CoRoT fields before CoRoT – Processing of Large Photometric Databases

1. CoRoT fields before CoRoT – Processing of Large Photometric Databases Zoltán Csubry Konkoly Observatory Budapest, Hungary Hungarian CoRoT Day Budapest, 2007. 03. 12.

2. Introduction • Previous talk: József Benkő - CoRoT fields before CoRoT • Goal: Find variable stars in NSVS database (in CoRoT eyes) • This talk presents the method we used to reach our goal

3. Pre-selection of candidates • ROTSE database: huge amount of photomertic data, pre-selection is needed • Variability index (Akerlof et al, 2000): correlation between the residuals from the comparison of each magnitude to the mean value →Ivar>4.5σ is a good criteria for variable star candidates • Stars with 11 or less good data points are omitted • Reduces the field to about 82 000 candidates (in CoRoT field of view and magnitude range)

4. Difficulties • Still large amount of data → automated data processing algorithm required • Noisy and inhomogeneous data set • False signals (random or systematic errors, trends, sampling effects etc.) → automatic data processing is very obscure (difficult to separate false and real signals) → We used a two-step semi-automated method

5. TiFrAn • Time-Frequency Analyzer • Developed in Konkoly Obs. for analyzis of multi-periodic time-series (by Z. Kolláth and Z. Csubry) • C software engine: →Time-frequency methods (Wavelet, Wigner-Ville, Choi-Williams, STFT etc.) →Other methods (FFT, DFT, interpolation, filtering, whitening etc.) • User-friendly Graphical Interface • High-level script language for complex tasks

6. TiFrAn script language • Compatible with Tcl • Enables complete and/or repeatable tasks, and automatic processing of large data sets • Flexible output (PostScript figures and full log of data processing steps)

7. Sample TiFrAn script • Read data from file • Calculate FFT • Find highest peak • Calculate frequency, amplitude and phase • Fit parameters • Whitening

8. Application for NSVS data • Calculate frequency spectra, find main peaks • Find connection between the main peak attribute and variability • Significance index: s = (Apeak - <sp>) / σsp

9. Significance index and variability • Rough manual analysis of a small subsample • Correlation between s and the variable ratio • Ratio of variables is less then 1% if s<4.5 • Reduce the field to ~10 000 stars (4481 on winter field and 5490 on summer field)

10. Frequency distribution • Significant number of peaks are near whole number of cycle/day or zero • Long-period variables or trends • Try to use trend-filtering algorithm to separate (TFA, Kovács et al. 2005) → unsuccessful, NSVS data distribution does not allow • Manual search for long period variable stars

11. Long period variables • Manual examination of TiFrAn output: variable stars with large amplitude and long period are easy to find • From higher significance index to lower • Lower s: semiregular and irregular variables, results are somewhat obscure → some of the stars are labeled as variable candidate

15. Results: → 2302 lp variables (1332 new) → 198 candidates

16. Reprocessing of remaining light curves • Whitening to eliminate sampling effects • Recalculate significance index • If s > 4.5 → Place back into the data pool for further analyzis

17. Short period variables • More complex TiFrAn script • Determine accurate frequency, amplitude and phase with nonlinear fit • Create folded light curve • Manual examination of TiFrAn output: original and folded light curve + spectrum

21. Results: → 206 sp variables and eclipsing binaries (58 new)

22. Conclusion • Automatized analyzis of noisy and inhomogeneous data is very difficult, usually manual intervention needed • Two-step semi-automated method works well: 1. Reduce the number of candidates automatically 2. Final selection manually • Results: 2512 variable stars in COROT fields (1396 new) → 2302 long period variables → 161 short period variables → 45 eclipsing binaries

23. Thank You!