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Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.
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This content will become publicly available on December 18, 2025
Novel position reconstruction methods for highly granular electromagnetic calorimeters
We present work on design and reconstruction methods for sampling electromagnetic calorimeters with emphasis on highly granular designs. We use the clustered logarithmically weighted center-of-gravity estimator (lwk-means) for initial benchmarking of position resolution. We find that the θ and φ resolution for high energy photons in Si-W designs improves when increasing both sampling frequency and sampling thickness. Augmenting only one is found to have mixed results. We find that lwk-means is unable to effectively use calorimeter transverse cell sizes smaller than 2 mm. New reconstruction methods for highly granular designs are developed. We find that methods that only measure the initial particle shower and disregard the remaining shower can take advantage of cell sizes down to at least 10 µm, significantly outperforming the benchmark method. Of these, the best method and design is the initial particle shower “single hit” method using the calorimeter design with the highest sampling frequency and sampling fraction.
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- Award ID(s):
- 2310030
- PAR ID:
- 10608088
- Publisher / Repository:
- EPJ Web Conf.
- Date Published:
- Journal Name:
- EPJ Web of Conferences
- Volume:
- 315
- ISSN:
- 2100-014X
- Page Range / eLocation ID:
- 03007
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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