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 Previous talk: József Benkő - CoRoT fields before CoRoT Goal: Find variable stars in NSVS database (in CoRoT eyes) Goal: Find variable stars in NSVS database (in CoRoT eyes) This talk presents the method we used to reach our goal 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 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 Variability index (Akerlof et al, 2000): correlation between the residuals from the comparison of each magnitude to the mean value →I var >4.5σ is a good criteria for variable star candidates Stars with 11 or less good data points are omitted Stars with 11 or less good data points are omitted Reduces the field to about 82 000 candidates 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 Still large amount of data → automated data processing algorithm required Noisy and inhomogeneous data set Noisy and inhomogeneous data set False signals (random or systematic errors, trends, sampling effects etc.) 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 Time-Frequency Analyzer Developed in Konkoly Obs. for analyzis of multi- periodic time-series (by Z. Kolláth and Z. Csubry) Developed in Konkoly Obs. for analyzis of multi- periodic time-series (by Z. Kolláth and Z. Csubry) C software engine: 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 User-friendly Graphical Interface High-level script language for complex tasks High-level script language for complex tasks

6. TiFrAn script language Compatible with Tcl Compatible with Tcl Enables complete and/or repeatable tasks, and automatic processing of large data sets Enables complete and/or repeatable tasks, and automatic processing of large data sets Flexible output (PostScript figures and full log of data processing steps) 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 Calculate frequency spectra, find main peaks Find connection between the main peak attribute and variability Find connection between the main peak attribute and variability Significance index: s = (A peak - ) / σ sp Significance index: s = (A peak - ) / σ sp

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 Significant number of peaks are near whole number of cycle/day or zero Long-period variables or trends 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 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 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 Manual examination of TiFrAn output: variable stars with large amplitude and long period are easy to find From higher significance index to lower From higher significance index to lower Lower s: semiregular and irregular variables, results are somewhat obscure 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 Whitening to eliminate sampling effects Recalculate significance index Recalculate significance index If s > 4.5 → Place back into the data pool for further analyzis If s > 4.5 → Place back into the data pool for further analyzis

17. Short period variables More complex TiFrAn script More complex TiFrAn script Determine accurate frequency, amplitude and phase with nonlinear fit Determine accurate frequency, amplitude and phase with nonlinear fit Create folded light curve Create folded light curve Manual examination of TiFrAn output: original and folded light curve + spectrum 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 Automatized analyzis of noisy and inhomogeneous data is very difficult, usually manual intervention needed Two-step semi-automated method works well: 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) Results: 2512 variable stars in COROT fields (1396 new) → 2302 long period variables → 161 short period variables → 45 eclipsing binaries

23. Thank You!