# Neuromorphic Readout for Hadron Calorimeters

arXiv: https://arxiv.org/abs/2502.12693 · HTML: https://mlsanity.com/paper/2502.12693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Roadmap to Neuromorphic Computing with Emerging Technologies](https://mlsanity.com/paper/2407.02353/) (2024)
- [Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions](https://mlsanity.com/paper/2210.11190/) (2022)
- [A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons](https://mlsanity.com/paper/2301.09962/) (2023)

## Built on by (most cited first, 1 of 1)

- [Spiking Neural Network inference on FPGAs with hls4ml](https://mlsanity.com/paper/2606.10008/) (2026)

## Abstract (arXiv)

We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.