[论文解读] Deep learning-based astronomical multimodal data fusion: A comprehensive review

Wujun Shao, Dongwei Fan+4 more|arXiv (Cornell University)|Feb 28, 2026

Gamma-ray bursts and supernovae被引用 0

一句话总结

本评审综述基于深度学习的天文学多模态数据融合，概述数据源、模态、模型、融合策略、数据集、挑战与未来方向。

ABSTRACT

With the rapid advancements in observational technologies and the widespread implementation of large-scale sky surveys, diverse electromagnetic wave data (e.g., optical and infrared) and non-electromagnetic wave data (e.g., gravitational waves) have become increasingly accessible. Astronomy has thus entered an unprecedented era of data abundance and complexity. Astronomers have long relied on unimodal data analysis to perceive the universe, but these efforts often provide only limited insights when confronted with the current massive and heterogeneous astronomical data. In this context, multimodal data fusion (MDF), as an emerging method, provides new opportunities to enhance the value of astronomical data and deepening the understanding of the universe by integrating information from different modalities. Recent progress in artificial intelligence (AI), particularly in deep learning (DL), has greatly accelerated the development of multimodal research in astronomy. Therefore, a timely review of this field is essential. This paper begins by discussing the motivation and necessity of astronomical MDF, followed by an overview of astronomical data sources and major data modalities. It then introduces representative DL models commonly used in astronomical multimodal studies, the general fusion process as well as various fusion strategies, emphasizing their characteristics, applicability, advantages, and limitations. Subsequently, the paper surveys existing astronomical multimodal studies and datasets. Finally, the discussion section synthesizes key findings, identifies potential challenges, and suggests promising directions for future research. By offering a structured overview and critical analysis, this review aims to inspire and guide researchers engaged in DL-based MDF in astronomy.

研究动机与目标

• Motivate the use of multimodal data fusion (MDF) in astronomy to overcome limitations of unimodal analyses.
• Provide a systematic framework for DL-based astronomical MDF covering data sources, modalities, fusion levels, and model development.
• Critically analyze representative DL architectures and fusion strategies for astronomy.
• Summarize existing studies and datasets and discuss current challenges and potential future directions.

提出的方法

• Introduce a unified framework for DL-based astronomical MDF linking data sources, modalities, fusion levels, and model development.
• Categorize and critically analyze representative DL architectures (ANNs, CNNs, AEs/VAEs, GANs, RNNs, Transformers) and fusion strategies.
• Survey existing astronomical MDF studies and public datasets.
• Discuss challenges such as data heterogeneity and lack of benchmarks, and suggest future research directions.

Figure 1: The basic framework of DL-based MDF in astronomy.

实验结果

研究问题

• RQ1 What data sources and modalities are used in astronomical MDF?
• RQ2 What DL models and fusion strategies are most effective for cross-modal astronomical data?
• RQ3 What datasets and benchmarks currently exist for astronomical MDF, and what are their limitations?
• RQ4 What are the main challenges and promising directions for DL-based MDF in astronomy?

主要发现

• The paper establishes a systematic framework for DL-based astronomical MDF.
• It categorizes and analyzes representative DL architectures and fusion strategies with their applicability.
• It provides a detailed overview of retrieved papers and datasets in the field.
• It discusses current challenges (data heterogeneity, lack of benchmarks) and proposes future research directions.

Figure 2: Multi-band and multi-platform collaborative observations. The upper part: Taking multi-band observation images of the Crab Nebula as an example, it is evident that data from different wavebands can reveal distinct details, demonstrating the complementarity among them. Credit: [ $\gamma$ -r