Application of a probabilistic neural network in radial velocity curve analysis of the spectroscopic binary stars PV Pup, BV Dra, AI Phe, V1130 Tau, NSV 223, and V502 Oph

2011 ◽  
Vol 89 (10) ◽  
pp. 1035-1040 ◽  
Author(s):  
K. Karami ◽  
K. Ghaderi ◽  
R. Mohebi ◽  
M.M. Soltanzadeh
Keyword(s):  
Radial Velocity ◽  
Binary Stars ◽  
Binary Systems ◽  
Probabilistic Neural Network ◽  
Curve Analysis ◽  
Velocity Data ◽  
Radial Velocity Curve ◽  
Spectroscopic Binary ◽  
Radial Velocity Data ◽  
Good Agreement

Using measured radial velocity data from six double-lined spectroscopic binary systems PV Pup, BV Dra, AI Phe, V1130 Tau, NSV 223 (or DZ Psc), and V502 Oph, we find corresponding orbital and spectroscopic elements via the method introduced by Karami et al. (New Astron. 14, 478 (2009)). Our numerical results are in good agreement with those obtained by others using more traditional methods. Using a statistical analysis, we also conclude that for BV Dra, V1130 Tau, NSV 223 (or DZ Psc), and V502 Oph, a circular orbit is quite consistent.

scholarly journals New Radial Velocity Studies and Curve Analysis of Spectroscopic Binary Systems CS22964-161, LV Her, RW Lac and HD 34700

2013 ◽  
Vol 20 ◽  
pp. 9-17
Author(s):  
K. Ghaderi ◽  
M.H. Baghadam ◽  
T. Rostami
Keyword(s):  
Neural Network ◽  
Radial Velocity ◽  
Binary Systems ◽  
Probabilistic Neural Network ◽  
Curve Analysis ◽  
Velocity Data ◽  
Traditional Methods ◽  
Spectroscopic Binary ◽  
Radial Velocity Data ◽  
Good Agreement

Using measured radial velocity data of four double-lined spectroscopic binary systems CS22964-161, LV Her, RW Lac and HD 34700, we find corresponding orbital and spectroscopic elements via a Probabilistic Neural Network (PNN). Our numerical results are in good agreement with those obtained by others using more traditional methods.

scholarly journals Velocity-Curve Analysis of the Spectroscopic Binary Stars V373 Cas, V2388 Oph, V401 Cyg, GM Dra, V523 Cas, AB And and HD 141929 by Artificial Neural Networks

2009 ◽  
Vol 26 (2) ◽  
pp. 121-127 ◽  
Author(s):  
K. Karami ◽  
K. Ghaderi ◽  
R. Mohebi ◽  
R. Sadeghi ◽  
M. M. Soltanzadeh
Keyword(s):  
Binary Stars ◽  
Binary Systems ◽  
Curve Analysis ◽  
Velocity Data ◽  
Orbital Parameters ◽  
Spectroscopic Binary ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radial Velocity Data ◽  
Good Agreement

AbstractWe used an Artificial Neural Network (ANN) to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of seven double-lined spectroscopic binary systems V373 Cas, V2388 Oph, V401 Cyg, GM Dra, V523 Cas, AB And and HD 141929, we found corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by others using more traditional methods.

Application of a probabilistic neural network in analysis of the radial velocity curve of spectroscopic binary stars

2012 ◽  
Vol 12 (12) ◽  
pp. 1666-1672 ◽  
Author(s):  
Kamal Ghaderi ◽  
Kayoomars Karami ◽  
Ali Pirkhedri ◽  
Hamid Haj Seyyed Javadi ◽  
Touba Rostami
Keyword(s):  
Neural Network ◽  
Radial Velocity ◽  
Binary Stars ◽  
Probabilistic Neural Network ◽  
Velocity Curve ◽  
Radial Velocity Curve ◽  
Spectroscopic Binary

scholarly journals NEW ORBITAL PARAMETERS AND RADIAL VELOCITY CURVE ANALYSIS OF SPECTROSCOPIC BINARY STARS

2012 ◽  
Vol 45 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Kamal Ghaderi ◽  
Ali Pirkhedri ◽  
Touba Rostami ◽  
Salem Khodamoradi ◽  
Hedayat Fatahi
Keyword(s):  
Radial Velocity ◽  
Binary Stars ◽  
Curve Analysis ◽  
Velocity Curve ◽  
Radial Velocity Curve ◽  
Orbital Parameters ◽  
Spectroscopic Binary

scholarly journals The Radial Velocity Curve of V652 Her (BD+13°3224)

1985 ◽  
Vol 87 ◽  
pp. 109-115
Author(s):  
P.W. Hill ◽  
C.S. Jeffery
Keyword(s):  
Radial Velocity ◽  
Time Resolution ◽  
High Frequency ◽  
Velocity Curve ◽  
Velocity Data ◽  
Effective Surface ◽  
Radial Velocity Curve ◽  
Minimum Radius ◽  
Helium Star ◽  
Radial Velocity Data

AbstractNew radial velocity data for the pulsating extreme helium star V652 Her (BD+13°3224) have been obtained with a time resolution of 100 s. High frequency structure in the radial velocity curve is detected, and a comparison with previous data suggests that the detailed shape of the velocity curve is variable. The data imply that the effective surface gravity must increase by a factor of 4 at minimum radius.

scholarly journals Kinematics of Disk Stars in the Solar Neighbourhood

1998 ◽  
Vol 11 (1) ◽  
pp. 574-574
Author(s):  
A.E. Gómez ◽  
S. Grenier ◽  
S. Udry ◽  
M. Haywood ◽  
V. Sabas ◽  
...  
Keyword(s):  
Dispersion Relation ◽  
Radial Velocity ◽  
Velocity Dispersion ◽  
Tangential Velocity ◽  
Thin Disk ◽  
The Other ◽  
Proper Motions ◽  
Velocity Data ◽  
Kinematic Data ◽  
Radial Velocity Data

Using Hipparcos parallaxes and proper motions together with radial velocity data and individual ages estimated from isochones, the velocity ellipsoid has been determined as a function of age. On the basis of the available kinematic data two different samples were considered: a first one (7789 stars) for which only tangential velocities were calculated and a second one containing 3104 stars with available U, V and W velocity components and total velocities ≤ 65 km.s-1. The main conclusions are: -Mixing is not complete at about 0.8-1 Gyr. -The shape of the velocity ellipsoid changes with time getting rounder from σu/σv/σ-w = 1/0.63/0.42 ± 0.04 at about 1 Gyr to1/0.7/0.62 ±0.04 at 4-5 Gyr. -The age-velocity-dispersion relation (from the sample with kinematical selection) rises to a maximum, thereafter remaining roughly constant; there is no dynamically significant evolution of the disk after about 4-5 Gyr. -Among the stars with solar metallicities and log(age) > 9.8 two groups are identified: one has typical thin disk characteristics, the other is older than 10 Gyr and lags the LSR at about 40 km.s-1 . -The variation of the tangential velocity with age(without selection on the tangential velocity) shows a discontinuity at about 10 Gyr, which may be attributed to stars typically of the thick disk populations for ages > 10 Gyr.

An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

2008 ◽  
Vol 136 (3) ◽  
pp. 945-963 ◽  
Author(s):  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Computational Cost ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Velocity Data ◽  
Background Error ◽  
Cross Covariance ◽  
Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

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