# Machine Learning Methods for Track Classification in the AT-TPC

arXiv: https://arxiv.org/abs/1810.10350 · HTML: https://mlsanity.com/paper/1810.10350/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders](https://mlsanity.com/paper/2503.00131/) (2025)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation](https://mlsanity.com/paper/2607.27501/) (2026)
- [Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers](https://mlsanity.com/paper/2202.12941/) (2022)
- [AI Meets Antimatter: Unveiling Antihydrogen Annihilations](https://mlsanity.com/paper/2412.00961/) (2024)
- [Antimatter Annihilation Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber](https://mlsanity.com/paper/2502.12169/) (2025)
- [Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC](https://mlsanity.com/paper/2605.28296/) (2026)

## Abstract (arXiv)

We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University. An automated method to single out the desired reaction product would result in more accurate physics results as well as a faster analysis process. Binary and multi-class classification methods were tested on data produced by the 46Ar(p,p) experiment run at the NSCL in September 2015. We found a Convolutional Neural Network to be the most successful classifier of proton scattering events for transfer learning. Results from this investigation and recommendations for event classification in future experiments are presented.