Data analysis of stellar specklegrams with neural networks

Area of Science:

• Astronomy
• Astrophysics
• Computer Science

Background:

• Stellar speckle interferometry is a technique used to obtain high-resolution images of celestial objects.
• Analyzing specklegrams of binary stars to determine their parameters is crucial for understanding stellar evolution and dynamics.
• Traditional methods for analyzing binary star specklegrams can be complex and computationally intensive.

Purpose of the Study:

• To investigate the application of artificial neural networks (ANNs) for the analysis of binary star specklegrams.
• To determine if ANNs can accurately estimate key binary star parameters from speckle data.
• To demonstrate the utility of ANNs in astronomical image processing, specifically for binary star analysis.

Main Methods:

• An artificial neural network model was developed and trained for specklegram analysis.
• The neural network was specifically designed to estimate two primary parameters: angular separation and position angle.
• The model processed specklegram data from binary stars to extract these parameters.

Main Results:

• The study successfully applied an artificial neural network to analyze binary star specklegrams.
• The neural network demonstrated effectiveness in estimating both the angular separation and position angle of binary stars.
• The results confirm that ANNs are a valuable tool for processing and analyzing stellar speckle data.

Conclusions:

• Artificial neural networks provide a powerful and efficient method for analyzing stellar specklegrams.
• The developed neural network approach is useful for accurately determining binary star parameters.
• This study highlights the potential of machine learning in advancing astronomical observations and data analysis.