Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data
The purpose of this study was to develop a period analysis algorithm for detecting new variable stars in the time-series data observed by charge coupled device (CCD). We used the data from a variable star monitoring program of the CBNUO. The R filter data of some magnetic cataclysmic variables obser...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Korean Space Science Society
2019-12-01
|
| Series: | Journal of Astronomy and Space Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.5140/JASS.2019.36.4.283 |
| _version_ | 1827393650010619904 |
|---|---|
| author | Kim, Dong-Heun Kim, Yonggi Yoon, Joh-Na Im, Hong-Seo |
| author_facet | Kim, Dong-Heun Kim, Yonggi Yoon, Joh-Na Im, Hong-Seo |
| author_sort | Kim, Dong-Heun |
| collection | DOAJ |
| description | The purpose of this study was to develop a period analysis algorithm for detecting new variable stars in the time-series data observed by charge coupled device (CCD). We used the data from a variable star monitoring program of the CBNUO. The R filter data of some magnetic cataclysmic variables observed for more than 20 days were chosen to achieve good statistical results. World Coordinate System (WCS) Tools was used to correct the rotation of the observed images and assign the same IDs to the stars included in the analyzed areas. The developed algorithm was applied to the data of DO Dra, TT Ari, RXSJ1803, and MU Cam. In these fields, we found 13 variable stars, five of which were new variable stars not previously reported. Our period analysis algorithm were tested in the case of observation data mixed with various fields of view because the observations were carried with 2K CCD as well as 4K CCD at the CBNUO. Our results show that variable stars can be detected using our algorithm even with observational data for which the field of view has changed. Our algorithm is useful to detect new variable stars and analyze them based on existing time-series data. The developed algorithm can play an important role as a recycling technique for used data. |
| first_indexed | 2024-03-08T17:53:18Z |
| format | Article |
| id | doaj.art-79cf0ba763b549a186036f899f85cb01 |
| institution | Directory Open Access Journal |
| issn | 2093-5587 2093-1409 |
| language | English |
| last_indexed | 2024-03-08T17:53:18Z |
| publishDate | 2019-12-01 |
| publisher | The Korean Space Science Society |
| record_format | Article |
| series | Journal of Astronomy and Space Sciences |
| spelling | doaj.art-79cf0ba763b549a186036f899f85cb012024-01-02T06:17:51ZengThe Korean Space Science SocietyJournal of Astronomy and Space Sciences2093-55872093-14092019-12-0136428329210.5140/JASS.2019.36.4.283Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational DataKim, Dong-Heun0Kim, Yonggi1Yoon, Joh-Na2Im, Hong-Seo3Chungbuk National University Observatory, Cheongju 28644, KoreaDepartment of Astronomy and Space Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Astronomy and Space Science, Chungbuk National University, Cheongju 28644, KoreaKorea Astronomy and Space Science Institute, Daejeon 34055, KoreaThe purpose of this study was to develop a period analysis algorithm for detecting new variable stars in the time-series data observed by charge coupled device (CCD). We used the data from a variable star monitoring program of the CBNUO. The R filter data of some magnetic cataclysmic variables observed for more than 20 days were chosen to achieve good statistical results. World Coordinate System (WCS) Tools was used to correct the rotation of the observed images and assign the same IDs to the stars included in the analyzed areas. The developed algorithm was applied to the data of DO Dra, TT Ari, RXSJ1803, and MU Cam. In these fields, we found 13 variable stars, five of which were new variable stars not previously reported. Our period analysis algorithm were tested in the case of observation data mixed with various fields of view because the observations were carried with 2K CCD as well as 4K CCD at the CBNUO. Our results show that variable stars can be detected using our algorithm even with observational data for which the field of view has changed. Our algorithm is useful to detect new variable stars and analyze them based on existing time-series data. The developed algorithm can play an important role as a recycling technique for used data.https://doi.org/10.5140/JASS.2019.36.4.283period analysisvariable stardata reductiontime-series observation |
| spellingShingle | Kim, Dong-Heun Kim, Yonggi Yoon, Joh-Na Im, Hong-Seo Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data Journal of Astronomy and Space Sciences period analysis variable star data reduction time-series observation |
| title | Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data |
| title_full | Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data |
| title_fullStr | Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data |
| title_full_unstemmed | Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data |
| title_short | Development of a Period Analysis Algorithm for Detecting Variable Stars in Time-Series Observational Data |
| title_sort | development of a period analysis algorithm for detecting variable stars in time series observational data |
| topic | period analysis variable star data reduction time-series observation |
| url | https://doi.org/10.5140/JASS.2019.36.4.283 |
| work_keys_str_mv | AT kimdongheun developmentofaperiodanalysisalgorithmfordetectingvariablestarsintimeseriesobservationaldata AT kimyonggi developmentofaperiodanalysisalgorithmfordetectingvariablestarsintimeseriesobservationaldata AT yoonjohna developmentofaperiodanalysisalgorithmfordetectingvariablestarsintimeseriesobservationaldata AT imhongseo developmentofaperiodanalysisalgorithmfordetectingvariablestarsintimeseriesobservationaldata |