update readme with arxiv number

Author: tobias-liaudat

README.md

@@ -1,9 +1,9 @@
-[![image](https://img.shields.io/badge/GitHub-quantifai-brightgreen.svg?style=flat)](https://github.com/astro-informatics/quantifai) [![image](https://img.shields.io/badge/License-GPL-blue.svg?style=flat)](https://github.com/astro-informatics/quantifai/blob/main/LICENSE.txt) [![image](https://img.shields.io/badge/arXiv-0000.00000-red.svg?style=flat)]( https://arxiv.org/abs/0000.00000)  
+[![image](https://img.shields.io/badge/GitHub-quantifai-brightgreen.svg?style=flat)](https://github.com/astro-informatics/quantifai) [![image](https://img.shields.io/badge/License-GPL-blue.svg?style=flat)](https://github.com/astro-informatics/quantifai/blob/main/LICENSE.txt) [![image](https://img.shields.io/badge/arXiv-2312.00125-red.svg?style=flat)]( https://arxiv.org/abs/2312.00125)  
 
 
 # QuantifAI
 
-`quantifai` is a PyTorch-based open-source radio interferometric imaging reconstruction package with scalable Bayesian uncertainty quantification relying on data-driven (learned) priors. This package was used to produce the results of [Liaudat et al. 2023](https://arxiv.org/abs/0000.00000). The `quantifai` model relies on the data-driven convex regulariser from [Goujon et al. 2022](https://arxiv.org/abs/2211.12461).
+`quantifai` is a PyTorch-based open-source radio interferometric imaging reconstruction package with scalable Bayesian uncertainty quantification relying on data-driven (learned) priors. This package was used to produce the results of [Liaudat et al. 2023](https://arxiv.org/abs/2312.00125). The `quantifai` model relies on the data-driven convex regulariser from [Goujon et al. 2022](https://arxiv.org/abs/2211.12461).
 
 In this code, we bypass the need to perform Markov chain Monte Carlo (MCMC) sampling for Bayesian uncertainty quantification, and we rely on convex accelerated optimisation algorithms. The `quantifai` package also includes MCMC algorithms for posterior sampling as they were used to validate our approach.
 
@@ -62,21 +62,21 @@ The easiest way to get into using `quantifai` is to check the different notebook
 
 ## Reproducibility
 
-All the scripts and notebooks used to generate the plots of [Liaudat et al. 2023](https://arxiv.org/abs/0000.00000) can be found in the `paper/Liaudat2023/` directory and the data in the `data/` directory.
+All the scripts and notebooks used to generate the plots of [Liaudat et al. 2023](https://arxiv.org/abs/2312.00125) can be found in the `paper/Liaudat2023/` directory and the data in the `data/` directory.
 
 The most computationally intensive results of the paper can be obtained by running the two scripts in `paper/Liaudat2023/scripts/`, where `UQ_SKROCK_CRR.py` corresponds to the `QuantifAI` model and `UQ_SKROCK_wavelets.py` to the wavelet-based model. The rest of the results and plots can be generated by running the different notebooks in `paper/Liaudat2023/notebooks/`.
 
 
 ## Attribution
 
-Should this code be used in any way, we kindly request that the following article is referenced. A BibTeX entry for this reference may look like:
+Should this code be used in any way, we kindly request that the following article be referenced. A BibTeX entry for this reference may look like:
 
 ```
 @article{liaudat2023:quantifai, 
     author = {Tobías~I.~Liaudat and Matthijs~Mars and Matthew~A.~Price and Marcelo~Pereyra and Marta~M.~Betcke and Jason~D.~McEwen},
     title = {Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging},
     journal = "RAS Techniques and Instruments (RASTI), submitted",
-    eprint = "arXiv:0000.00000",
+    eprint = "arXiv:2312.00125",
     year = "2023",
 }
 ```