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Enhanced Astronomical Source Classification with Integration of Attention Mechanisms and Vision Transformers by Bhavanam, Srinadh Reddy; Channappayya, Sumohana S.; Srijith, P. K.; Desai, Shantanu is a scholarly article available to read on EtoBox.

What is Enhanced Astronomical Source Classification with Integration of Attention Mechanisms and Vision Transformers about?

Accurate classification of celestial objects is essential for advancing our understanding of the universe. MargNet is a recently developed deep learning-based classifier applied to SDSS DR16 dataset to segregate stars, quasars, and compact galaxies using photometric data. MargNet utilizes a stacked architecture, combining a Convolutional Neural Network (CNN) for image modelling and an Artificial Neural Network (ANN) for modelling photometric parameters. In this study, we propose enhancing MargNet's performance by incorporating attention mechanisms and Vision Transformer (ViT)-based models for processing image data. The attention mechanism allows the model to focus on relevant features and capture intricate patterns within images, effectively distinguishing between different classes of celestial objects. Additionally, we leverage ViTs, a transformer-based deep learning architecture renowned for exceptional performance in image classification tasks. We enhance the model's understanding of complex astronomical images by utilizing ViT's ability to capture global dependencies and contextual information. Our approach uses a curated dataset comprising 240,000 compact and 150,000 faint obj

Author

Bhavanam, Srinadh Reddy; Channappayya, Sumohana S.; Srijith, P. K.; Desai, Shantanu

Published

2024

Language

EN