Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model
This work addresses the challenge of building interpretable and physically motivated models for telescope data without needing special calibration, offering a significant performance improvement for astronomers.
The authors tackled the problem of modeling telescope instrumental response by introducing a differentiable optical forward model, shifting from pixel-based to wavefront-based modeling, which reduced reconstruction errors by 5 times at observation resolution and over 10 times for 3x super-resolution in a simplified space telescope setting.
We propose a paradigm shift in the data-driven modeling of the instrumental response field of telescopes. By adding a differentiable optical forward model into the modeling framework, we change the data-driven modeling space from the pixels to the wavefront. This allows to transfer a great deal of complexity from the instrumental response into the forward model while being able to adapt to the observations, remaining data-driven. Our framework allows a way forward to building powerful models that are physically motivated, interpretable, and that do not require special calibration data. We show that for a simplified setting of a space telescope, this framework represents a real performance breakthrough compared to existing data-driven approaches with reconstruction errors decreasing 5 fold at observation resolution and more than 10 fold for a 3x super-resolution. We successfully model chromatic variations of the instrument's response only using noisy broad-band in-focus observations.