The Pandora Software Development Kit for Pattern Recognition
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Abstract (arXiv)
The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora Software Development Kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e+e- linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2015.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 4; 2026: 1.
6 papers.
- 2025Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliderscited by 8
- 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
- 2023Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorscited by 4
- 2025Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chamberscited by 2
- 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
- 2026End-to-end event reconstruction for precision physics at future colliderscited by 2
- 2026End-to-end event reconstruction for precision physics at future colliders4 Mar 2026
- 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogates21 Nov 2025
- 2025CaloHadronic: a diffusion model for the generation of hadronic showers26 Jun 2025
- 2025Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers11 Apr 2025
- 2025Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders28 Feb 2025
- 2023Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors13 Sep 2023