diff --git a/CITATION.bib b/CITATION.bib
new file mode 100644
index 0000000..5fe4dbe
--- /dev/null
+++ b/CITATION.bib
@@ -0,0 +1,9 @@
+@software{pidgan:2024abc,
+  author    = "Lucio Anderlini and Matteo Barbetti",
+  title     = "{PIDGAN: GAN-based models to flash-simulate the LHCb PID detectors}",
+  version   = "v0.2.0",
+  url       = "https://github.com/mbarbetti/pidgan",
+  doi       = "10.5281/zenodo.10463728",
+  publisher = "Zenodo",
+  year      = "2024",
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index c468405..c3ebc35 100644
--- a/README.md
+++ b/README.md
@@ -34,40 +34,123 @@
 [![Docker - Version](https://img.shields.io/docker/v/mbarbetti/pidgan?label=docker)](https://hub.docker.com/r/mbarbetti/pidgan)
 -->
 
-### Generative Adversarial Networks
+### What is PIDGAN?
+
+PIDGAN is a Python package built upon TensorFlow 2 to provide ready-to-use implementations for several GAN algorithms (listed in [this table](#generative-adversarial-networks)). The package was originally designed to simplify the training and optimization of GAN-based models for the _Particle Identification_ (PID) system of the LHCb experiment. Today, PIDGAN is a versatile package that can be employed in a wide range of _High Energy Physics_ (HEP) applications and, in general, whenever one has anything to do with tabular data and aims to learn the conditional probability distributions of a set of target features. This package is one of the building blocks to define a _Flash Simulation_ framework of the LHCb experiment [1].
+
+#### PIDGAN is (almost) all you need (for flash-simulation)
+
+Standard techniques for simulations consume tons of CPU hours in reproducing _all_ the radiation-matter interactions occurring within a HEP detector when traversed by primary and secondary particles. Directly transforming generated particles into analysis-level objects allows Flash Simulation strategies to speed up significantly the simulation production, up to x1000 [1]. Such transformations can be defined by using _Generative Adversarial Networks_ (GAN) [[2][gan]] trained to take into account the kinematics of the traversing particles and the detection conditions (e.g., magnet polarity, occupancy).
+
+GANs rely on the simultaneous (adversarial) training of two neural networks called _generator_ and _discriminator_, whose competition ends once reached the [Nash equilibrium](https://en.wikipedia.org/wiki/Nash_equilibrium). At this point, the generator can be used as simulator to generate new data according to the conditional probability distributions learned during the training [[3][cgan]]. By relying on the TensorFlow and Keras APIs, PIDGAN allows to define and train a GAN model with no more than 20 code lines.
+
+```python
+from pidgan.players.generators import Generator
+from pidgan.players.discriminators import Discriminator
+from pidgan.algorithms import GAN
+
+x = ... # conditions
+y = ... # targets
+
+G = Generator(
+  output_dim=y.shape[1],
+  latent_dim=64,
+  output_activation="linear",
+)
+
+D = Discriminator(
+  output_dim=1,
+  output_activation="sigmoid",
+)
+
+model = GAN(generator=G, discriminator=D)
+model.compile(
+  metrics=["accuracy"],
+  generator_optimizer="rmsprop",
+  discriminator_optimizer="rmsprop",
+)
+
+model.fit(x, y, batch_size=256, epochs=100)
+```
+
+### Installation guide
+
+#### First steps
+
+Before installing PIDGAN, we suggest preparing a fully operational TensorFlow installation by following the instructions described in the [dedicated guide](https://www.tensorflow.org/install). If your device is equipped with one of the NVIDIA GPU cards supported by TensorFlow (see [Hardware requirements](https://www.tensorflow.org/install/pip#hardware_requirements)), do not forget to verify the correct installation of the libraries for hardware acceleration by running:
+
+```bash
+python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
+```
+
+If the equipped GPU card is not included in the list printed by the previous command, your device and/or Python environment may be misconfigured. Please refer to [this table](https://www.tensorflow.org/install/source#gpu) for the correct configuration of CUDA Toolkit and cuDNN requested by the different TensorFlow versions.
+
+#### How to install
+
+PIDGAN has a minimal list of [requirements](https://github.com/mbarbetti/pidgan/blob/main/requirements/base.txt):
+
+- Python >=3.7, <3.13
+- TensorFlow >= 2.8, < 2.17
+- scikit-learn >= 1.0, < 1.6
+- NumPy < 2.0
+- Hopaas client (https://github.com/landerlini/hopaas_client)
+
+The easiest way to install PIDGAN is via `pip`:
+
+```bash
+pip install pidgan
+```
+
+In addition, since `hopaas_client` is not available on [PyPI](https://pypi.org), you need to install it manually to unlock the complete set of PIDGAN functionalities:
+
+```bash
+pip install git+https://github.com/landerlini/hopaas_client
+```
+
+#### Optional dependencies
+
+Standard HEP applications may need additional packages for data management, results visualization/validation, and model export. PIDGAN and any [additional requirements](https://github.com/mbarbetti/pidgan/blob/main/requirements/hep.txt) potentially useful in HEP can be installed via `pip` in one shot:
+
+```bash
+pip install pidgan[hep]
+```
+
+### Models available
+
+#### Generative Adversarial Networks
 
