsation period and a long secondary variation. And we collect SED data of regular Mira and multi-period Mira we found they are have different component with their SED. We then applied our results to [HBS 2006] 40671, a confirmed long period Mira found in M33. Using observed light curves from Hartmann et al (2006) data, Barsukova et al. (2011) found a period of 665 day for this Mira.  In addition to Hartmann's data (2005~2006), we also collected LOT( Lulin Observatory) data, CRTS (Catalina Real-time Transient Survey) data, ASAS-SN data,  PTF (Palomar Transient Factory) and ZTF (Zwicky Transient Facility) data taken from 2009 to 2018.  Combining all datasets that spanned ~13 years we can refine the period of this Mira. Based on the combined light curve, we found that this Mira could exhibit a long secondary variation, hence we classified [HBS 2006] 40671 as a multi-periodic Mira.