1. Descubrimiento, Estudio, Clasificación y Modelado de Estrellas Variables Department of High and Applied Mathematics, Odessa National Maritime University, Ukraine, virnina@gmail.com

2. I. Descubrimiento de Estrellas Variables

3. 5 steps Choosing of the field and observations Searching for new variable stars Selection of comparison stars, photometry Photometric data analysis Publishing of the results

4. Determination of variability type Classical classification is available on the web-page http://sai.msu.su/groups/cluster/gcvs/gcvs/iii/vartype.txt intense variable X-ray sources Groups of types of variability: eruptive variable stars pulsating variable stars rotating variable stars cataclysmic (explosive and nova-like) variables eclipsing binary systems

5. Determination of variability type The most frequent types EclipsingPulsating

6. II. Modeling

7. Modeling using the Wilson-Devinney (W-D) code, Monte Carlo searching algorithm

8. How does the W-D code with the Monte Carlo searching algorithm work?

9. First (initial) iteration

10. Convergence of the iterations

11. BM UMa (V-band) P=0.27123d

12. Input parameters Main parameters of the system: i[80 °.. 90 ° ]– inclination T 1 4700 K (fixed)– temperature of the primary component T 2 [4100 K.. 5500 K] – temperature of the secondary component q[1.5.. 3.0]– mass ratio Ω 1 [3.95.. 6.61]– potential of the primary component Ω 2 [3.95.. 6.61] – potential of the secondary component g 1 0.32 ( fixed )– gravity brightening of the primary component A 1 0.5 ( fixed ) – reflection effect for the primary component g 2 0.32 ( fixed ) – gravity brightening of the secondary component A 2 0.5 ( fixed ) – reflection effect for the primary component e0 ( fixed ) – eccentricity p 90 ( fixed ) – periastron  [-0.02.. 0.02] – phase shift

13. Results of modeling parameters: Inclination 86.815 ± 0.005 T 2 4510 ± 10 mass ratio1.858 ± 0.001 Ω 1 4.986 ± 0.001 Ω 2 4.986 ± 0.001 fill-out factor10.7%  0.0017 r 1 pole 0.31 r 1 side 0.32 r 1 back 0.36 r 2 pole 0.41 r 2 side 0.44 r 2 back 0.47

14. WZ Crv – a binary system with asymmetric phase curves

15. Temperatures and relative radiuses

18. + Spot

23. Super-WASP observations M wasp =0.3528R+0.6472V-0.1213 M wasp =580nm

24. Fitting Parameters ParametersWASP-2006WASP-2007WASP-2008Our observ. Inclination, °81.2381.4381.1583.84 T 1, K12500150001480012830 T 2, K 5650565056505650  1 5.8236.3496.1855.851  2 3.4113.7313.5853.464 Mass ratio0.7960.9580.8980.807 Third light, %7.411.68.72.9 (V-band) Spot parameters Co-latitude696230160 Longitude151171160212 Radius48304356 Temp. factor0.890.530.610.73

25. Spot Changes on the Primary Component ParametersWASP-2006WASP-2007WASP-2008 Co-latitude, °75 ± 251 ± 141.5 ± 0.5 Longitude, ° 155 ± 1167 ± 3154 ± 1 Radius, ° 45 ± 228 ± 157 ± 1 Temp. factor0.874 ± 0.0060.595 ± 0.0020.853 ± 0.001

26. Fitting of WASP Data 200620072008

27. Conclusions Advantages and Disadvantages of W-D code with MC searching algorithm + – The searching runs automatically; Only the borders of parameters are required; From the statistical point of view, the algorithm founds the best solution. Some parameters (mass ratio, inclination etc.) are too unsure; Sometimes statistically best solution is rather far from the real parameters.