Story
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Key takeaway
New computer techniques can help telescopes see fainter, more distant objects in space, revealing more about the early universe.
Quick Explainer
The researchers developed a self-supervised deep learning technique called ASTERIS that leverages spatiotemporal information across multiple astronomical exposures to effectively denoise and enhance the detection of faint celestial objects. ASTERIS uses a specialized transformer-based neural network architecture to fuse data from aligned image sequences, selectively denoising the fainter regions while preserving the overall dynamic range. This approach allows ASTERIS to push the detection limits deeper compared to traditional methods like image co-addition, by better suppressing correlated noise sources that limit the sensitivity of astronomical imaging.
Deep Dive
Technical Deep Dive: Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Overview
The work presented a novel astronomical self-supervised transformer-based denoising algorithm called ASTERIS, which integrates spatiotemporal information across multiple exposures to improve detection limits in astronomical imaging. ASTERIS was benchmarked on mock data and validated on real data from the James Webb Space Telescope (JWST) and Subaru telescope, showing significant improvements over existing methods.
Problem & Context
- The detection limit of astronomical imaging is limited by various noise sources, including instrumental, sky background, and photon statistics
- While larger telescopes and advanced instrumentation can improve the signal-to-noise ratio (S/N), other background noise sources cannot be easily corrected
- Software signal processing algorithms have been used to improve S/N, such as aligning and co-adding multiple exposures, but this provides diminishing returns at greater depths
- Deep learning denoising methods have been employed, but they are limited by differences between synthetic and real data, as well as the complexity and variability of noise in astronomical imaging
Methodology
- ASTERIS extends the Noise2Noise concept to leverage spatiotemporal correlations across multiple exposures of the same target field
- It uses a tailored spatiotemporal learning strategy with a dedicated attention mechanism to fuse information from aligned exposures
- For self-supervised training, ASTERIS requires 16 exposures divided into two independent sets, which share the same underlying signal but have independent noise realizations
- The loss function combines mean squared error (MSE) between co-added input and target sets, and mean absolute error (MAE) between individual input-target pairs
- ASTERIS selectively operates on the fainter parts of each image by imposing a 3σ flux threshold, preserving the original dynamic range
Data & Experimental Setup
- ASTERIS was trained and tested using real JWST and Subaru telescope data, including the GLIMPSE, JOF, Ultra Deep Field Medium Band, and Medium bands, Mega Science programs
- For benchmarking, mock data was generated by injecting sources into real JWST images, ensuring independent and variable background realizations
- The performance of ASTERIS was compared to existing methods including co-addition, Gaussian smoothing, BM3D, BM4D, Neighbor2Neighbor, and Noise2Noise
Results
- On mock data, ASTERIS improved the detection limit by 1.0 magnitude at 90% completeness and purity compared to co-addition, while preserving the point spread function and photometric accuracy
- On real JWST and Subaru data, ASTERIS identified previously undetectable features, including low-surface-brightness galaxy structures and gravitationally-lensed arcs
- Applied to deep JWST images, ASTERIS identified three times more redshift ≳ 9 galaxy candidates, with rest-frame ultraviolet luminosity 1.0 magnitude fainter, than previous methods
Interpretation
- ASTERIS' spatiotemporal denoising approach effectively suppresses correlated noise, enabling the detection of fainter sources compared to existing methods
- The combination of MSE and MAE losses in the training process balances completeness and purity, reducing both missed detections and false positives
- The selective denoising of faint regions optimizes ASTERIS for detecting and characterizing the faintest astronomical sources
Limitations & Uncertainties
- The performance of ASTERIS is limited by the availability of training data, both in terms of the number of exposures and the diversity of observational conditions
- While ASTERIS generalizes well across JWST datasets, applying it to ground-based Subaru data required retraining to account for differences in atmospheric effects and instrumentation
What Comes Next
- The authors suggest exploring the application of ASTERIS to other astronomical imaging instruments and data types, such as those from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope
- Further improvements may be possible by incorporating additional physical constraints and domain knowledge into the neural network architecture and training process