louis citations

joss/paper.bib

@@ -272,4 +272,39 @@ @ARTICLE{Cantalloube2021
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
 
+@ARTICLE{Desdoigts2023,
+       author = {{Desdoigts}, Louis and {Pope}, Benjamin J.~S. and {Dennis}, Jordan and {Tuthill}, Peter G.},
+        title = "{Differentiable optics with {\ensuremath{\partial}}Lux: I{\textemdash}deep calibration of flat field and phase retrieval with automatic differentiation}",
+      journal = {Journal of Astronomical Telescopes, Instruments, and Systems},
+     keywords = {detectors, phase retrieval, simulations, diffractive optics, Astrophysics - Instrumentation and Methods for Astrophysics},
+         year = 2023,
+        month = apr,
+       volume = {9},
+          eid = {028007},
+        pages = {028007},
+          doi = {10.1117/1.JATIS.9.2.028007},
+archivePrefix = {arXiv},
+       eprint = {2406.08703},
+ primaryClass = {astro-ph.IM},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2023JATIS...9b8007D},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@ARTICLE{Desdoigts2024,
+       author = {{Desdoigts}, Louis and {Pope}, Benjamin and {Gully-Santiago}, Michael and {Tuthill}, Peter},
+        title = "{Differentiable Optics with dLux II: Optical Design Maximising Fisher Information}",
+      journal = {arXiv e-prints},
+     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
+         year = 2024,
+        month = jun,
+          eid = {arXiv:2406.08704},
+        pages = {arXiv:2406.08704},
+          doi = {10.48550/arXiv.2406.08704},
+archivePrefix = {arXiv},
+       eprint = {2406.08704},
+ primaryClass = {astro-ph.IM},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240608704D},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
 

joss/paper.md

@@ -42,7 +42,7 @@ One of the foundational problems in optical astronomy is that of imaging scenes
 While there are many data-driven approaches to nonparametrically inferring and subtracting this PSF [@Cantalloube2021], the motivation for our work here is to use principled deterministic physics to model optical systems; to perform high-dimensional inferences from data, jointly about telescopes and the scenes they observe; to train neural networks to model electronics together with optics; and to produce principled, high-dimensional designs for telescope hardware. These problems necessitate a physical optics model which is fast and differentiable.
 
 <!-- what is dLux -->
-In this paper we introduce `dLux`[^dlux], an open-source Python package for differentiable physical optics simulation. Leveraging `jax` [@jax] for automatic differentiation and vectorization, it deploys natively on CPU, GPU, and parallelized HPC environments. `dLux` can perform Fourier and Fresnel optical simulations using matrix and FFT based propagation [@Soummer2007], as well as simulate linear and nonlinear detector effects. 
+In this paper we introduce `dLux`[^dlux], an open-source Python package for differentiable physical optics simulation. Leveraging `jax` [@jax] for automatic differentiation and vectorization, it deploys natively on CPU, GPU, and parallelized HPC environments. `dLux` can perform Fourier and Fresnel optical simulations using matrix and FFT based propagation [@Soummer2007], as well as simulate linear and nonlinear detector effects. In published work so far, `dLux` has been used to demonstrate inference of pixel sensitivities jointly with optical aberrations in imaging data [@Desdoigts2023] and to demonstrate principled optimal experimental design of a telescope by direct optimization of the Fisher Information Matrix [@Desdoigts2024]. 
 
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