Discovery, Study, Classification and Modeling of Variable Stars Natalia A. Virnina Department of High and Applied Mathematics, Odessa National Maritime University, Ukraine, [email protected]
Jan 06, 2018
Discovery, Study, Classification and Modeling
of Variable Stars
Natalia A. Virnina
Department of High and Applied Mathematics, Odessa National Maritime University, Ukraine,
I. Discovery of Variable Stars
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
1. Choosing of the field and observations
The best choice is rather small telescope with large field of view.
The best region for searching for new variables is the one (nearly) free of variables.
The chosen field could be checked with the “AAVSO variable stars plotter”: www.aavso.org/observing/charts/vsp/
Where to begin to achieve the best results?
2. Search for new variable stars
1. Visual comparison of different frames (ineffective method)
2. Blinking of different frames (weakly effective method)
3. Statistical search (effective method):
VAST – for the Linux platform
C-Munipack – for the Windows platform
Working with C-Munipack…
Checking of the potential candidates
Checking of the potential candidates
Check the discovery using VizieR-service http://vizier.u-strasbg.fr/viz-bin/VizieR
3. Comparison stars selection
•Important! The comparison stars have to be constant.
•Better to use several comparison stars than only one.
The standard stars in the UBVRcIc photometric system were measured in the vicinities of some variable stars by A. Henden and the AAVSO
group.
In the absence of “standard” stars in the field, the SDSS database could be used as well, if to transform u, g, r, i, z photometric data into the
standard BVRcIc
One comparison star
Five comparison stars
4. Photometric data analyzing
1. Heliocentric correction.
2. Search for the period (periodogram analysis).
3. Classification.
4. Determination of extrema timings and the initial epoch.
Search for the period
“Peranso”, “WinEfk”, “Period 04”…
One of the most universal methods is Lafler & Kinman’s (1965) method
Search for the period
Determination of variability type
Classical classification is available on the web-pagehttp://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
Determination of variability type
The most frequent types
• Eclipsing • Pulsating
4. Determination of extrema timings
4. Determination of extrema timings
Kwee-van Woerden (1956) method
4. Determination of extrema timings
Kwee-van Woerden (1956) method
Referred in the ADS:• «Variable Stars» («Переменные звёзды»)• OEJV (Open European Journal on Variable Stars)• IBVS (International Bulletin of Variable Stars)• Journal of the AAVSO (JAAVSO, eJAAVSO)
Non-Referred• Bulletin de l’AFOEV• BAV Rundbrief• The Astronomer• VSNET Circular
…
5. Publishing of the results
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=0.27123d
Input parametersMain parameters of the system:
i [80° .. 90°] – inclination
T1 4700 K (fixed) – temperature of the primary component
T2 [4100 K .. 5500 K] – temperature of the secondary component
q [1.5 .. 3.0] – mass ratio
Ω1 [3.95 .. 6.61] – potential of the primary component
Ω2 [3.95 .. 6.61] – potential of the secondary component
g1 0.32 (fixed) – gravity brightening of the primary component
A1 0.5 (fixed) – reflection effect for the primary component
g2 0.32 (fixed) – gravity brightening of the secondary component
A2 0.5 (fixed) – reflection effect for the primary component
e 0 (fixed) – eccentricity
p 90 (fixed) – periastron
[-0.02 .. 0.02] – phase shift
Results of modelingparameters:
Inclination 86.815 ± 0.005T2 4510 ± 10
mass ratio 1.858 ± 0.001Ω1 4.986 ± 0.001
Ω2 4.986 ± 0.001
fill-out factor10.7% 0.0017
r1pole 0.31
r1side 0.32
r1back 0.36
r2pole 0.41
r2side 0.44
r2back 0.47
WZ Crv – a binary system with asymmetric phase curves
Temperatures and relative radiuses
+ Spot
Super-WASP observations
Mwasp=0.3528R+0.6472V-0.1213
Mwasp=580nm
Fitting ParametersParameters WASP-2006 WASP-2007 WASP-2008 Our observ.
Inclination, ° 81.23 81.43 81.15 83.84
T1, K 12500 15000 14800 12830
T2, K 5650 5650 5650 5650
1 5.823 6.349 6.185 5.851
2 3.411 3.731 3.585 3.464
Mass ratio 0.796 0.958 0.898 0.807
Third light, % 7.4 11.6 8.7 2.9 (V-band)
Spot parameters
Co-latitude 69 62 30 160
Longitude 151 171 160 212
Radius 48 30 43 56
Temp. factor 0.89 0.53 0.61 0.73
Spot Changes on the Primary Component
Parameters WASP-2006 WASP-2007 WASP-2008
Co-latitude, ° 75±2 51±1 41.5±0.5
Longitude, ° 155±1 167±3 154±1
Radius, ° 45±2 28±1 57±1
Temp. factor 0.874±0.006 0.595±0.002 0.853±0.001
Fitting of WASP Data
2006 2007 2008
ConclusionsAdvantages 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.
Thanks for attention!