Journal of The Korean Astronomical Society (천문학회지)

The Korean Astronomical Society (한국천문학회)
권호 표지
DOI QR코드

DOI QR Code

EFFICIENT PERIOD SEARCH FOR TIME SERIES PHOTOMETRY

  • SHIN MIN-SU (Yonsei University Observatory and Department of Astronomy) ;
  • BYUN YONG-IK (Yonsei University Observatory and Department of Astronomy)
  • Published : 2004.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

We developed an algorithm to identify and determine periods of variable sources. With its robustness and high speed, it is expected to become an useful tool for surveys with large volume of data. This new scheme consists of an initial coarse. process of finding several candidate periods followed by a secondary process of much finer period search. With this multi-step approach, best candidates among statistically possible periods are produced without human supervision and also without any prior assumption on the nature of the variable star in question. We tested our algorithm with 381 stars taken from the ASAS survey and the result is encouraging. In about $76\%$ cases, our results are nearly identical as their published periods. Our algorithm failed to provide convincing periods for only about $10\%$ cases. For the remaining $14\%$, our results significantly differ from their periods. We show that, in many of these cases, our periods are superior and much closer to the true periods. However, the existence of failures, and also periods sometimes worse than manually controlled results, indicates that this algorithm needs further improvement. Nevertheless, the present experiment shows that this is a positive step toward a fully automated period analysis for future variability surveys.

Keywords

References

  1. ApJ v.436 Application of cubic splines to the spectral analysis of unequally spaced data Akerlof,C.(et al.) https://doi.org/10.1086/174954
  2. AJ v.119 ROTSE All-Sky Surveys for Variable Stars. I. Test Fields Akerlof,C.(et al.) https://doi.org/10.1086/301321
  3. Small Telescopes Astronomy on Global Scales(ASP Conf. Ser. 246) IAU Collaw. 183 Byun,Y.I.;Han,W.Y.;Kang,Y.W.(et al.);B.Paczynski(ed.);W.P.Chen(ed.);C.Lemme(ed.)
  4. A&A v.264 Power spectra of gapped time series - A comparison of several methods Carbonell,M.;Ballester,J.L.
  5. AcA v.48 The All Sky Aitonated Survey. The Catalog of the Periodic Variable Stars in the Selected Fields Pojmanski,G.
  6. AcA v.50 The All Sky Aitonated Survey. Catalog of about 3800 Variable Stars Pojmanski,G.
  7. ApJ v.263 Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data Scargle,J.D. https://doi.org/10.1086/160554
  8. ApJ v.460 Fast and Statistically Optimal Period Search in Uneven Sampled Observations Schwarzenberg-Czerny,A.
  9. ApJ v.489 The Correct Probability Distribution for the Phase Dispersion Minimization Peridogram Schwarzenberg-Czerny,A. https://doi.org/10.1086/304832
  10. Baltic Astronomy v.7 Period Search in Large Datasets Schwarzenberg-Czerny,A.
  11. ApJ v.516 Optimum Period Search: Quantitative Analysis Schwarzenberg-Czerny,A. https://doi.org/10.1086/307081
  12. ApJ v.224 Period determination using phase dispersion minimization Stellingwerf,R.F. https://doi.org/10.1086/156444
  13. Light Curves of Variable Stars Sterken,C.;Jaschek,C.
  14. AcA v.44 The Optical Gravitational Lensing Experiment. The Catalog of Periodic Variables Stars in the Galactic Bulge. I. Periodic Variables in the Center of the Baade's Window Udakski,A.;Kubiak,M.;Szymanski,M.;Kaluzny,J.;Mateo,M.;Krzeminski,W.

Cited by

  1. Analysis of massive data in astronomy vol.29, pp.6, 2016, https://doi.org/10.5351/KJAS.2016.29.6.1107
  2. A comparison of period finding algorithms vol.434, pp.4, 2013, https://doi.org/10.1093/mnras/stt1264
  3. DETECTING VARIABILITY IN MASSIVE ASTRONOMICAL TIME-SERIES DATA. II. VARIABLE CANDIDATES IN THE NORTHERN SKY VARIABILITY SURVEY vol.143, pp.3, 2012, https://doi.org/10.1088/0004-6256/143/3/65
  4. A database of UV variables from the GALEX surveys vol.53, pp.6, 2014, https://doi.org/10.1016/j.asr.2013.07.022
  5. Detecting variability in massive astronomical time series data – I. Application of an infinite Gaussian mixture model vol.400, pp.4, 2009, https://doi.org/10.1111/j.1365-2966.2009.15576.x
  6. Detecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering Method vol.156, pp.5, 2018, https://doi.org/10.3847/1538-3881/aae263