Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking data-driven point spread function modeling with a
  differentiable optical model   In astronomy, upcoming space telescopes with wide-field optical instruments
have a spatially varying point spread function (PSF). Certain scientific goals
require a high-fidelity estimation of the PSF at target positions where no
direct measurement of the PSF is provided. Even though observations of the PSF
are available at some positions of the field of view (FOV), they are
undersampled, noisy, and integrated in wavelength in the instrument's passband.
PSF modeling requires building a model from these observations that can infer a
super-resolved PSF at any wavelength and any position in the FOV. Current
data-driven PSF models can tackle spatial variations and super-resolution, but
are not capable of capturing chromatic variations. Our model, coined WaveDiff,
proposes a paradigm shift in the data-driven modeling of the point spread
function 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. The proposed model relies on efficient automatic
differentiation technology as well as modern stochastic first-order
optimization techniques recently developed by the thriving machine-learning
community. Our framework paves the way to building powerful models that are
physically motivated and do not require special calibration data. This paper
demonstrates the WaveDiff model on a simplified setting of a space telescope.
The proposed framework represents a performance breakthrough with respect to
existing data-driven approaches. The pixel reconstruction errors decrease
6-fold at observation resolution and 44-fold for a 3x super-resolution. The
ellipticity errors are reduced by a factor of at least 20 and the size error by
a factor of more than 250. By only using noisy broad-band in-focus
observations, we successfully capture the PSF chromatic variations due to
diffraction.

15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math