# The Tracking Machine Learning challenge : Throughput phase

arXiv: https://arxiv.org/abs/2105.01160 · HTML: https://mlsanity.com/paper/2105.01160/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 7 of 7)

- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking](https://mlsanity.com/paper/2103.06995/) (2021)
- [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://mlsanity.com/paper/2012.01563/) (2020)
- [Charged particle tracking via edge-classifying interaction networks](https://mlsanity.com/paper/2103.16701/) (2021)
- [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://mlsanity.com/paper/2007.00149/) (2020)
- [Charged particle tracking with quantum annealing-inspired optimization](https://mlsanity.com/paper/1908.04475/) (2019)
- [Beyond 4D Tracking: Using Cluster Shapes for Track Seeding](https://mlsanity.com/paper/2012.04533/) (2020)

## Built on by (most cited first, 12 of 12)

- [Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction](https://mlsanity.com/paper/2109.12636/) (2021)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning](https://mlsanity.com/paper/2207.07958/) (2022)
- [Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics](https://mlsanity.com/paper/2402.12535/) (2024)
- [A Language Model for Particle Tracking](https://mlsanity.com/paper/2402.10239/) (2024)
- [High Pileup Particle Tracking with Object Condensation](https://mlsanity.com/paper/2312.03823/) (2023)
- [Combining the Strengths of Dutch Survey and Register Data in a Data Challenge to Predict Fertility (PreFer)](https://mlsanity.com/paper/2402.00705/) (2024)
- [ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset](https://mlsanity.com/paper/2512.15230/) (2025)
- [FAIR Universe HiggsML Uncertainty Dataset and Competition](https://mlsanity.com/paper/2410.02867/) (2024)
- [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://mlsanity.com/paper/2407.13925/) (2024)
- [Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction](https://mlsanity.com/paper/2510.07594/) (2025)
- [Towards impactful challenges: post-challenge paper, benchmarks and other dissemination actions](https://mlsanity.com/paper/2312.06036/) (2023)

## Abstract (arXiv)

This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. created at the Large Hadron Collider (LHC): given O(105) points, the participants had to connect them into O(104) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived.