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.
翻译:我们建议对望远镜工具响应领域的数据驱动模型进行范式转变。 通过在模型框架中添加一个不同的光学前向模型,我们将数据驱动模型空间从像素改为波浪前位。 这样可以将大量复杂因素从工具响应转移到前方模型, 同时能够适应观测, 剩下的数据驱动。 我们的框架允许在建设强大的、 物理驱动的、 可解释的和不需要特殊校准数据的模型方面向前迈出一步。 我们显示,对于简化的空间望远镜设置而言,这个框架与现有的数据驱动方法相比,是一种真正的性能突破,其重建错误在观测分辨率上减少5倍,3x超级分辨率减少10倍以上。 我们成功地模拟了仪器反应的色变模式,但只能使用焦距宽频的宽带观测。