# Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)

arXiv: https://arxiv.org/abs/2401.05295 · HTML: https://mlsanity.com/paper/2401.05295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks](https://mlsanity.com/paper/1712.10321/) (2017)
- [Phase-aware Speech Enhancement with Deep Complex U-Net](https://mlsanity.com/paper/1903.03107/) (2019)
- [Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters](https://mlsanity.com/paper/1705.02355/) (2017)
- [Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics](https://mlsanity.com/paper/1912.06794/) (2019)
- [T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling](https://mlsanity.com/paper/1811.08295/) (2018)
- [How to GAN away Detector Effects](https://mlsanity.com/paper/1912.00477/) (2019)
- [GANplifying Event Samples](https://mlsanity.com/paper/2008.06545/) (2020)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [Cherenkov Detectors Fast Simulation Using Neural Networks](https://mlsanity.com/paper/1903.11788/) (2019)
- [Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks](https://mlsanity.com/paper/1905.11825/) (2019)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [Data Amplification: Instance-Optimal Property Estimation](https://mlsanity.com/paper/1903.01432/) (2019)
- [Data Amplification: A Unified and Competitive Approach to Property Estimation](https://mlsanity.com/paper/1904.00070/) (2019)

## Abstract (arXiv)

To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). We describe the proposed network architecture, its training methodology and the approach to train the GAN with real pulses from a scintillator receiving radiation from sources of 137Cs and 22Na. The Generator was installed in a Xilinx's System-On-Chip (SoC). We show how the network is capable of generating pulses with the same shape as the real ones that even match the data distributions in the original pulse-height histogram data.