# Particle identification with machine learning from incomplete data in the ALICE experiment

arXiv: https://arxiv.org/abs/2403.17436 · HTML: https://mlsanity.com/paper/2403.17436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Machine-learning-based particle identification with missing data](https://mlsanity.com/paper/2401.01905/) (2023)
- [Attention-based Multi-instance Neural Network for Medical Diagnosis from Incomplete and Low Quality Data](https://mlsanity.com/paper/1904.04460/) (2019)
- [Using Machine Learning for Particle Identification in ALICE](https://mlsanity.com/paper/2204.06900/) (2022)

## Built on by (most cited first, 3 of 3)

- [OmniJet-αC: Learning point cloud calorimeter simulations using generative transformers](https://mlsanity.com/paper/2501.05534/) (2025)
- [Novel machine learning applications at the LHC](https://mlsanity.com/paper/2409.20413/) (2024)
- [Particle Multi-Axis Transformer for Jet Tagging](https://mlsanity.com/paper/2406.06638/) (2024)

## Abstract (arXiv)

The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data.