 | Algorithms* | Source | Avail | Test | Lipschitz** | Refs | Tutorial |
 |:-----------:|:------:|:-----:|:----:|:-----------:|:----:|:--------:|
-| GAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/GAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/GAN.py) | ✅ | ✅ | ❌ | [1][1], [8][8], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-GAN-LHCb_RICH.ipynb) |
-| BceGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN.py) | ✅ | ✅ | ❌ | [2][2], [8][8], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN-LHCb_RICH.ipynb) |
-| LSGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/LSGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/LSGAN.py) | ✅ | ✅ | ❌ | [3][3], [8][8], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-LSGAN-LHCb_RICH.ipynb) |
-| WGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN.py) | ✅ | ✅ | ✅ | [4][4], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN-LHCb_RICH.ipynb) |
-| WGAN-GP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN_GP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN_GP.py) | ✅ | ✅ | ✅ | [5][5], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_GP-LHCb_RICH.ipynb) |
-| CramerGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/CramerGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/CramerGAN.py) | ✅ | ✅ | ✅ | [6][6], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-CramerGAN-LHCb_RICH.ipynb) |
-| WGAN-ALP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN_ALP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN_ALP.py) | ✅ | ✅ | ✅ | [7][7], [9][9] |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_ALP-LHCb_RICH.ipynb) |
-| BceGAN-GP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN_GP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN_GP.py) | ✅ | ✅ | ✅ | [2][2], [5][5], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN_GP-LHCb_RICH.ipynb) |
-| BceGAN-ALP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN_ALP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN_ALP.py) | ✅ | ✅ | ✅ | [2][2], [7][7], [9][9] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN_ALP-LHCb_RICH.ipynb) |
-
-*each GAN algorithm is designed to operate taking __conditions__ as input [[10][10]]
+| GAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/GAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/GAN.py) | ✅ | ✅ | ❌ | [2][gan], [10][pre-wgan], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-GAN-LHCb_RICH.ipynb) |
+| BceGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN.py) | ✅ | ✅ | ❌ | [4][dcgan], [10][pre-wgan], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN-LHCb_RICH.ipynb) |
+| LSGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/LSGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/LSGAN.py) | ✅ | ✅ | ❌ | [5][lsgan], [10][pre-wgan], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-LSGAN-LHCb_RICH.ipynb) |
+| WGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN.py) | ✅ | ✅ | ✅ | [6][wgan], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN-LHCb_RICH.ipynb) |
+| WGAN-GP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN_GP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN_GP.py) | ✅ | ✅ | ✅ | [7][wgan-gp], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_GP-LHCb_RICH.ipynb) |
+| CramerGAN | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/CramerGAN.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/CramerGAN.py) | ✅ | ✅ | ✅ | [8][cramer-gan], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-CramerGAN-LHCb_RICH.ipynb) |
+| WGAN-ALP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/WGAN_ALP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/WGAN_ALP.py) | ✅ | ✅ | ✅ | [9][wgan-alp], [11][gan-tricks] |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_ALP-LHCb_RICH.ipynb) |
+| BceGAN-GP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN_GP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN_GP.py) | ✅ | ✅ | ✅ | [4][dcgan], [7][wgan-gp], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN_GP-LHCb_RICH.ipynb) |
+| BceGAN-ALP | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k2/BceGAN_ALP.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/k3/BceGAN_ALP.py) | ✅ | ✅ | ✅ | [4][dcgan], [9][wgan-alp], [11][gan-tricks] | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN_ALP-LHCb_RICH.ipynb) |
+
+*each GAN algorithm is designed to operate taking __conditions__ as input [[3][cgan]]
 
