Mode Frequency Analysis of Variables: HD 163032 and HD 6859

• Main Authors: Yen-Chen Pan, 潘彥丞
• Other Authors: Dean-Yi Chou
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/84678566846026539822

碩士 === 國立清華大學 === 天文研究所 === 96 === EAST Network is a globally observational network. It is expected to solve the problem of time gaps appeared in a long term observation caused by the rotation of the earth. The time string data with gaps may result in fake signals. In the beginning of thesis, we present the works on the search of variable stars and light curve analysis. We use ensemble photometry (Honeycutt 1992) to search the suspects of variable in a group of stars automatically. The method will be helpful in searching the variables in star clusters. In the procedure of differential photometry, we develop an improved technique to modify our reference star. We not only smooth the reference but also preserve its spectral feature. The fluctuations of light curves are reduced successfully. Next, we use the program PERIOD04 (Lenz & Breger 2004) to analyze the multi-periodic properties of our targets. The targets are two double stars HD163032 and HD 6859. HD 163032 is a new variable star discovered by EAST on May 2007 (Li-Han, Wang 2007). There is no measurement recorded about its variability before. HD 6859 is a known δ scuti variable. After period analysis, we found that the dominant frequencies of HD 163032 correspond to a typical δ scuti variable. If we can confirm its spectral type, we may probably infer the variable type of HD 163032. For both double stars, the observational duty cycle is low (0.18 for HD 163032; 0.02 for HD 6859), so the extracted frequencies are not convincing enough. Furthermore, the drift of zero-points in light curves from different days seems to affect the results of our analysis. We think it may be caused by the differences of systematic variation between different days. We hope that a continued observation of different sites in the future will improve the quality of our data.