A Multi-modal Fusion Network for Star-Galaxy Classification from CSST Simulated Datasets

TL;DR

This paper presents a deep learning model combining image and catalog data for star-galaxy classification, achieving high accuracy on simulated CSST datasets, and demonstrating robustness for faint and high-redshift objects.

Contribution

The study introduces a multi-modal fusion network based on ResNet-50 and BiLSTM that effectively integrates image and catalog data for improved celestial classification.

Findings

Achieved over 99.8% recall for galaxies and stars.

Demonstrated high accuracy on faint and high-redshift objects.

Validated the effectiveness of multi-modal data fusion and data augmentation.

Abstract

The distinction between stars and galaxies is a fundamental problem in the field of celestial classification. This issue has become challenging for these ongoing and upcoming digital surveys, which will produce terabytes and even petabytes of astronomical data. While deep learning offers a powerful solution for star-galaxy classification in large-scale datasets, most current approaches are limited by their reliance on catalog data alone, which consists primarily of multi-band magnitudes and imprecise morphological parameters. Therefore, we utilize China Space Station Telescope (CSST) simulation data to build a dataset with both image and photometric catalog, including 32,371 stars and 93,525 galaxies. A supervised deep learning network based on ResNet-50 and BiLSTM is proposed to improve the classification of two types of astronomical objects. The features of the catalog and image are integrated by the model, achieving 99.81% recall for galaxies and 99.66% recall for stars after training on GPU for 50 epochs. We evaluated the effects of data augmentation and multi-modal data fusion, which demonstrate that our model has commendable performance. Furthermore, our model also has a high accuracy rate for faint astronomical objects and high redshift galaxies, demonstrating its applicability to the upcoming CSST scientific data.