 **the GAN training is regularized to ensure that the discriminator encodes a 1-Lipschitz function
 
-### Generators
+#### Generators
 
 | Players | Source | Avail | Test | Skip conn | Refs |
 |:-------:|:------:|:-----:|:----:|:---------:|:----:|
-| Generator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k2/Generator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k3/Generator.py) | ✅ | ✅ | ❌ | [1][1], [10][10] |
-| ResGenerator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k2/ResGenerator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k3/ResGenerator.py) | ✅ | ✅ | ✅ | [1][1], [10][10], [11][11] |
+| Generator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k2/Generator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k3/Generator.py) | ✅ | ✅ | ❌ | [2][gan], [3][cgan] |
+| ResGenerator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k2/ResGenerator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/k3/ResGenerator.py) | ✅ | ✅ | ✅ | [2][gan], [3][cgan], [12][skip-conn] |
 
-### Discriminators
+#### Discriminators
 
 | Players | Source | Avail | Test | Skip conn | Aux proc | Refs |
 |:-------:|:------:|:-----:|:----:|:---------:|:--------:|:----:|
-| Discriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/Discriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/Discriminator.py) | ✅ | ✅ | ❌ | ❌ | [1][1], [9][9], [10][10] |
-| ResDiscriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/ResDiscriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/ResDiscriminator.py) | ✅ | ✅ | ✅ | ❌ | [1][1], [9][9], [10][10], [11][11] |
-| AuxDiscriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/AuxDiscriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/AuxDiscriminator.py) | ✅ | ✅ | ✅ | ✅ | [1][1], [9][9], [10][10], [11][11], [12][12] |
+| Discriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/Discriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/Discriminator.py) | ✅ | ✅ | ❌ | ❌ | [2][gan], [3][cgan], [11][gan-tricks] |
+| ResDiscriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/ResDiscriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/ResDiscriminator.py) | ✅ | ✅ | ✅ | ❌ | [2][gan], [3][cgan], [11][gan-tricks], [12][skip-conn] |
+| AuxDiscriminator | [`k2`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k2/AuxDiscriminator.py)/[`k3`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/k3/AuxDiscriminator.py) | ✅ | ✅ | ✅ | ✅ | [2][gan], [3][cgan], [11][gan-tricks], [12][skip-conn], [13][aux-feat] |
 
-### Other players
+#### Other players
 
 | Players | Source | Avail | Test | Skip conn | Aux proc | Multiclass |
 |:-------:|:------:|:-----:|:----:|:---------:|:--------:|:---------:|
@@ -79,31 +162,52 @@
 | AuxMultiClassifier | [`src`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/classifiers/AuxMultiClassifier.py) | ✅ | ✅ | ✅ | ✅ | ✅ |
 
