Our Technology

Advances in Ground-Based Astronomical Imaging through Deep Learning Deconvolution

Abstract

At noSat, we conduct cutting-edge research to improve the quality of astronomical images obtained from Earth. Our work focuses on developing innovative deconvolution methods that combine classical techniques with deep learning approaches. By overcoming limitations caused by atmospheric turbulence and sensor noise, we aim to produce high-resolution and high-fidelity images comparable to those obtained from space, thus opening new perspectives for the study of the universe.

Introduction

Obtaining high spatial resolution images is essential for detailed exploration of celestial structures and phenomena. Ground-based telescopes, despite technological advancements, face major challenges such as atmospheric-induced blurring and instrumental noise. These limitations hinder astronomers' ability to observe and finely analyze galaxies, stars, and other astrophysical objects.

Methodology

We have developed a three-step deconvolution framework that integrates deep learning techniques with classical methods:

1. Tikhonov Deconvolution

   We begin by applying a regularized Tikhonov deconvolution to solve the ill-posed inverse problem of image deconvolution. This step provides an initial estimate of the deconvolved image while controlling noise amplification.

2. Deep Learning Enhancement with SUNet

   Based on this initial estimate, we use a deep learning model called SUNet (Swin Transformer UNet). SUNet integrates Swin Transformer blocks within a UNet architecture, allowing it to capture both local and global features across multiple scales. Trained on high-quality astronomical images, the model learns the underlying structures and patterns present in astrophysical data.

3. Debiasing with Multi-Resolution Support

   Aware that neural networks can introduce subtle biases, we have integrated a debiasing step based on multi-resolution support using wavelet transforms. This technique identifies and corrects residual biases, ensuring the preservation of authentic astrophysical signals and accurate flux estimation.

Results

Comprehensive testing of our methodology has demonstrated significant advances over traditional methods:

• Resolution Recovery: The enhanced images show a notable increase in spatial resolution, allowing the detection of small-scale structures such as star-forming regions and stellar clusters that were previously unresolved.
• Generalization to Various Noise Conditions: The deep learning model exhibits a remarkable ability to generalize to images with varied noise characteristics, attesting to its robustness and adaptability to different observational datasets.
• Computational Efficiency: Our framework enables rapid deconvolution, processing each image in a matter of milliseconds. This efficiency is crucial for managing the large volumes of data from modern astronomical surveys.

Case Study: Galaxy Cluster Analysis

We applied our deconvolution method to a sample of ground-based images from the ESO Distant Cluster Survey (EDisCS). By enhancing these images, we conducted an in-depth analysis of the internal structures of galaxies within clusters at different redshifts. Our findings include:

• Detection of Star-Forming Clumps: We accurately identified and quantified "clumps" or star-forming regions within galactic disks, revealing variations correlated with properties such as disk color.
• New Astrophysical Insights: The improved resolution provided new information on environmental effects on galaxy evolution, particularly on mechanisms influencing star formation in cluster environments.

Conclusion

Our innovative approach represents a major advancement in ground-based astronomical imaging. By combining classical techniques with state-of-the-art deep learning models, we achieve high-resolution and high-fidelity images that open new opportunities for astrophysical research. This technology not only enhances current observational capabilities but also paves the way for future discoveries by maximizing the use of existing telescope infrastructures.

Data and Software Availability

In our commitment to contributing to the scientific community, we have made our codes and trained models available. Researchers can access these resources to reproduce our results and apply our methodology to their own datasets.

Future Perspectives

We plan to extend our technology to apply it to other areas of astrophysics, such as the study of exoplanets and large-scale structures of the universe. By continuously integrating the latest advances in artificial intelligence, we are committed to providing ever more powerful tools to the scientific community.