added JOSS paper

deepCR/paper.bib

@@ -0,0 +1,35 @@
+@ARTICLE{deepcr,
+   author = {{Zhang}, K. and {Bloom}, J.~S.},
+    title = "{deepCR: Cosmic Ray Rejection with Deep Learning}",
+  journal = {arXiv e-prints},
+archivePrefix = "arXiv",
+   eprint = {1907.09500},
+ primaryClass = "astro-ph.IM",
+ keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition},
+     year = 2019,
+    month = jul,
+   adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190709500Z},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@ARTICLE{lacosmic,
+   author = {{van Dokkum}, P.~G.},
+    title = "{Cosmic-Ray Rejection by Laplacian Edge Detection}",
+  journal = {\pasp},
+   eprint = {astro-ph/0108003},
+ keywords = {Instrumentation: Detectors, Methods: Data Analysis-techniques: image processing},
+     year = 2001,
+    month = nov,
+   volume = 113,
+    pages = {1420-1427},
+      doi = {10.1086/323894},
+   adsurl = {https://ui.adsabs.harvard.edu/abs/2001PASP..113.1420V},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@inproceedings{pytorch,
+    title = {Automatic differentiation in PyTorch},
+   author = {Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
+booktitle = {NIPS-Workshop},
+     year = {2017}
+}

paper.md

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+---
+title: 'deepCR: Cosmic Rejection with Deep Learning'
+tags:
+  - Python
+  - Pytorch
+  - astronomy
+  - image processing
+  - cosmic ray
+  - deep learning
+
+authors:
+  - name: Keming Zhang
+    orcid: 0000-0002-9870-5695
+    affiliation: 1 # (Multiple affiliations must be quoted)
+  - name: Joshua S. Bloom
+    orcid: 0000-0002-7777-216X
+    affiliation: "1, 2"
+affiliations:
+ - name: Department of Astronomy, University of California, Berkeley
+   index: 1
+ - name: Lawrence Berkeley National Laboratory
+   index: 2
+date: 5 August 2019
+bibliography: paper.bib
+---
+
+# Summary
+
+Astronomical imaging and spectroscopy data are frequently corrupted by
+"cosmic rays" (CR) which are high energy charged particles that are instrumental, 
+terrestrial, or cosmic in origin. When such particles pass through solid state 
+detectors, such as charged coupled devices (CCDs), they create excess 
+flux in the pixels hit which lead to artifacts in images. These 
+artifacts must be identified and either masked or replaced, before 
+further scientific analysis could be done on the image data. It is straightforward 
+to identify these artifacts when multiple exposures of the same field are 
+taken. In such cases, a median image could be calculated from aligned single 
+exposures, effectively creating a CR-free image. Each one of the exposures 
+is then compared with the median image to identify the cosmic rays. However, 
+when CCD read-out times are non-negligible, or when sources of 
+interest are transient or variable, cosmic ray rejection with multiple 
+exposures can be sub-optimal or infeasible. These cases would require specialized
+algorithms to detect cosmic rays in single images.
+
+``deepCR`` is a Python package for single frame cosmic ray rejection which is
+based on deep learning and written with the Pytorch framework [@pytorch]. 
+Since ``deepCR`` is based on deep learning, different models trained on
+data taken with different instrument configurations are required, when applied to different
+data. The current version of ``deepCR`` is prepackaged with model for Hubble 
+Space Telescope ACS/WFC imaging data, and we expect models available 
+to grow with contribution from the community. We plan to host a "model zoo"
+ which enables ``deepCR`` to work across different instrument configurations.
+
+The API of ``deepCR`` includes functionality for both applying models and 
+training models. To apply an available model, ``deepCR`` takes in an input image
+ and produces a cosmic ray mask and an "inpainted" image, with 
+ the artifact pixels replaced with ``deepCR`` predictions. To train a new model, 
+ users would feed in custom dataset to the training API, which is automated.
+ ``deepCR`` works with both CPU, which is well-threaded at application time, and GPU.
+ On GPU, training a new model takes as short as 20 minutes, 
+ while applying ``deepCR`` on a 10 Mpix image requires less than 0.2 second, 
+ orders of magnitude faster than current state of the art ``LACosmic`` [@lacosmic].
+![Example of cosmic ray contaminated image cutouts (first row), deepCR
+cosmic ray mask predictions (middle row), and original image with artifact
+pixels replaced with deepCR predictions (last row).](imgs/postage-sm.png)
+
+
+In the paper accompanying ``deepCR`` [@deepcr], the authors showed that 
+on Hubble Space Telescope (HST) ACS/WFC data, 
+``deepCR`` is more robost, and at least as fast as the current 
+state-of-the-art single frame cosmic ray rejection package, ``LACosmic``. The API 
+of ``deepCR`` serve as a drop in replacement for ``LACosmic``, 
+so that users may experiment with different packages easily. At 
+reasonable false detection rates, ``deepCR`` achieved near perfect
+cosmic ray detection in extragalactic and globular cluster fields, and above 
+90% in more difficult dense stellar fields in nearby resolved galaxies. 
+Since HST imaging is among the hardest cosmic ray rejection to be
+solved, ``deepCR`` would work well across many different instrument set-ups,
+including ground based imaging and spectroscopy. The combination of 
+speed and accuracy of ``deepCR`` allows astronomers to potentially save
+large amounts of precious observational and computational resources.
+
+# Acknowledgements
+
+This work was supported by a Gordon and Betty Moore Foundation Data-Driven Discovery grant.
+
+# References