# Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model

arXiv: https://arxiv.org/abs/2111.12541 · HTML: https://mlsanity.com/paper/2111.12541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder](https://mlsanity.com/paper/2001.11716/) (2020)

## Built on by (most cited first, 3 of 3)

- [Differentiable and accelerated spherical harmonic and Wigner transforms](https://mlsanity.com/paper/2311.14670/) (2023)
- [Rethinking data-driven point spread function modeling with a differentiable optical model](https://mlsanity.com/paper/2203.04908/) (2022)
- [Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies](https://mlsanity.com/paper/2306.07996/) (2023)

## Abstract (arXiv)

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.