Enhanced Astronomical Source Classification with Integration of Attention Mechanisms and Vision Transformers

TL;DR

This paper enhances astronomical object classification by integrating attention mechanisms and Vision Transformers into existing deep learning models, improving accuracy and efficiency in processing large astronomical datasets.

Contribution

It introduces attention and ViT-based models into MargNet, a deep learning classifier, to improve celestial object classification from photometric and image data.

Findings

Attention mechanisms improve feature focus and pattern recognition.

ViT-based models achieve comparable accuracy with reduced complexity.

Hybrid models effectively combine photometric and image data for classification.

Abstract

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 objects. The models learn classification directly from the data, minimizing human intervention. Furthermore, we explore ViT as a hybrid architecture that uses photometric features and images together as input to predict astronomical objects. Our results demonstrate that the proposed attention mechanism augmented CNN in MargNet marginally outperforms the traditional MargNet and the proposed ViT-based MargNet models. Additionally, the ViT-based hybrid model emerges as the most lightweight and easy-to-train model with classification accuracy similar to that of the best-performing attention-enhanced MargNet.