update readme

README.md

@@ -2,7 +2,7 @@
 
 Python codes to extract the underlying matter density map from a 21 cm intensity field, making use of a convolutional neural network (CNN) with the U-Net architecture. Implemented in Pytorch. The astrophysical parameters of the simulations can also be predicted with a secondary CNN. The simulations of matter density and 21 cm maps have been performed with the code [21cmFAST](https://github.com/andreimesinger/21cmFAST/commits/master).
 
-See the paper [arXiv:2006.14305](https://arxiv.org/abs/2006.14305) for more details.
+See the paper [ApJ 907 44 (2021)](https://iopscience.iop.org/article/10.3847/1538-4357/abd245), [arXiv:2006.14305](https://arxiv.org/abs/2006.14305) for more details.
 
 ## Description of the scripts
 
@@ -12,7 +12,7 @@ The files included are the following:
 
 * `HI2DM.py`: main script for training and testing the U-Net network to recover the matter density field from 21 cm maps.
 
-* `HI2Astro.py`: script for training and testing a secondary CNN to predict the astrophysical parameters of the 21 cm maps. It is optional to employ the pre-trained weights of the encoder in the U-Net, trained running `HI2DM.py`. 
+* `HI2Astro.py`: script for training and testing a secondary CNN to predict the astrophysical parameters of the 21 cm maps. It is optional to employ the pre-trained weights of the encoder in the U-Net, trained running `HI2DM.py`.
 
 * `Plotter.py`: driver for plotting several outputs and statistics. Most of routines are defined in `Source/plot_routines.py`.
 
@@ -52,4 +52,8 @@ You may want to run the scripts in the following order:
 
 ## Contact
 
-For comments, questions etc. you can reach me at <[email protected]>
+If you use the code, please link this repository and cite [ApJ 907 44 (2021)](https://iopscience.iop.org/article/10.3847/1538-4357/abd245).
+
+## Contact
+
+For comments, questions etc. you can reach me at <[email protected]>