# TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

arXiv: https://arxiv.org/abs/2309.14027 · HTML: https://mlsanity.com/paper/2309.14027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)

## Built on by (most cited first, 4 of 4)

- [End-to-End Optimal Detector Design with Mutual Information Surrogates](https://mlsanity.com/paper/2503.14342/) (2025)
- [Large Language Models for Physics Instrument Design](https://mlsanity.com/paper/2601.07580/) (2026)
- [The Optimal use of Segmentation for Sampling Calorimeters](https://mlsanity.com/paper/2310.04442/) (2023)
- [Neural surrogates for designing gravitational wave detectors](https://mlsanity.com/paper/2511.19364/) (2025)

## Abstract (arXiv)

We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github.