joss paper: add refs

paper/paper.bib

@@ -1,4 +1,4 @@
-@article{1974A&AS...15..417H,
+@ARTICLE{Hogbom_1974,
    author = {{H{\"o}gbom}, J.~A.},
     title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}",
   journal = {\aaps},
@@ -10,7 +10,20 @@ @article{1974A&AS...15..417H
   adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
 
-@book{2007ASPC..376..127M,
+@ARTICLE{Clark_1980,
+   author = {{Clark}, B.~G.},
+    title = "{An efficient implementation of the algorithm 'CLEAN'}",
+  journal = {\aap},
+ keywords = {Algorithms, Data Processing, Image Processing, Radio Astronomy, Run Time (Computers), Astronomical Photography, Computer Techniques, Fast Fourier Transformations, Iterative Solution, Very Large Array (Vla)},
+     year = 1980,
+    month = sep,
+   volume = 89,
+    pages = {377},
+   adsurl = {https://ui.adsabs.harvard.edu/abs/1980A%26A....89..377C},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@INPROCEEDINGS{McMullin_2007,
        author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and
          {Golap}, K.},
         title = "{CASA Architecture and Applications}",
@@ -22,4 +35,23 @@ @book{2007ASPC..376..127M
         pages = {127},
        adsurl = {https://ui.adsabs.harvard.edu/abs/2007ASPC..376..127M},
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@ARTICLE{CASA_2022,
+       author = {{CASA Team} and {Bean}, Ben and {Bhatnagar}, Sanjay and {Castro}, Sandra and {Donovan Meyer}, Jennifer and {Emonts}, Bjorn and {Garcia}, Enrique and {Garwood}, Robert and {Golap}, Kumar and {Gonzalez Villalba}, Justo and {Harris}, Pamela and {Hayashi}, Yohei and {Hoskins}, Josh and {Hsieh}, Mingyu and {Jagannathan}, Preshanth and {Kawasaki}, Wataru and {Keimpema}, Aard and {Kettenis}, Mark and {Lopez}, Jorge and {Marvil}, Joshua and {Masters}, Joseph and {McNichols}, Andrew and {Mehringer}, David and {Miel}, Renaud and {Moellenbrock}, George and {Montesino}, Federico and {Nakazato}, Takeshi and {Ott}, Juergen and {Petry}, Dirk and {Pokorny}, Martin and {Raba}, Ryan and {Rau}, Urvashi and {Schiebel}, Darrell and {Schweighart}, Neal and {Sekhar}, Srikrishna and {Shimada}, Kazuhiko and {Small}, Des and {Steeb}, Jan-Willem and {Sugimoto}, Kanako and {Suoranta}, Ville and {Tsutsumi}, Takahiro and {van Bemmel}, Ilse M. and {Verkouter}, Marjolein and {Wells}, Akeem and {Xiong}, Wei and {Szomoru}, Arpad and {Griffith}, Morgan and {Glendenning}, Brian and {Kern}, Jeff},
+        title = "{CASA, the Common Astronomy Software Applications for Radio Astronomy}",
+      journal = {\pasp},
+     keywords = {Single-dish antennas, Aperture synthesis, Radio astronomy, Radio interferometry, Long baseline interferometry, Astronomy software, Open source software, Software documentation, Astronomy data reduction, Astronomy data analysis, 1460, 53, 1338, 1346, 932, 1855, 1866, 1869, 1861, 1858, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics},
+         year = 2022,
+        month = nov,
+       volume = {134},
+       number = {1041},
+          eid = {114501},
+        pages = {114501},
+          doi = {10.1088/1538-3873/ac9642},
+archivePrefix = {arXiv},
+       eprint = {2210.02276},
+ primaryClass = {astro-ph.IM},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2022PASP..134k4501C},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }

paper/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'MPoL: A Python package for interferometric imaging'
+title: 'MPoL: A Python package for scalable, nonparametric interferometric imaging'
 tags:
   - Python
   - astronomy
@@ -14,7 +14,7 @@ authors:
     # affiliation: "1, 2" # (Multiple affiliations must be quoted)
   - name: Jeff Jennings
     orcid: 0000-0002-7032-2350
-    corresponding: true
+    # corresponding: true
     affiliation: 2
   # - name:Brianna Zawadzki
   #   orcid: 0000-0001-9319-1296
@@ -41,7 +41,7 @@ Interferometric imaging is the process of recovering a spatial domain image from
 
 # Statement of need
 
-Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@1974A&AS...15..417H], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@2007ASPC..376..127M]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. 
+Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@Hogbom_1974; @Clark_1980], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@McMullin_2007; @CASA_2022]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. 
 
 `MPoL` is a statistically robust, nonparametric modeling approach to interferometric imaging in a user-friendly, well-documented package that is computationally performant. The software is designed to be applied to reconstruction of an individual image or an entire 'cube' of tens to hundreds of images of an astronomical source observed at different frequencies. The images obtained are of simultaneously higher spatial resolution and sensitivity than their counterparts produced by `CLEAN`. Programatically, `MPoL` is built on `PyTorch`, using its auto-differentiation capabilities to drive likelihood optimization with gradient descent and its parallelization support to optionally accelerate the imaging workflow on GPUs and TPUs. The imaging framework in `MPoL` is also flexible, with the ability to easily add alternative or additional priors into likelihood calculation. Extensions to the core functionality are actively developed, such as the recent implementation of parametric inference with `Pyro`, as are further optimizations to the core routines.