diff --git a/joss/paper.bib b/joss/paper.bib
index 0e4c7154..8c6642ee 100644
--- a/joss/paper.bib
+++ b/joss/paper.bib
@@ -53,6 +53,26 @@ @INPROCEEDINGS{poppy
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
 
+@ARTICLE{Soummer2007,
+       author = {{Soummer}, R. and {Pueyo}, L. and {Sivaramakrishnan}, A. and {Vanderbei}, R.~J.},
+        title = "{Fast computation of Lyot-style coronagraph propagation}",
+      journal = {Optics Express},
+     keywords = {Astrophysics},
+         year = 2007,
+        month = jan,
+       volume = {15},
+       number = {24},
+        pages = {15935},
+          doi = {10.1364/OE.15.015935},
+archivePrefix = {arXiv},
+       eprint = {0711.0368},
+ primaryClass = {astro-ph},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2007OExpr..1515935S},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+
+
 @article{Wechsler24,
 author = {Felix Wechsler and Carlo Gigli and Jorge Madrid-Wolff and Christophe Moser},
 journal = {Opt. Express},
diff --git a/joss/paper.md b/joss/paper.md
index e36aebf4..3aa1f391 100644
--- a/joss/paper.md
+++ b/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 [@Soumm, 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. 
 
 <!-- more here -->