Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913910976675840 |
|---|---|
| author | Mokhtar, Farouk Pata, Joosep Garcia, Dolores Wulff, Eric Zhang, Mengke Kagan, Michael Duarte, Javier |
| author_facet | Mokhtar, Farouk Pata, Joosep Garcia, Dolores Wulff, Eric Zhang, Mengke Kagan, Michael Duarte, Javier |
| contents | We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders Mokhtar, Farouk Pata, Joosep Garcia, Dolores Wulff, Eric Zhang, Mengke Kagan, Michael Duarte, Javier High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology Data Analysis, Statistics and Probability Instrumentation and Detectors We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning. |
| title | Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders |
| topic | High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2503.00131 |