 ### References
-1. I.J. Goodfellow _et al._, "Generative Adversarial Networks", [arXiv:1406.2661][1]
-2. A. Radford, L. Metz, S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", [arXiv:1511.06434][2]
-3. X. Mao _et al._, "Least Squares Generative Adversarial Networks", [arXiv:1611.04076][3]
-4. M. Arjovsky, S. Chintala, L. Bottou, "Wasserstein GAN", [arXiv:1701.07875][4]
-5. I. Gulrajani _et al._, "Improved Training of Wasserstein GANs", [arXiv:1704.00028][5]
-6. M.G. Bellemare _et al._, "The Cramer Distance as a Solution to Biased Wasserstein Gradients", [arXiv:1705.10743][6]
-7. D. Terjék, "Adversarial Lipschitz Regularization", [arXiv:1907.05681][7]
-8. M. Arjovsky, L. Bottou, "Towards Principled Methods for Training Generative Adversarial Networks", [arXiv:1701.04862][8]
-9. T. Salimans _et al._, "Improved Techniques for Training GANs", [arXiv:1606.03498][9]
-10. M. Mirza, S. Osindero, "Conditional Generative Adversarial Nets", [arXiv:1411.1784][10]
-11. K. He _et al._, "Deep Residual Learning for Image Recognition", [arXiv:1512.03385][11]
-12. A. Rogachev, F. Ratnikov, "GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response", [arXiv:2207.06329][12]
-
-[1]: https://arxiv.org/abs/1406.2661
-[2]: https://arxiv.org/abs/1511.06434
-[3]: https://arxiv.org/abs/1611.04076
-[4]: https://arxiv.org/abs/1701.07875
-[5]: https://arxiv.org/abs/1704.00028
-[6]: https://arxiv.org/abs/1705.10743
-[7]: https://arxiv.org/abs/1907.05681
-[8]: https://arxiv.org/abs/1701.04862
-[9]: https://arxiv.org/abs/1606.03498
-[10]: https://arxiv.org/abs/1411.1784
-[11]: https://arxiv.org/abs/1512.03385
-[12]: https://arxiv.org/abs/2207.06329
+1. M. Barbetti, "The flash-simulation paradigm and its implementation based on Deep Generative Models for the LHCb experiment at CERN", PhD thesis, University of Firenze, 2024
+2. I.J. Goodfellow _et al._, "Generative Adversarial Networks", [arXiv:1406.2661][gan]
+3. M. Mirza, S. Osindero, "Conditional Generative Adversarial Nets", [arXiv:1411.1784][cgan]
+4. A. Radford, L. Metz, S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", [arXiv:1511.06434][dcgan]
+5. X. Mao _et al._, "Least Squares Generative Adversarial Networks", [arXiv:1611.04076][lsgan]
+6. M. Arjovsky, S. Chintala, L. Bottou, "Wasserstein GAN", [arXiv:1701.07875][wgan]
+7. I. Gulrajani _et al._, "Improved Training of Wasserstein GANs", [arXiv:1704.00028][wgan-gp]
+8. M.G. Bellemare _et al._, "The Cramer Distance as a Solution to Biased Wasserstein Gradients", [arXiv:1705.10743][cramer-gan]
+9. D. Terjék, "Adversarial Lipschitz Regularization", [arXiv:1907.05681][wgan-alp]
+10. M. Arjovsky, L. Bottou, "Towards Principled Methods for Training Generative Adversarial Networks", [arXiv:1701.04862][pre-wgan]
+11. T. Salimans _et al._, "Improved Techniques for Training GANs", [arXiv:1606.03498][gan-tricks]
+12. K. He _et al._, "Deep Residual Learning for Image Recognition", [arXiv:1512.03385][skip-conn]
+13. A. Rogachev, F. Ratnikov, "GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response", [arXiv:2207.06329][aux-feat]
+
+[gan]: https://arxiv.org/abs/1406.2661
+[dcgan]: https://arxiv.org/abs/1511.06434
+[lsgan]: https://arxiv.org/abs/1611.04076
+[wgan]: https://arxiv.org/abs/1701.07875
+[wgan-gp]: https://arxiv.org/abs/1704.00028
+[cramer-gan]: https://arxiv.org/abs/1705.10743
+[wgan-alp]: https://arxiv.org/abs/1907.05681
+[pre-wgan]: https://arxiv.org/abs/1701.04862
+[gan-tricks]: https://arxiv.org/abs/1606.03498
+[cgan]: https://arxiv.org/abs/1411.1784
+[skip-conn]: https://arxiv.org/abs/1512.03385
+[aux-feat]: https://arxiv.org/abs/2207.06329
 
 ### Credits
-Most of the GAN algorithms are an evolution of what provided by the [mbarbetti/tf-gen-models](https://github.com/mbarbetti/tf-gen-models) repository. The `BceGAN` model is freely inspired by the TensorFlow tutorial [Deep Convolutional Generative Adversarial Network](https://www.tensorflow.org/tutorials/generative/dcgan) and the Keras tutorial [Conditional GAN](https://keras.io/examples/generative/conditional_gan). The `WGAN_ALP` model is an adaptation of what provided by the [dterjek/adversarial_lipschitz_regularization](https://github.com/dterjek/adversarial_lipschitz_regularization) repository.
+Most of the GAN algorithms are an evolution of what provided by the [mbarbetti/tf-gen-models](https://github.com/mbarbetti/tf-gen-models) repository. The BceGAN model is freely inspired by the TensorFlow tutorial [Deep Convolutional Generative Adversarial Network](https://www.tensorflow.org/tutorials/generative/dcgan) and the Keras tutorial [Conditional GAN](https://keras.io/examples/generative/conditional_gan). The WGAN-ALP model is an adaptation of what provided by the [dterjek/adversarial_lipschitz_regularization](https://github.com/dterjek/adversarial_lipschitz_regularization) repository.
+
+### Citing PIDGAN
+To cite this repository:
+
+```bibtex
+@software{pidgan:2024abc,
+  author    = "Lucio Anderlini and Matteo Barbetti",
+  title     = "{PIDGAN: GAN-based models to flash-simulate the LHCb PID detectors}",
+  version   = "v0.2.0",
+  url       = "https://github.com/mbarbetti/pidgan",
+  doi       = "10.5281/zenodo.10463728",
+  publisher = "Zenodo",
+  year      = "2024",
+}
+```
+
+In the above bibtex entry, names are in alphabetical order, the version number is intended to be that from [pidgan/version.py](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/version.py), and the year corresponds to the project's open-source release.
+
+### License
+PIDGAN has a GNU General Public License v3 (GPLv3), as found in the [LICENSE](https://github.com/mbarbetti/pidgan/blob/main/LICENSE) file.