Descubrimiento, Estudio, Clasificación y Modelado de Estrellas Variables Department of High and Applied Mathematics, Odessa National Maritime University,

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Presentation transcript:

Descubrimiento, Estudio, Clasificación y Modelado de Estrellas Variables Department of High and Applied Mathematics, Odessa National Maritime University, Ukraine,

I. Descubrimiento de Estrellas Variables

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

Determination of variability type Classical classification is available on the web-page 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

Determination of variability type The most frequent types EclipsingPulsating

II. Modeling

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

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

First (initial) iteration

Convergence of the iterations

BM UMa (V-band) P= d

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

Results of modeling parameters: Inclination ± T ± 10 mass ratio1.858 ± Ω ± Ω ± fill-out factor10.7%  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

WZ Crv – a binary system with asymmetric phase curves

Temperatures and relative radiuses

+ Spot

Super-WASP observations M wasp =0.3528R V M wasp =580nm

Fitting Parameters ParametersWASP-2006WASP-2007WASP-2008Our observ. Inclination, ° T 1, K T 2, K   Mass ratio Third light, % (V-band) Spot parameters Co-latitude Longitude Radius Temp. factor

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

Fitting of WASP Data

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.